Multilayer perceptron neural network for downscaling rainfall in arid ...
Indian Academy of Sciences (India)
Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan ... A multilayer perceptron (MLP) neural network has been proposed in the present study for the downscaling of rainfall in the data scarce arid region of Baluchistan province of Pakistan, which is considered as ...
Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network
DEFF Research Database (Denmark)
Míguez González, M; López Peña, F.; Díaz Casás, V.
2011-01-01
acknowledged in the last few years. This work proposes a prediction system based on a multilayer perceptron (MP) neural network. The training and testing of the MP network is accomplished by feeding it with simulated data of a three degrees-of-freedom nonlinear model of a fishing vessel. The neural network...
Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks.
Abderrahim, Hamza; Chellali, Mohammed Reda; Hamou, Ahmed
2016-01-01
Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic for carving such complex problem. In this paper, we used a multilayer perceptron neural network to forecast the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 μm (PM10) in Algiers, Algeria. The data for training and testing the network are based on the data sampled from 2002 to 2006 collected by SAMASAFIA network center at El Hamma station. The meteorological data, air temperature, relative humidity, and wind speed, are used as inputs network parameters in the formation of model. The training patterns used correspond to 41 days data. The performance of the developed models was evaluated on the basis index of agreement and other statistical parameters. It was seen that the overall performance of model with 15 neurons is better than the ones with 5 and 10 neurons. The results of multilayer network with as few as one hidden layer and 15 neurons were quite reasonable than the ones with 5 and 10 neurons. Finally, an error around 9% has been reached.
ASSESSMENT OF LIBRARY USERS’ FEEDBACK USING MODIFIED MULTILAYER PERCEPTRON NEURAL NETWORKS
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K G Nandha Kumar
2017-07-01
Full Text Available An attempt has been made to evaluate the feedbacks of library users of four different libraries by using neural network based data mining techniques. This paper presents the results of a survey of users’ satisfactory level on four different libraries. The survey has been conducted among the users of four libraries of educational institutions of Kovai Medical Center Research and Educational Trust. Data were collected through questionnaires. Artificial neural network based data mining techniques are proposed and applied to assess the libraries in terms of level of satisfaction of users. In order to assess the users’ satisfaction level, two neural network techniques: Modified Multilayer Perceptron Network-Supervised and Modified Multilayer Perceptron Network-Unsupervised are proposed. The proposed techniques are compared with the conventional classification algorithm Multilayer Perceptron Neural Network and found better in overall performance. It is found that the quality of service provided by the libraries is highly good and users are highly satisfied with various aspects of library service. The Arts and Science College Library secured the maximum percent in terms of user satisfaction. This shows that the users’ satisfaction of ASCL is better than the other libraries. This study provides an insight into the actual quality and satisfactory level of users of libraries after proper assessment. It is strongly expected that the results will help library authorities to enhance services and quality in the near future.
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Flávio Clésio Silva de Souza
2014-06-01
Full Text Available The purpose of the present research is to apply a Multilayer Perceptron (MLP neural network technique to create classification models from a portfolio of Non-Performing Loans (NPLs to classify this type of credit derivative. These credit derivatives are characterized as the amount of loans that were not paid and are already overdue more than 90 days. Since these titles are, because of legislative motives, moved by losses, Credit Rights Investment Funds (FDIC performs the purchase of these debts and the recovery of the credits. Using the Multilayer Perceptron (MLP architecture of Artificial Neural Network (ANN, classification models regarding the posterior recovery of these debts were created. To evaluate the performance of the models, evaluation metrics of classification relating to the neural networks with different architectures were presented. The results of the classifications were satisfactory, given the classification models were successful in the presented economics costs structure.
Ceballos-Magaña, Silvia G; de Pablos, Fernando; Jurado, José Marcos; Martín, María Jesús; Alcázar, Ángela; Muñiz-Valencia, Roberto; Gonzalo-Lumbreras, Raquel; Izquierdo-Hornillos, Roberto
2013-02-15
Differentiation of silver, gold, aged and extra-aged tequila using 1-propanol, ethyl acetate, 2-methyl-1-propanol, 3-methyl-1-butanol and 2-methyl-1-butanol and furan derivatives like 5-(hydroxymethyl)-2-furaldehyde and 2-furaldehyde has been carried out. The content of 1-propanol, ethyl acetate, 2-methyl-1-propanol, 3-methyl-1-butanol and 2-methyl-1-butanol was determined by means of head space solid phase microextraction gas chromatography mass-spectrometry. 5-(Hydroxymethyl)-2-furaldehyde and 2-furaldehyde were determined by high performance liquid chromatography with diode array detection. Kruskal-Wallis test was used to highlight significant differences between types of tequila. Principal component analysis was applied as visualisation technique. Linear discriminant analysis and multilayer perceptron artificial neural networks were used to construct classification models. The best classification performance was obtained when multilayer perceptron model was applied. Copyright © 2012 Elsevier Ltd. All rights reserved.
Classification of Parking Spots Using Multilayer Perceptron Networks
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FALCAO, H. S.
2013-12-01
Full Text Available This project intends to develop a prototype for the identification of free spots in open air parking area where there is a good aerial view without obstacles, allowing for the identification of occupied and free spots. We used image processing techniques and pattern recognition using Artificial Neural Networks (ANN. In order to help implement the prototype, we used Matlab. In order to simulate the parking area, we created a model so that we could acquire the images using a webcam, process them, train the neural network, classify the spots and finally, show the results. The results show that it is viable to apply pattern recognition through image capture to classify parking spots
Generation of hourly irradiation synthetic series using the neural network multilayer perceptron
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Hontoria, L.; Aguilera, J. [Universidad de Jaen, Linares-Jaen (Spain). Dpto. de Electronica; Zufiria, P. [Ciudad Universitaria, Madrid (Spain). Grupo de Redes Neuronales
2002-05-01
In this work, a methodology based on the neural network model called multilayer perceptron (MLP) to solve a typical problem in solar energy is presented. This methodology consists of the generation of synthetic series of hourly solar irradiation. The model presented is based on the capacity of the MLP for finding relations between variables for which interrelation is unknown explicitly. The information available can be included progressively at the series generator at different stages. A comparative study with other solar irradiation synthetic generation methods has been done in order to demonstrate the validity of the one proposed. (author)
Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network
Sun, W. Z.; Jiang, M. Y.; Ren, L.; Dang, J.; You, T.; Yin, F.-F.
2017-09-01
To improve the prediction accuracy of respiratory signals using adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for gated treatment of moving target in radiation therapy. The respiratory signals acquired using a real-time position management (RPM) device from 138 previous 4DCT scans were retrospectively used in this study. The ADMLP-NN was composed of several artificial neural networks (ANNs) which were used as weaker predictors to compose a stronger predictor. The respiratory signal was initially smoothed using a Savitzky-Golay finite impulse response smoothing filter (S-G filter). Then, several similar multi-layer perceptron neural networks (MLP-NNs) were configured to estimate future respiratory signal position from its previous positions. Finally, an adaptive boosting (Adaboost) decision algorithm was used to set weights for each MLP-NN based on the sample prediction error of each MLP-NN. Two prediction methods, MLP-NN and ADMLP-NN (MLP-NN plus adaptive boosting), were evaluated by calculating correlation coefficient and root-mean-square-error between true and predicted signals. For predicting 500 ms ahead of prediction, average correlation coefficients were improved from 0.83 (MLP-NN method) to 0.89 (ADMLP-NN method). The average of root-mean-square-error (relative unit) for 500 ms ahead of prediction using ADMLP-NN were reduced by 27.9%, compared to those using MLP-NN. The preliminary results demonstrate that the ADMLP-NN respiratory prediction method is more accurate than the MLP-NN method and can improve the respiration prediction accuracy.
Kayri, Murat
2015-01-01
The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…
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Mohammad Fathian
2012-04-01
Full Text Available In this paper, the problem of predicting the exchange rate time series in the foreign exchange rate market is going to be solved using a time-delayed multilayer perceptron neural network with gold price as external factor. The input for the learning phase of the artificial neural network are the exchange rate data of the last five days plus the gold price in two different currencies of the exchange rate as the external factor for helping the artificial neural network improving its forecast accuracy. The five-day delay has been chosen because of the weekly cyclic behavior of the exchange rate time series with the consideration of two holidays in a week. The result of forecasts are then compared with using the multilayer peceptron neural network without gold price external factor by two most important evaluation techniques in the literature of exchange rate prediction. For the experimental analysis phase, the data of three important exchange rates of EUR/USD, GBP/USD, and USD/JPY are used.
An artificial multilayer perceptron neural network for diagnosis of proximal dental caries.
Devito, Karina Lopes; de Souza Barbosa, Flávio; Felippe Filho, Waldir Neme
2008-12-01
To evaluate if the application of an artificial intelligence model, a multilayer perceptron neural network, improves the radiographic diagnosis of proximal caries. One hundred sixty radiographic images of proximal surfaces of extracted human teeth were assessed regarding the presence of caries by 25 examiners. Examination of the radiographs was used to feed the neural network, and the corresponding teeth were sectioned and assessed under optical microscope (gold standard). This gold standard served to teach the neural network to diagnose caries on the basis of the radiographic exams. To gauge the network's capacity for generalization, i.e., its performance with new cases, data were divided into 3 subgroups for training, test, and cross-validation. The area under the receiver operating characteristic (ROC) curve allowed comparison of efficacy between network and examiner diagnosis. For the best of the 25 examiners, the ROC curve area was 0.717, whereas network diagnosis achieved an ROC curve area of 0.884, indicating a sizeable improvement in proximal caries diagnosis. Considering all examiners, the diagnostic improvement using the neural network was 39.4%.
Quaternionic Multilayer Perceptron with Local Analyticity
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Nobuyuki Matsui
2012-11-01
Full Text Available A multi-layered perceptron type neural network is presented and analyzed in this paper. All neuronal parameters such as input, output, action potential and connection weight are encoded by quaternions, which are a class of hypercomplex number system. Local analytic condition is imposed on the activation function in updating neurons’ states in order to construct learning algorithm for this network. An error back-propagation algorithm is introduced for modifying the connection weights of the network.
Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron
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Mohammad Subhi Al-batah
2015-01-01
Full Text Available Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP and Cascade Forward Neural Network (CFNN, are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.
Evaluation of multilayer perceptron algorithms for an analysis of network flow data
Bieniasz, Jedrzej; Rawski, Mariusz; Skowron, Krzysztof; Trzepiński, Mateusz
2016-09-01
The volume of exchanged information through IP networks is larger than ever and still growing. It creates a space for both benign and malicious activities. The second one raises awareness on security network devices, as well as network infrastructure and a system as a whole. One of the basic tools to prevent cyber attacks is Network Instrusion Detection System (NIDS). NIDS could be realized as a signature-based detector or an anomaly-based one. In the last few years the emphasis has been placed on the latter type, because of the possibility of applying smart and intelligent solutions. An ideal NIDS of next generation should be composed of self-learning algorithms that could react on known and unknown malicious network activities respectively. In this paper we evaluated a machine learning approach for detection of anomalies in IP network data represented as NetFlow records. We considered Multilayer Perceptron (MLP) as the classifier and we used two types of learning algorithms - Backpropagation (BP) and Particle Swarm Optimization (PSO). This paper includes a comprehensive survey on determining the most optimal MLP learning algorithm for the classification problem in application to network flow data. The performance, training time and convergence of BP and PSO methods were compared. The results show that PSO algorithm implemented by the authors outperformed other solutions if accuracy of classifications is considered. The major disadvantage of PSO is training time, which could be not acceptable for larger data sets or in real network applications. At the end we compared some key findings with the results from the other papers to show that in all cases results from this study outperformed them.
An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network
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Amiri S
2013-12-01
Full Text Available Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM, white matter (WM and cerebrospinal ﬂuid (CSF, but only if the obtained segmentation results are correct. Due to image artifacts such as noise, low contrast and intensity non-uniformity, there are some classifcation errors in the results of image segmentation. Objective: An automated algorithm based on multi-layer perceptron neural networks (MLPNN is presented for segmenting MR images. The system is to identify two tissues of WM and GM in human brain 2D structural MR images. A given 2D image is processed to enhance image intensity and to remove extra cerebral tissue. Thereafter, each pixel of the image under study is represented using 13 features (8 statistical and 5 non- statistical features and is classifed using a MLPNN into one of the three classes WM and GM or unknown. Results: The developed MR image segmentation algorithm was evaluated using 20 real images. Training using only one image, the system showed robust performance when tested using the remaining 19 images. The average Jaccard similarity index and Dice similarity metric for the GM and WM tissues were estimated to be 75.7 %, 86.0% for GM, and 67.8% and 80.7%for WM, respectively. Conclusion: The obtained performances are encouraging and show that the presented method may assist with segmentation of 2D MR images especially where categorizing WM and GM is of interest.
Multilayer Perceptron: Architecture Optimization and Training
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Hassan Ramchoun
2016-09-01
Full Text Available The multilayer perceptron has a large wide of classification and regression applications in many fields: pattern recognition, voice and classification problems. But the architecture choice has a great impact on the convergence of these networks. In the present paper we introduce a new approach to optimize the network architecture, for solving the obtained model we use the genetic algorithm and we train the network with a back-propagation algorithm. The numerical results assess the effectiveness of the theoretical results shown in this paper, and the advantages of the new modeling compared to the previous model in the literature.
DEFF Research Database (Denmark)
Kucuk, Nil; Manohara, S.R.; Hanagodimath, S.M.
2013-01-01
In this work, multilayered perceptron neural networks (MLPNNs) were presented for the computation of the gamma-ray energy absorption buildup factors (BA) of seven thermoluminescent dosimetric (TLD) materials [LiF, BeO, Na2B4O7, CaSO4, Li2B4O7, KMgF3, Ca3(PO4)2] in the energy region 0.015–15Me...... data for TLD materials have been given with penetration depth and incident photon energy as comparative to the results of the interpolation method using the Geometrical Progression (G-P) fitting formula....
Işik, Hakan; Sezer, Esma
2012-02-01
In this study, it has been intended to perform an automatic classification of Electroencephalography (EEG) signals via Artificial Neural Networks (ANN) and to investigate these signals using Wavelet Transform (WT) for diagnosing epilepsy syndrome. EEG signals have been decomposed into frequency sub-bands using WT and a set of feature vectors which were extracted from the sub-bands. Dimensions of these feature vectors have been reduced via Principal Component Analysis (PCA) method and then classified as epileptic or healthy using Multilayer Perceptron (MLP) and ELMAN ANN. Performance evaluation of the used ANN models have been carried out by performing Receiver Operation Characteristic (ROC) analysis.
Multilayer Perceptrons to Approximate Quaternion Valued Functions.
Xibilia, M G.; Muscato, G; Fortuna, L; Arena, P
1997-03-01
In this paper a new type of multilayer feedforward neural network is introduced. Such a structure, called hypercomplex multilayer perceptron (HMLP), is developed in quaternion algebra and allows quaternionic input and output signals to be dealt with, requiring a lower number of neurons than the real MLP, thus providing a reduced computational complexity. The structure introduced represents a generalization of the multilayer perceptron in the complex space (CMLP) reported in the literature. The fundamental result reported in the paper is a new density theorem which makes HMLPs universal interpolators of quaternion valued continuous functions. Moreover the proof of the density theorem can be restricted in order to formulate a density theorem in the complex space. Due to the identity between the quaternion and the four-dimensional real space, such a structure is also useful to approximate multidimensional real valued functions with a lower number of real parameters, decreasing the probability of being trapped in local minima during the learning phase. A numerical example is also reported in order to show the efficiency of the proposed structure. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.
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Sun, W; Jiang, M; Yin, F [Duke University Medical Center, Durham, NC (United States)
2016-06-15
Purpose: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). Methods: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study. The ASMLP uses two multi-layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, a Savitzky-Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg-Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished. Results: In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average). Conclusion: The preliminary results
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Alireza Taravat
2014-12-01
Full Text Available Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR, as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM and MultiLayer Perceptron (MLP neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN model generates poor accuracies.
KLASIFIKASI WEBSITE MENGGUNAKAN ALGORITMA MULTILAYER PERCEPTRON
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Nyoman Purnama
2014-12-01
Full Text Available Sistem klasifikasi merupakan proses temu balik informasi yang sangat bergantung dari elemen-elemen penyusunnya.Sistem ini banyak digunakan untuk mengatasi permasalahan segmentasi data. Klasifikasi dapat digunakan pada website sebagaimetode untuk mengelompokkan website. Website merupakan salah satu data yang memiliki informasi yang beraneka-ragam,sehingga pengelompokan data ini penting untuk diteliti. Sistem klasifikasi dimulai dengan melakukan proses pengumpulaninformasi dari halaman website (parsing dan untuk setiap hasil parsing dilakukan proses penghapusan kata henti, stemming,feature selection dengan tf-idf. Hasil dari proses ini berupa fitur yang menjadi inputan algoritma Multilayer Perceptron. Dalamalgoritma ini terjadi proses pembelajaran terhadap pola input masukan dan pembuatan bobot pelatihan. Bobot ini akandigunakan pada proses klasifikasi. Hasil dari penelitian menunjukkan bahwa algoritma Multilayer Perceptron dapatmenghasilkan klasifikasi website dengan akurasi yang bagus. Hal ini dibuktikan dengan beberapa tahapan penelitian yangberbeda dan didapatkan nilai akurasi rata-rata diatas 70%.
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Mawloud GUERMOUI
2016-07-01
Full Text Available Accurate estimation of Daily Global Solar Radiation (DGSR has been a major goal for solar energy application. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly of the search for relationships between weather variables, such as temperature, humidity, sunshine duration, etc. In this respect, the present study focuses on the development of artificial neural network (ANN model for estimation of daily global solar radiation on horizontal surface in Ghardaia city (South Algeria. In this analysis back-propagation algorithm is applied. Daily mean air temperature, relative humidity and sunshine duration was used as climatic inputs parameters, while the daily global solar radiation (DGSR was the only output of the ANN. We have evaluated Multi-Layer Perceptron (MLP models to estimate DGSR using three year of measurement (2005-2008. It was found that MLP-model based on sunshine duration and mean air temperature give accurate results in term of Mean Absolute Bias Error, Root Mean Square Error, Relative Square Error and Correlation Coefficient. The obtained values of these indicators are 0.67 MJ/m², 1.28 MJ/m², 6.12%and 98.18%, respectively which shows that MLP is highly qualified for DGSR estimation in semi-arid climates.
Heddam, Salim
2016-09-01
This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In the proposed model, four water quality variables that are water temperature, dissolved oxygen, pH, and specific conductance were selected as the inputs for the MLPNN model, and the PC as the output. To demonstrate the capability and the usefulness of the MLPNN model, a total of 15,849 data measured at 15-min (15 min) intervals of time are used for the development of the model. The data are collected at the lower Charles River buoy, and available from the US Environmental Protection Agency (USEPA). For comparison purposes, a multiple linear regression (MLR) model that was frequently used for predicting water quality variables in previous studies is also built. The performances of the models are evaluated using a set of widely used statistical indices. The performance of the MLPNN and MLR models is compared with the measured data. The obtained results show that (i) the all proposed MLPNN models are more accurate than the MLR models and (ii) the results obtained are very promising and encouraging for the development of phycocyanin-predictive models.
Extreme Learning Machine for Multilayer Perceptron.
Tang, Jiexiong; Deng, Chenwei; Huang, Guang-Bin
2016-04-01
Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via l1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.
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G. S. Fesghandis
2017-02-01
Full Text Available Given that the new product failure in practice entails huge costs for organizations, the need for competitive planning has led organizations to apply appropriate approaches; one of these approaches is to predict new product success before market entry. Accordingly, this study predicts NPD success by comparing two techniques, the Multilayer Perceptron (MLP and the Radial Basis Function (RBF in the clothing industry of Tabriz. In order to collect data, a questionnaire with good validity and reliability was distributed among the population. MLP and RBF were used to analyze data. Based on MSE, RMSE and R2, data analysis showed that MLP had lower error than RBF in predicting NPD success.
Johns, Jesse M.; Burkes, Douglas
2017-07-01
In this work, a multilayered perceptron (MLP) network is used to develop predictive isothermal time-temperature-transformation (TTT) models covering a range of U-Mo binary and ternary alloys. The selected ternary alloys for model development are U-Mo-Ru, U-Mo-Nb, U-Mo-Zr, U-Mo-Cr, and U-Mo-Re. These model's ability to predict 'novel' U-Mo alloys is shown quite well despite the discrepancies between literature sources for similar alloys which likely arise from different thermal-mechanical processing conditions. These models are developed with the primary purpose of informing experimental decisions. Additional experimental insight is necessary in order to reduce the number of experiments required to isolate ideal alloys. These models allow test planners to evaluate areas of experimental interest; once initial tests are conducted, the model can be updated and further improve follow-on testing decisions. The model also improves analysis capabilities by reducing the number of data points necessary from any particular test. For example, if one or two isotherms are measured during a test, the model can construct the rest of the TTT curve over a wide range of temperature and time. This modeling capability reduces the cost of experiments while also improving the value of the results from the tests. The reduced costs could result in improved material characterization and therefore improved fundamental understanding of TTT dynamics. As additional understanding of phenomena driving TTTs is acquired, this type of MLP model can be used to populate unknowns (such as material impurity and other thermal mechanical properties) from past literature sources.
Energy Technology Data Exchange (ETDEWEB)
Johns, Jesse M.; Burkes, Douglas
2017-07-01
In this work, a multilayered perceptron (MLP) network is used to develop predictive isothermal time-temperature-transformation (TTT) models covering a range of U-Mo binary and ternary alloys. The selected ternary alloys for model development are U-Mo-Ru, U-Mo-Nb, U-Mo-Zr, U-Mo-Cr, and U-Mo-Re. These model’s ability to predict 'novel' U-Mo alloys is shown quite well despite the discrepancies between literature sources for similar alloys which likely arise from different thermal-mechanical processing conditions. These models are developed with the primary purpose of informing experimental decisions. Additional experimental insight is necessary in order to reduce the number of experiments required to isolate ideal alloys. These models allow test planners to evaluate areas of experimental interest; once initial tests are conducted, the model can be updated and further improve follow-on testing decisions. The model also improves analysis capabilities by reducing the number of data points necessary from any particular test. For example, if one or two isotherms are measured during a test, the model can construct the rest of the TTT curve over a wide range of temperature and time. This modeling capability reduces the cost of experiments while also improving the value of the results from the tests. The reduced costs could result in improved material characterization and therefore improved fundamental understanding of TTT dynamics. As additional understanding of phenomena driving TTTs is acquired, this type of MLP model can be used to populate unknowns (such as material impurity and other thermal mechanical properties) from past literature sources.
LeMoyne, Robert; Mastroianni, Timothy
2016-08-01
Natural gait consists of synchronous and rhythmic patterns for both the lower and upper limb. People with hemiplegia can experience reduced arm swing, which can negatively impact the quality of gait. Wearable and wireless sensors, such as through a smartphone, have demonstrated the ability to quantify various features of gait. With a software application the smartphone (iPhone) can function as a wireless gyroscope platform capable of conveying a gyroscope signal recording as an email attachment by wireless connectivity to the Internet. The gyroscope signal recordings of the affected hemiplegic arm with reduced arm swing arm and the unaffected arm are post-processed into a feature set for machine learning. Using a multilayer perceptron neural network a considerable degree of classification accuracy is attained to distinguish between the affected hemiplegic arm with reduced arm swing arm and the unaffected arm.
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Azadeh Gholami
2016-01-01
Full Text Available A modified multi-layer perceptron (MLP model based on decision trees (DT-MLP is presented to predict velocity and water free-surface profiles in a 90° open-channel bend. The ability of the new hybrid model to predict the velocity and flow depth in a 90° sharp bend is investigated and compared with the abilities of MLP and multiple-linear regression (MLR models. The MLP and DT-MLP networks are trained and tested using 520 and 506 experimental data measured for velocity and flow depth, respectively, at five different discharge rates of 5, 7.8, 13.6, 19.1 and 25.3 l/s. The MLP and DT-MLP comparison results against MLR reveal that the two artificial neural networks (ANNs are 84% and 16% more accurate than the MLR model in predicting the velocity and flow depth variables, respectively. According to the results, the root mean square error (RMSE value of the DT-MLP model decreases by 9% and 7.5% in predicting velocity and flow depth, respectively, compared with the MLP model. It was found that the hybrid decision-tree-based method can significantly improve MLP neural network performance in forecasting velocity and free-surface profiles in a 90° open-channel bend.
Ivić, Branka; Ibrić, Svetlana; Cvetković, Nebojsa; Petrović, Aleksandra; Trajković, Svetlana; Djurić, Zorica
2010-07-01
The purpose of the study was to screen the effects of formulation factors on the in vitro release profile of diclofenac sodium from matrix tablets using design of experiment (DOE). Formulations of diclofenac sodium tablets, with Carbopol 71G as matrix substance, were optimized by artificial neural network. According to Central Composite Design, 10 formulations of diclofenac sodium matrix tablets were prepared. As network inputs, concentration of Carbopol 71G and the Kollidon K-25 were selected. In vitro dissolution time profiles at 5 different sampling times were chosen as responses. The independent variables and the release parameters were processed by multilayer perceptrons neural network (MLP). Results of drug release studies indicate that drug release rates vary between different formulations, with a range of 1 h to more than 8 h to complete dissolution. For two tested formulations there was no difference between experimental and MLP predicted in vitro profiles. The MLP model was optimized. The root mean square value for the trained network was 0.07%, which indicated that the optimal MLP model was reached. The optimal tablet formulation predicted by MLP was with 23% of Carbopol 71G and 0.8% of Kollidon K-25. Calculated difference factor (f(1) 7.37) and similarity factor (f(2) 70.79) indicate that there is no difference between predicted and experimentally observed drug release profiles for the optimal formulation. The satisfactory prediction of drug release for optimal formulation by the MLP in this study has shown the applicability of this optimization method in modeling extended release tablet formulation.
Pourahmad, Saeedeh; Azad, Mohsen; Paydar, Shahram
2015-03-30
To diagnose the malignancy in thyroid tumor, neural network approach is applied and the performances of thirteen batch learning algorithms are investigated on accuracy of the prediction. Therefore, a back propagation feed forward neural networks (BP FNNs) is designed and three different numbers of neuron in hidden layer are compared (5, 10 and 20 neurons). The pathology result after the surgery and clinical findings before surgery of the patients are used as the target outputs and the inputs, respectively. The best algorithm(s) is/are chosen based on mean or maximum accuracy values in the prediction and also area under Receiver Operating Characteristic Curve (ROC curve). The results show superiority of the network with 5 neurons in the hidden layer. In addition, the better performances are occurred for Polak-Ribiere conjugate gradient, BFGS quasi-newton and one step secant algorithms according to their accuracy percentage in prediction (83%) and for Scaled Conjugate Gradient and BFGS quasi-Newton based on their area under the ROC curve (0.905).
Directory of Open Access Journals (Sweden)
Khuat Thanh Tung
2016-11-01
Full Text Available Optical Character Recognition plays an important role in data storage and data mining when the number of documents stored as images is increasing. It is expected to find the ways to convert images of typewritten or printed text into machine-encoded text effectively in order to support for the process of information handling effectively. In this paper, therefore, the techniques which are being used to convert image into editable text in the computer such as principal component analysis, multilayer perceptron network, self-organizing maps, and improved multilayer neural network using principal component analysis are experimented. The obtained results indicated the effectiveness and feasibility of the proposed methods.
Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics
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F. Cadini
2008-01-01
Full Text Available Artificial neural networks are powerful algorithms for constructing nonlinear empirical models from operational data. Their use is becoming increasingly popular in the complex modeling tasks required by diagnostic, safety, and control applications in complex technologies such as those employed in the nuclear industry. In this paper, the nonlinear modeling capabilities of an infinite impulse response multilayer perceptron (IIR-MLP for nuclear dynamics are considered in comparison to static modeling by a finite impulse response multilayer perceptron (FIR-MLP and a conventional static MLP. The comparison is made with respect to the nonlinear dynamics of a nuclear reactor as investigated by IIR-MLP in a previous paper. The superior performance of the locally recurrent scheme is demonstrated.
Madyastha, Raghavendra K.; Aazhang, Behnaam; Henson, Troy F.; Huxhold, Wendy L.
1992-01-01
This paper addresses the issue of applying a globally convergent optimization algorithm to the training of multilayer perceptrons, a class of Artificial Neural Networks. The multilayer perceptrons are trained towards the solution of two highly nonlinear problems: (1) signal detection in a multi-user communication network, and (2) solving the inverse kinematics for a robotic manipulator. The research is motivated by the fact that a multilayer perceptron is theoretically capable of approximating any nonlinear function to within a specified accuracy. The algorithm that has been employed in this study combines the merits of two well known optimization algorithms, the Conjugate Gradients and the Trust Regions Algorithms. The performance is compared to a widely used algorithm, the Backpropagation Algorithm, that is basically a gradient-based algorithm, and hence, slow in converging. The performances of the two algorithms are compared with the convergence rate. Furthermore, in the case of the signal detection problem, performances are also benchmarked by the decision boundaries drawn as well as the probability of error obtained in either case.
Minimizing Hexapod Robot Foot Deviations Using Multilayer Perceptron
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Vytautas Valaitis
2015-12-01
Full Text Available Rough-terrain traversability is one of the most valuable characteristics of walking robots. Even despite their slower speeds and more complex control algorithms, walking robots have far wider usability than wheeled or tracked robots. However, efficient movement over irregular surfaces can only be achieved by eliminating all possible difficulties, which in many cases are caused by a high number of degrees of freedom, feet slippage, frictions and inertias between different robot parts or even badly developed inverse kinematics (IK. In this paper we address the hexapod robot-foot deviation problem. We compare the foot-positioning accuracy of unconfigured inverse kinematics and Multilayer Perceptron-based (MLP methods via theory, computer modelling and experiments on a physical robot. Using MLP-based methods, we were able to significantly decrease deviations while reaching desired positions with the hexapod's foot. Furthermore, this method is able to compensate for deviations of the robot arising from any possible reason.
Tamouridou, Afroditi A; Alexandridis, Thomas K; Pantazi, Xanthoula E; Lagopodi, Anastasia L; Kashefi, Javid; Kasampalis, Dimitris; Kontouris, Georgios; Moshou, Dimitrios
2017-10-11
Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green-red-near infrared) attached on a fixed wing unmanned aerial vehicle (UAV) was utilized for the acquisition of high-resolution images (0.1 m resolution). The Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) was used to identify the S. marianum among other vegetation, mostly Avena sterilis L. The three spectral bands of Red, Green, Near Infrared (NIR) and the texture layer resulting from local variance were used as input. The S. marianum identification rates using MLP-ARD reached an accuracy of 99.54%. Τhe study had an one year duration, meaning that the results are specific, although the accuracy shows the interesting potential of S. marianum mapping with MLP-ARD on multispectral UAV imagery.
Multilayer Optical Learning Networks
Wagner, Kelvin; Psaltis, Demetri
1987-08-01
In this paper we present a new approach to learning in a multilayer optical neural network which is based on holographically interconnected nonlinear Fabry-Perot etalons. The network can learn the interconnections that form a distributed representation of a desired pattern transformation operation. The interconnections are formed in an adaptive and self aligning fashion, as volume holographic gratings in photorefractive crystals. Parallel arrays of globally space integrated inner products diffracted by the interconnecting hologram illuminate arrays of nonlinear Fabry-Perot etalons for fast thresholding of the transformed patterns. A phase conjugated reference wave interferes with a backwards propagating error signal to form holographic interference patterns which are time integrated in the volume of the photorefractive crystal in order to slowly modify and learn the appropriate self aligning interconnections. A holographic implementation of a single layer perceptron learning procedure is presented that can be extendept ,to a multilayer learning network through an optical implementation of the backward error propagation (BEP) algorithm.
Recognition of Epileptiform Patterns in the Human Electroencephalogram Using Multi-Layer Perceptron
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V. Mokran
1995-06-01
Full Text Available Automatic detection of epileptiform patterns is highly desirable during continuous monitoring of patients with epilepsy. This paper describes an unconvential system for automatic off-line recognition of epileptic sharp transients in the human electroencephalogram (EEG, based on a standard neural network architecture - multi-layer perceptron (MLP, and implemented on a Silicon Graphics Indigo workstation. The system makes comprehensive use of wide spatial contextual information available on 12 channels of EEG and takes advantage of discrete dyadic wavelet transform (DDWT for efficient parameterisation of EEG data. The EEG database consists of 12 patients, 7 of which are used in the process of training of MLP. The resulting MLP is presented with the testing data set consisting of all data vectors from all 12 patients, and is shown to be capable to recognise a wide variety of epileptic signals.
Research of human gesture recognition algorithm based on multi-layer perceptron
Wei, Xiaojuan; Wang, Mingling; Xiao, Liyi
2017-08-01
This paper mainly introduces the research of human body state recognition technology based on multi-layer perceptron. By using the application environment of ASUS Xtion pro live infrared camera, this paper focuses on the daily behavior recognition of human body in small area. First is the depth image analysis, this paper extracts the characteristics of the postures that need to be classified and we need a large number of samples. Then the collected samples are trained and classified by the multi-layer perceptron model. Finally, the human state collected by the camera can be identified in real time.
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Iman Aghayan
2012-11-01
Full Text Available This paper compares two fuzzy clustering algorithms – fuzzy subtractive clustering and fuzzy C-means clustering – to a multi-layer perceptron neural network for their ability to predict the severity of crash injuries and to estimate the response time on the traffic crash data. Four clustering algorithms – hierarchical, K-means, subtractive clustering, and fuzzy C-means clustering – were used to obtain the optimum number of clusters based on the mean silhouette coefficient and R-value before applying the fuzzy clustering algorithms. The best-fit algorithms were selected according to two criteria: precision (root mean square, R-value, mean absolute errors, and sum of square error and response time (t. The highest R-value was obtained for the multi-layer perceptron (0.89, demonstrating that the multi-layer perceptron had a high precision in traffic crash prediction among the prediction models, and that it was stable even in the presence of outliers and overlapping data. Meanwhile, in comparison with other prediction models, fuzzy subtractive clustering provided the lowest value for response time (0.284 second, 9.28 times faster than the time of multi-layer perceptron, meaning that it could lead to developing an on-line system for processing data from detectors and/or a real-time traffic database. The model can be extended through improvements based on additional data through induction procedure.
Pham, Binh Thai; Tien Bui, Dieu; Pourghasemi, Hamid Reza; Indra, Prakash; Dholakia, M. B.
2017-04-01
The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.
An application of the multilayer perceptron: Solar radiation maps in Spain
Energy Technology Data Exchange (ETDEWEB)
Hontoria, L.; Aguilera, J. [Grupo Investigacion y Desarrollo en Energia Solar y Automatica, Dpto. de Ingenieria Electronica, de Telecomunicaciones y Automatica, Escuela Politecnica Superior de Jaen, Campus de las Lagunillas, Universidad de Jaen, 23071 Jaen (Spain); Zufiria, P. [Grupo de Redes Neuronales, Dpto. de Matematica Aplicada a las Tecnologias de la Informacion, ETSI Telecomunicaciones, UPM Ciudad Universitaria s/n, 28040 Madrid (Spain)
2005-11-01
In this work an application of a methodology to obtain solar radiation maps is presented. This methodology is based on a neural network system [Lippmann, R.P., 1987. An introduction to computing with neural nets. IEEE ASSP Magazine, 4-22] called Multi-Layer Perceptron (MLP) [Haykin, S., 1994. Neural Networks. A Comprehensive Foundation. Macmillan Publishing Company; Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366]. To obtain a solar radiation map it is necessary to know the solar radiation of many points spread wide across the zone of the map where it is going to be drawn. For most of the locations all over the world the records of these data (solar radiation in whatever scale, daily or hourly values) are non-existent. Only very few locations have the privilege of having good meteorological stations where records of solar radiation have being registered. But even in those locations with historical records of solar data, the quality of these solar series is not as good as it should be for most purposes. In addition, to draw solar radiation maps the number of points on the maps (real sites) that it is necessary to work with makes this problem difficult to solve. Nevertheless, with the application of the methodology proposed in this paper, this problem has been solved and solar radiation maps have been obtained for a small region of Spain: Jaen province, a southern province of Spain between parallels 38{sup o}25' N and 37{sup o}25' N, and meridians 4{sup o}10' W and 2{sup o}10' W, and for a larger region: Andalucia, the most southern region of Spain situated between parallels 38{sup o}40' N and 36{sup o}00' N, and meridians 7{sup o}30' W and 1{sup o}40' W. (author)
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Sahar Zolfaghari
2017-12-01
Full Text Available As a result of increasing machines capabilities in modern manufacturing, machines run continuously for hours. Therefore, early fault detection is required to reduce the maintenance expenses and obviate high cost and unscheduled downtimes. Fault diagnosis systems that provide features extraction and patterns classification of the fault are able to detect and classify the failures in machines. The majority of the related works that reported a procedure for detection of rotor bar breakage so far have applied motor current signal analysis using discrete wavelet transform. In this paper, the most appropriate features are extracted from the coefficients of a wavelet packet transform after fast Fourier transform of current signal. The aim of this study is to develop an effective and sensitive method for fault detection under low load conditions. Through combining the strength of both time-scale and frequency domain analysis techniques, a unified wavelet packet signature analysis pinpoints the fault signature in the special fault-oriented frequency bands. The wavelet analysis combined with a feed-forward neural network classifier provides an intelligent methodology for the automatic diagnosis of the fault severity during runtime of the motor. The faults severity is considered as one, two, and three broken rotor bars. The results have confirmed that the proposed method is effective for diagnosing rotor bar breakage fault in an induction motor and classification of fault severity.
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Jiří Fejfar
2012-01-01
Full Text Available We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL, an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM.After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to, we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.
Mozumder, Chitrini; Tripathi, Nitin K.
2014-10-01
In recent decades, the world has experienced unprecedented urban growth which endangers the green environment in and around urban areas. In this work, an artificial neural network (ANN) based model is developed to predict future impacts of urban and agricultural expansion on the uplands of Deepor Beel, a Ramsar wetland in the city area of Guwahati, Assam, India, by 2025 and 2035 respectively. Simulations were carried out for three different transition rates as determined from the changes during 2001-2011, namely simple extrapolation, Markov Chain (MC), and system dynamic (SD) modelling, using projected population growth, which were further investigated based on three different zoning policies. The first zoning policy employed no restriction while the second conversion restriction zoning policy restricted urban-agricultural expansion in the Guwahati Municipal Development Authority (GMDA) proposed green belt, extending to a third zoning policy providing wetland restoration in the proposed green belt. The prediction maps were found to be greatly influenced by the transition rates and the allowed transitions from one class to another within each sub-model. The model outputs were compared with GMDA land demand as proposed for 2025 whereby the land demand as produced by MC was found to best match the projected demand. Regarding the conservation of Deepor Beel, the Landscape Development Intensity (LDI) Index revealed that wetland restoration zoning policies may reduce the impact of urban growth on a local scale, but none of the zoning policies was found to minimize the impact on a broader base. The results from this study may assist the planning and reviewing of land use allocation within Guwahati city to secure ecological sustainability of the wetlands.
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Maryam Khodadadi
2016-06-01
Full Text Available Background: Data mining (DM is an approach used in extracting valuable information from environmental processes. This research depicts a DM approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (WSP in Birjand, a city in Eastern Iran. Methods: Multiple regression (MR and neural network (NN models were examined using influent characteristics (pH, Biochemical oxygen demand [BOD5], temperature, chemical oxygen demand [COD], total suspended solids [TSS], total dissolved solid [TDS], electrical conductivity [EC] and turbidity as the regression input vectors. Models were adjusted to input attributes, effluent BOD5 (BODout and COD (CODout. The models performances were estimated by 10-fold external cross-validation. An internal 5-fold cross-validation was also used for the training data set in NN model. The models were compared using regression error characteristic (REC plot and other statistical measures such as relative absolute error (RAE. Sensitivity analysis was also applied to extract useful knowledge from NN model. Results: NN models (with RAE = 78.71 ± 1.16 for BODout and 83.67 ± 1.35 for CODout and MR models (with RAE = 84.40% ± 1.07 for BODout and 88.07 ± 0.80 for CODout indicate different performances and the former was better (P < 0.05 for the prediction of both effluent BOD5 and COD parameters. For the prediction of CODout the NN model with hidden layer size (H = 4 and decay factor = 0.75 ± 0.03 presented the best predictive results. For BODout the H and decay factor were found to be 4 and 0.73 ± 0.03, respectively. TDS was found as the most descriptive influent wastewater characteristics for the prediction of the WSP performance. The REC plots confirmed the NN model performance superiority for both BOD and COD effluent prediction. Conclusion: Modeling the performance of WSP systems using NN models along with sensitivity analysis can offer better
Multilayer Perceptron prediction for the Production of Methyl Acetate via Reactive Distillation
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Salih A. Rushdi
2016-12-01
Full Text Available In this paper, it was given a profile for the design of reactive distillation to produce methyl acetate from acetic acid and methanol in the presence of sulfuric acid as catalyst. The conversion of the reaction was studied as function to time, reflux ratio and feed mole ratio and predicted using Multilayer Perceptron through SPSS software 21. The result shows that using ANN will reduce the Squares Error to 0.262 and the Relative Error to 12.1%.
Multilayer Perceptron prediction for the Production of Methyl Acetate via Reactive Distillation
Salih A. Rushdi; Taha Mahdi
2016-01-01
In this paper, it was given a profile for the design of reactive distillation to produce methyl acetate from acetic acid and methanol in the presence of sulfuric acid as catalyst. The conversion of the reaction was studied as function to time, reflux ratio and feed mole ratio and predicted using Multilayer Perceptron through SPSS software 21. The result shows that using ANN will reduce the Squares Error to 0.262 and the Relative Error to 12.1%.
Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.
Hu, Yi-Chung
2014-01-01
On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.
FPGA Implementation of Multilayer Perceptron for Modeling of Photovoltaic panel
Mekki, H.; Mellit, A.; Salhi, H.; Belhout, K.
2008-06-01
The Number of electronic applications using artificial neural network-based solutions has increased considerably in the last few years. However, their applications in photovoltaic systems are very limited. This paper introduces the preliminary result of the modeling and simulation of photovoltaic panel based on neural network and VHDL-language. In fact, an experimental database of meteorological data (irradiation, temperature) and output electrical generation signals of the PV-panel (current and voltage) has been used in this study. The inputs of the ANN-PV-panel are the daily total irradiation and mean average temperature while the outputs are the current and voltage generated from the panel. Firstly, a dataset of 4x364 have been used for training the network. Subsequently, the neural network (MLP) corresponding to PV-panel is simulated using VHDL language based on the saved weights and bias of the network. Simulation results of the trained MLP-PV panel based on Matlab and VHDL are presented. The proposed PV-panel model based ANN and VHDL permit to evaluate the performance PV-panel using only the environmental factors and involves less computational efforts, and it can be used for predicting the output electrical energy from the PV-panel.
Hybrid combination of multi-layer perceptron and neutron activation ...
Indian Academy of Sciences (India)
... the irradiation and consecutive measurement of the percentage of the constituent elements in different cement samples were done using MCNPX with γ -ray spectra as the output. Specific peaks of Ca, Si, Al, and Fe obtained from these spectra were used as input for artificial neural network (18 of them for training and 8 for ...
Hybrid combination of multi-layer perceptron and neutron activation ...
Indian Academy of Sciences (India)
2017-01-04
Jan 4, 2017 ... Pramana – J. Phys. (2017) 88: 24. Artificial neural network (ANN) has been used many times in nuclear techniques due to its classification, clustering, and prediction abilities. ANN can be used in artificial intelligence techniques for detecting drugs and explosives using neutron computerized tomography.
On Clifford neurons and Clifford multi-layer perceptrons.
Buchholz, Sven; Sommer, Gerald
2008-09-01
We study the framework of Clifford algebra for the design of neural architectures capable of processing different geometric entities. The benefits of this model-based computation over standard real-valued networks are demonstrated. One particular example thereof is the new class of so-called Spinor Clifford neurons. The paper provides a sound theoretical basis to Clifford neural computation. For that purpose the new concepts of isomorphic neurons and isomorphic representations are introduced. A unified training rule for Clifford MLPs is also provided. The topic of activation functions for Clifford MLPs is discussed in detail for all two-dimensional Clifford algebras for the first time.
A Pattern Recognition Approach to Understanding the Multi-Layer Perceptron.
1986-07-01
8217iinsky and Papert Ell showed that an associative network with no hidden layers, for which there was a training algorithm, could not perform all...suggested. 10 V REFERENCES [13 M. L. Minsky and S. Papert , Perceptrons. Cambridge, MA: MIT Press, 1969. (23 D. E. Rumelhart, G. E. Hinton and R. J. Williams
Zhang, Yudong; Sun, Yi; Phillips, Preetha; Liu, Ge; Zhou, Xingxing; Wang, Shuihua
2016-07-01
This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 fractional Fourier entropy (FRFE) features were extracted from each brain image. Next, we submit those features to a multi-layer perceptron (MLP) classifier. Two improvements were proposed for MLP. One improvement is the pruning technique that determines the optimal hidden neuron number. We compared three pruning techniques: dynamic pruning (DP), Bayesian detection boundaries (BDB), and Kappa coefficient (KC). The other improvement is to use the adaptive real-coded biogeography-based optimization (ARCBBO) to train the biases and weights of MLP. The experiments showed that the proposed FRFE + KC-MLP + ARCBBO achieved an average accuracy of 99.53 % based on 10 repetitions of K-fold cross validation, which was better than 11 recent PBDS methods.
Vaiana, Michael; Muldoon, Sarah Feldt
2018-01-01
The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.
DEFF Research Database (Denmark)
Dickison, Mark; Magnani, Matteo; Rossi, Luca
Multilayer networks, in particular multilayer social networks, where users belong to and interact on different networks at the same time, are an active research area in social network analysis, computer science, and physics. These networks have traditionally been studied within these separate...... social network systems, the evolution of interconnected social networks, and dynamic processes such as information spreading. A single real dataset is used to illustrate the concepts presented throughout the book, demonstrating both the practical utility and the potential shortcomings of the various...... research communities, leading to the development of several independent models and methods to deal with the same set of problems. This book unifies and consolidates existing practical and theoretical knowledge on multilayer networks including data collection and analysis, modeling, and mining of multilayer...
DEFF Research Database (Denmark)
Dickison, Mark; Magnani, Matteo; Rossi, Luca
social network systems, the evolution of interconnected social networks, and dynamic processes such as information spreading. A single real dataset is used to illustrate the concepts presented throughout the book, demonstrating both the practical utility and the potential shortcomings of the various......Multilayer networks, in particular multilayer social networks, where users belong to and interact on different networks at the same time, are an active research area in social network analysis, computer science, and physics. These networks have traditionally been studied within these separate...... research communities, leading to the development of several independent models and methods to deal with the same set of problems. This book unifies and consolidates existing practical and theoretical knowledge on multilayer networks including data collection and analysis, modeling, and mining of multilayer...
DEFF Research Database (Denmark)
Dickison, Mark; Magnani, Matteo; Rossi, Luca
Multilayer networks, in particular multilayer social networks, where users belong to and interact on different networks at the same time, are an active research area in social network analysis, computer science, and physics. These networks have traditionally been studied within these separate...... social network systems, the evolution of interconnected social networks, and dynamic processes such as information spreading. A single real dataset is used to illustrate the concepts presented throughout the book, demonstrating both the practical utility and the potential shortcomings of the various...
Directory of Open Access Journals (Sweden)
Alejandro J. Orozco-Naranjo
2013-11-01
Full Text Available This paper presents the results obtained by developing a methodology to detect 5 types of heartbeats (Normal (N, Right bundle branch block (RBBB, Left bundle branch block (LBBB, Premature atrial contraction (APC and Premature ventricular contraction (PVC, using Wavelet transform packets with non-adaptative mode applied on features extraction from heartbeats. It was used the Shannon function to calculate the entropy and It was added an identification nodes stage per every type of cardiac signal in the Wavelet tree. The using of Wavelet packets transform allows the access to information which results of decomposition of low and high frecuency, giving providing a more integral analysis than achieved by the discrete Wavelet transform. Three families of mother Wavelet were evaluated on transformation: Daubechies, Symlet and Reverse Biorthogonal, which were results from a previous research in that were identified the mother Wavelet that had higher entropy with the cardiac signals. With non-adaptive mode, the computational cost is reduced when Wavelet packets are used; this cost represents the most marked disadvantage from the transform. To classify the heartbeats were used Support Vector Machines and Multilayer Perceptron. The best classification error was achieved employing Support Vector Machine and a radial basis function; it was 2.57 %.
Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method
Voyant, Cyril; Tamas, Wani; Paoli, Christophe; Balu, Aurélia; Muselli, Marc; Nivet, Marie-Laure; Notton, Gilles
2014-03-01
A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its "black box" aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where "all" configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA.
DEFF Research Database (Denmark)
Dickison, Mark; Magnani, Matteo; Rossi, Luca
social network systems, the evolution of interconnected social networks, and dynamic processes such as information spreading. A single real dataset is used to illustrate the concepts presented throughout the book, demonstrating both the practical utility and the potential shortcomings of the various...... research communities, leading to the development of several independent models and methods to deal with the same set of problems. This book unifies and consolidates existing practical and theoretical knowledge on multilayer networks including data collection and analysis, modeling, and mining of multilayer...
Uysal, Gökçen; Şensoy, Aynur; Şorman, A. Arda
2016-12-01
This paper investigates the contribution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Snow Cover Area (SCA) product and in-situ snow depth measurements to Artificial Neural Network model (ANN) based daily streamflow forecasting in a mountainous river basin. In order to represent non-linear structure of the snowmelt process, Multi-Layer Perceptron (MLP) Feed-Forward Backpropagation (FFBP) architecture is developed and applied in Upper Euphrates River Basin (10,275 km2) of Turkey where snowmelt constitutes approximately 2/3 of total annual volume of runoff during spring and early summer months. Snowmelt season is evaluated between March and July; 7 years (2002-2008) seasonal daily data are used during training while 3 years (2009-2011) seasonal daily data are split for forecasting. One of the fastest ANN training algorithms, the Levenberg-Marquardt, is used for optimization of the network weights and biases. The consistency of the network is checked with four performance criteria: coefficient of determination (R2), Nash-Sutcliffe model efficiency (ME), root mean square error (RMSE) and mean absolute error (MAE). According to the results, SCA observations provide useful information for developing of a neural network model to predict snowmelt runoff, whereas snow depth data alone are not sufficient. The highest performance is experienced when total daily precipitation, average air temperature data are combined with satellite snow cover data. The data preprocessing technique of Discrete Wavelet Analysis (DWA) is coupled with MLP modeling to further improve the runoff peak estimates. As a result, Nash-Sutcliffe model efficiency is increased from 0.52 to 0.81 for training and from 0.51 to 0.75 for forecasting. Moreover, the results are compared with that of a conceptual model, Snowmelt Runoff Model (SRM), application using SCA as an input. The importance and the main contribution of this study is to use of satellite snow products and data
Quantum perceptron over a field and neural network architecture selection in a quantum computer.
da Silva, Adenilton José; Ludermir, Teresa Bernarda; de Oliveira, Wilson Rosa
2016-04-01
In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator. Copyright © 2016 Elsevier Ltd. All rights reserved.
Multilayer perceptron neural network for downscaling rainfall in arid ...
Indian Academy of Sciences (India)
rent and future climate is therefore very essential. (Huth et al. 2008). General circulation models (GCMs) ... studies have been conducted for downscaling rain- fall in the arid regions such as, the studies by Sen et al. ...... developed from statistical downscaling methods; www. ipcc-data.org/guidelines/dgm no2 v1 09 2004.pdf.
Directory of Open Access Journals (Sweden)
Paul Onubi Ayegba
2016-12-01
Full Text Available Multilayer Perceptron (MLP models have been developed to predict twophase average void fraction and probability density function (PDF of void fraction in 90o bends. The Artificial Neural Network (ANN methodology was reported using MLP trained with 2 algorithms. Logarithmic sigmoid transfer function was used in a single hidden layer for both algorithms (Gradient descent (GDMV and Levenberg-Marquardt (LM algorithms. Both MLP models were optimised by varying the number of neurons in the hidden layer while monitoring the Mean Square Error (MSE. The performance of the models was evaluated using the Average Absolute Relative Error (AARE and Cross Correlation Coefficient (R. Both MLP models developed for the prediction of average void faction before the bend performed excellently well. However, the MLP model trained with LM algorithm having 3 neurons in the hidden layer gave better performance. Similarly, the MLP model trained with LM algorithm, having 11 neurons in the hidden layer for the prediction of PDF of void fraction before the bend gave excellent prediction. Model performance for the MLP models after the bend gave poor generalisation property. However, the MLP model based on GDMV algorithm gave better prediction for predicting average void fraction and PDF of void fraction after the bend. It was concluded that MLP models may with some confidence be used to predict the average void fraction and the PDFs of void fraction observed before a vertical 90o bend
A method of parameterising a feed forward multi-layered perceptron ...
African Journals Online (AJOL)
This approach is demonstrated through the modelling of four South African economic variables, namely the average monthly returns on the money, bond and equity markets as well as monthly inflation. Artificial neural networks can be constructed on the aforementioned variables in isolation or, jointly, in an integrated model.
Low-cost autonomous perceptron neural network inspired by quantum computation
Zidan, Mohammed; Abdel-Aty, Abdel-Haleem; El-Sadek, Alaa; Zanaty, E. A.; Abdel-Aty, Mahmoud
2017-11-01
Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms.
Chen, Fangyue; Chen, Guanrong Ron; He, Guolong; Xu, Xiubin; He, Qinbin
2009-10-01
Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class, and 3) non-LSBF and non-PBF class. To implement these functions, UP takes different kinds of simple topological structures in which each contains at most one hidden layer along with the smallest possible number of hidden neurons. Inspired by the concept of DNA sequences in biological systems, a novel learning algorithm named DNA-like learning is developed, which is able to quickly train a network with any prescribed BF. The focus is on performing LSBF and PBF by a single-layer perceptron (SLP) with the new algorithm. Two criteria for LSBF and PBF are proposed, respectively, and a new measure for a BF, named nonlinearly separable degree (NLSD), is introduced. In the sense of this measure, the PBF is the most complex one. The new algorithm has many advantages including, in particular, fast running speed, good robustness, and no need of considering the convergence property. For example, the number of iterations and computations in implementing the basic 2-bit logic operations such as AND, OR, and XOR by using the new algorithm is far smaller than the ones needed by using other existing algorithms such as error-correction (EC) and backpropagation (BP) algorithms. Moreover, the synaptic weights and threshold values derived from UP can be directly used in designing of the template of cellular neural networks (CNNs), which has been considered as a new spatial-temporal sensory computing paradigm.
Mathematical Formulation of Multilayer Networks
Directory of Open Access Journals (Sweden)
Manlio De Domenico
2013-12-01
Full Text Available A network representation is useful for describing the structure of a large variety of complex systems. However, most real and engineered systems have multiple subsystems and layers of connectivity, and the data produced by such systems are very rich. Achieving a deep understanding of such systems necessitates generalizing “traditional” network theory, and the newfound deluge of data now makes it possible to test increasingly general frameworks for the study of networks. In particular, although adjacency matrices are useful to describe traditional single-layer networks, such a representation is insufficient for the analysis and description of multiplex and time-dependent networks. One must therefore develop a more general mathematical framework to cope with the challenges posed by multilayer complex systems. In this paper, we introduce a tensorial framework to study multilayer networks, and we discuss the generalization of several important network descriptors and dynamical processes—including degree centrality, clustering coefficients, eigenvector centrality, modularity, von Neumann entropy, and diffusion—for this framework. We examine the impact of different choices in constructing these generalizations, and we illustrate how to obtain known results for the special cases of single-layer and multiplex networks. Our tensorial approach will be helpful for tackling pressing problems in multilayer complex systems, such as inferring who is influencing whom (and by which media in multichannel social networks and developing routing techniques for multimodal transportation systems.
Mathematical Formulation of Multilayer Networks
De Domenico, Manlio; Solé-Ribalta, Albert; Cozzo, Emanuele; Kivelä, Mikko; Moreno, Yamir; Porter, Mason A.; Gómez, Sergio; Arenas, Alex
2013-10-01
A network representation is useful for describing the structure of a large variety of complex systems. However, most real and engineered systems have multiple subsystems and layers of connectivity, and the data produced by such systems are very rich. Achieving a deep understanding of such systems necessitates generalizing “traditional” network theory, and the newfound deluge of data now makes it possible to test increasingly general frameworks for the study of networks. In particular, although adjacency matrices are useful to describe traditional single-layer networks, such a representation is insufficient for the analysis and description of multiplex and time-dependent networks. One must therefore develop a more general mathematical framework to cope with the challenges posed by multilayer complex systems. In this paper, we introduce a tensorial framework to study multilayer networks, and we discuss the generalization of several important network descriptors and dynamical processes—including degree centrality, clustering coefficients, eigenvector centrality, modularity, von Neumann entropy, and diffusion—for this framework. We examine the impact of different choices in constructing these generalizations, and we illustrate how to obtain known results for the special cases of single-layer and multiplex networks. Our tensorial approach will be helpful for tackling pressing problems in multilayer complex systems, such as inferring who is influencing whom (and by which media) in multichannel social networks and developing routing techniques for multimodal transportation systems.
Directory of Open Access Journals (Sweden)
Rezvan Abbasi
2017-08-01
Full Text Available Electroencephalogram signals (EEG have always been used in medical diagnosis. Evaluation of the statistical characteristics of EEG signals is actually the foundation of all brain signal processing methods. Since the correct prediction of disease status is of utmost importance, the goal is to use those models that have minimum error and maximum reliability. In anautomatic epileptic seizure detection system, we should be able to distinguish between EEG signals before, during and after seizure. Extracting useful characteristics from EEG data can greatly increase the classification accuracy. In this new approach, we first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform(DWT and then we derive statistical characteristics such as maximum, minimum, average and standard deviation for each sub-band. A multilayer perceptron (MLPneural network was used to assess the different scenarios of healthy and seizure among the collected signal sets. In order to assess the success and effectiveness of the proposed method, the confusion matrix was used and its accuracy was achieved98.33 percent. Due to the limitations and obstacles in analyzing EEG signals, the proposed method can greatly help professionals experimentally and visually in the classification and diagnosis of epileptic seizures.
Ghorbani, M. A.; Deo, Ravinesh C.; Yaseen, Zaher Mundher; H. Kashani, Mahsa; Mohammadi, Babak
2017-08-01
An accurate computational approach for the prediction of pan evaporation over daily time horizons is a useful decisive tool in sustainable agriculture and hydrological applications, particularly in designing the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this study, a hybrid predictive model (Multilayer Perceptron-Firefly Algorithm (MLP-FFA)) based on the FFA optimizer that is embedded within the MLP technique is developed and evaluated for its suitability for the prediction of daily pan evaporation. To develop the hybrid MLP-FFA model, the pan evaporation data measured between 2012 and 2014 for two major meteorological stations (Talesh and Manjil) located at Northern Iran are employed to train and test the predictive model. The ability of the hybrid MLP-FFA model is compared with the traditional MLP and support vector machine (SVM) models. The results are evaluated using five performance criteria metrics: root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NS), and the Willmott's Index (WI). Taylor diagrams are also used to examine the similarity between the observed and predicted pan evaporation data in the test period. Results show that an optimal MLP-FFA model outperforms the MLP and SVM model for both tested stations. For Talesh, a value of WI = 0.926, NS = 0.791, and RMSE = 1.007 mm day-1 is obtained using MLP-FFA model, compared with 0.912, 0.713, and 1.181 mm day-1 (MLP) and 0.916, 0.726, and 1.153 mm day-1 (SVM), whereas for Manjil, a value of WI = 0.976, NS = 0.922, and 1.406 mm day-1 is attained that contrasts 0.972, 0.901, and 1.583 mm day-1 (MLP) and 0.971, 0.893, and 1.646 mm day-1 (SVM). The results demonstrate the importance of the Firefly Algorithm applied to improve the performance of the MLP-FFA model, as verified through its better predictive performance compared to the MLP and SVM model.
A Multilayer Model of Computer Networks
Shchurov, Andrey A.
2015-01-01
The fundamental concept of applying the system methodology to network analysis declares that network architecture should take into account services and applications which this network provides and supports. This work introduces a formal model of computer networks on the basis of the hierarchical multilayer networks. In turn, individual layers are represented as multiplex networks. The concept of layered networks provides conditions of top-down consistency of the model. Next, we determined the...
Evolutionary games on multilayer networks: A colloquium
Wang, Zhen; Szolnoki, Attila; Perc, Matjaz
2015-01-01
Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling th...
Multilayer bootstrap network for unsupervised speaker recognition
Zhang, Xiao-Lei
2015-01-01
We apply multilayer bootstrap network (MBN), a recent proposed unsupervised learning method, to unsupervised speaker recognition. The proposed method first extracts supervectors from an unsupervised universal background model, then reduces the dimension of the high-dimensional supervectors by multilayer bootstrap network, and finally conducts unsupervised speaker recognition by clustering the low-dimensional data. The comparison results with 2 unsupervised and 1 supervised speaker recognition...
Directory of Open Access Journals (Sweden)
S.M. Hosseini-Moghari
2016-10-01
Full Text Available Introduction: Due to economic, social, and environmental perplexities associated with drought, it is considered as one of the most complex natural hazards. To investigate the beginning along with analyzing the direct impacts of drought; the significance of drought monitoring must be highlighted. Regarding drought management and its consequences alleviation, drought forecasting must be taken into account (11. The current research employed multi-layer perceptron (MLP, adaptive neuro-fuzzy inference system (ANFIS, radial basis function (RBF and general regression neural network (GRNN. It is interesting to note that, there has not been any record of applying GRNN in drought forecasting. Materials and Methods: Throughout this paper, Standard Precipitation Index (SPI was the basis of drought forecasting. To do so, the precipitation data of Gonbad Kavous station during the period of 1972-73 to 2006-07 were used. To provide short-term, mid-term, and long-term drought analysis; SPI for 1, 3, 6, 9, 12, and 24 months was evaluated. SPI evaluation benefited from four statistical distributions, namely, Gamma, Normal, Log-normal, and Weibull along with Kolmogrov-Smirnov (K-S test. Later, to compare the capabilities of four utilized neural networks for drought forecasting; MLP, ANFIS, RBF, and GRNN were applied. MLP as a multi-layer network, which has a sigmoid activation function in hidden layer plus linear function in output layer, can be considered as a powerful regressive tool. ANFIS besides adaptive neuro networks, employed fuzzy logic. RBF, the foundation of radial basis networks, is a three-layer network with Gaussian function in its hidden layer, and a linear function in the output layer. GRNN is another type of RBF which is used for radial basis regressive problems. The performance criteria of the research were as follows: Correlation (R2, Root Mean Square Error (RMSE, Mean Absolute Error (MAE. Results Discussion: According to statistical distribution
Directory of Open Access Journals (Sweden)
Chudech Losiri
2016-07-01
Full Text Available Urban expansion is considered as one of the most important problems in several developing countries. Bangkok Metropolitan Region (BMR is the urbanized and agglomerated area of Bangkok Metropolis (BM and its vicinity, which confronts the expansion problem from the center of the city. Landsat images of 1988, 1993, 1998, 2003, 2008, and 2011 were used to detect the land use and land cover (LULC changes. The demographic and economic data together with corresponding maps were used to determine the driving factors for land conversions. This study applied Cellular Automata-Markov Chain (CA-MC and Multi-Layer Perceptron-Markov Chain (MLP-MC to model LULC and urban expansions. The performance of the CA-MC and MLP-MC yielded more than 90% overall accuracy to predict the LULC, especially the MLP-MC method. Further, the annual population and economic growth rates were considered to produce the land demand for the LULC in 2014 and 2035 using the statistical extrapolation and system dynamics (SD. It was evident that the simulated map in 2014 resulting from the SD yielded the highest accuracy. Therefore, this study applied the SD method to generate the land demand for simulating LULC in 2035. The outcome showed that urban occupied the land around a half of the BMR.
First steps towards the realization of a double layer perceptron based on organic memristive devices
Directory of Open Access Journals (Sweden)
A. V. Emelyanov
2016-11-01
Full Text Available Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.
Information Propagation in Clustered Multilayer Networks
Zhuang, Yong
2015-01-01
In today's world, individuals interact with each other in more complicated patterns than ever. Some individuals engage through online social networks (e.g., Facebook, Twitter), while some communicate only through conventional ways (e.g., face-to-face). Therefore, understanding the dynamics of information propagation among humans calls for a multi-layer network model where an online social network is conjoined with a physical network. In this work, we initiate a study of information diffusion in a clustered multi-layer network model, where all constituent layers are random networks with high clustering. We assume that information propagates according to the SIR model and with different information transmissibility across the networks. We give results for the conditions, probability, and size of information epidemics, i.e., cases where information starts from a single individual and reaches a positive fraction of the population. We show that increasing the level of clustering in either one of the layers increas...
Evolutionary games on multilayer networks: a colloquium
Wang, Zhen; Wang, Lin; Szolnoki, Attila; Perc, Matjaž
2015-05-01
Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling through the utilities of players, through the flow of information, as well as through the popularity of different strategies on different network layers. The colloquium highlights the importance of pattern formation and collective behavior for the promotion of cooperation under adverse conditions, as well as the synergies between network science and evolutionary game theory.
The polymorphic, multilayered and networked urbanised territory
DEFF Research Database (Denmark)
Nielsen, Tom
2015-01-01
The discussion of the network city has in recent years been supplemented by an increasing interest in reconsidering the notion of territory. Looking into both geographical and urban design theories, we find examples of a focus on how the networks of the city not only connect them irreversibly...... in theory. The concept of The Polymorphic, Multilayered and Networked Urbanised Territory is introduced to grasp the reality experienced in European regions outside the largest and most potent versions of contemporary cities....
Multi-Layer and Recursive Neural Networks for Metagenomic Classification.
Ditzler, Gregory; Polikar, Robi; Rosen, Gail
2015-09-01
Recent advances in machine learning, specifically in deep learning with neural networks, has made a profound impact on fields such as natural language processing, image classification, and language modeling; however, feasibility and potential benefits of the approaches to metagenomic data analysis has been largely under-explored. Deep learning exploits many layers of learning nonlinear feature representations, typically in an unsupervised fashion, and recent results have shown outstanding generalization performance on previously unseen data. Furthermore, some deep learning methods can also represent the structure in a data set. Consequently, deep learning and neural networks may prove to be an appropriate approach for metagenomic data. To determine whether such approaches are indeed appropriate for metagenomics, we experiment with two deep learning methods: i) a deep belief network, and ii) a recursive neural network, the latter of which provides a tree representing the structure of the data. We compare these approaches to the standard multi-layer perceptron, which has been well-established in the machine learning community as a powerful prediction algorithm, though its presence is largely missing in metagenomics literature. We find that traditional neural networks can be quite powerful classifiers on metagenomic data compared to baseline methods, such as random forests. On the other hand, while the deep learning approaches did not result in improvements to the classification accuracy, they do provide the ability to learn hierarchical representations of a data set that standard classification methods do not allow. Our goal in this effort is not to determine the best algorithm in terms accuracy-as that depends on the specific application-but rather to highlight the benefits and drawbacks of each of the approach we discuss and provide insight on how they can be improved for predictive metagenomic analysis.
Cruz, Febus Reidj G.; Padilla, Dionis A.; Hortinela, Carlos C.; Bucog, Krissel C.; Sarto, Mildred C.; Sia, Nirlu Sebastian A.; Chung, Wen-Yaw
2017-02-01
This study is about the determination of moisture content of milled rice using image processing technique and perceptron neural network algorithm. The algorithm involves several inputs that produces an output which is the moisture content of the milled rice. Several types of milled rice are used in this study, namely: Jasmine, Kokuyu, 5-Star, Ifugao, Malagkit, and NFA rice. The captured images are processed using MATLAB R2013a software. There is a USB dongle connected to the router which provided internet connection for online web access. The GizDuino IOT-644 is used for handling the temperature and humidity sensor, and for sending and receiving of data from computer to the cloud storage. The result is compared to the actual moisture content range using a moisture tester for milled rice. Based on results, this study provided accurate data in determining the moisture content of the milled rice.
Random walk centrality in interconnected multilayer networks
Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Alex
2015-01-01
Real-world complex systems exhibit multiple levels of relationships. In many cases they require to be modeled as interconnected multilayer networks, characterizing interactions of several types simultaneously. It is of crucial importance in many fields, from economics to biology and from urban planning to social sciences, to identify the most (or the less) influential nodes in a network using centrality measures. However, defining the centrality of actors in interconnected complex networks is not trivial. In this paper, we rely on the tensorial formalism recently proposed to characterize and investigate this kind of complex topologies, and extend two well known random walk centrality measures, the random walk betweenness and closeness centrality, to interconnected multilayer networks. For each of the measures we provide analytical expressions that completely agree with numerically results.
Initialization of multilayer forecasting artifical neural networks
Bochkarev, Vladimir V.; Maslennikova, Yulia S.
2014-01-01
In this paper, a new method was developed for initialising artificial neural networks predicting dynamics of time series. Initial weighting coefficients were determined for neurons analogously to the case of a linear prediction filter. Moreover, to improve the accuracy of the initialization method for a multilayer neural network, some variants of decomposition of the transformation matrix corresponding to the linear prediction filter were suggested. The efficiency of the proposed neural netwo...
The multilayer nature of ecological networks.
Pilosof, Shai; Porter, Mason A; Pascual, Mercedes; Kéfi, Sonia
2017-03-23
Although networks provide a powerful approach to study a large variety of ecological systems, their formulation does not typically account for multiple interaction types, interactions that vary in space and time, and interconnected systems such as networks of networks. The emergent field of 'multilayer networks' provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it specifically allows one to differentiate and model 'intralayer' and 'interlayer' connectivity. The framework provides a set of concepts and tools that can be adapted and applied to ecology, facilitating research on high-dimensional, heterogeneous systems in nature. Here, we formally define ecological multilayer networks based on a review of previous, related approaches; illustrate their application and potential with analyses of existing data; and discuss limitations, challenges, and future applications. The integration of multilayer network theory into ecology offers largely untapped potential to investigate ecological complexity and provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.
Measure of Node Similarity in Multilayer Networks
DEFF Research Database (Denmark)
Møllgaard, Anders; Zettler, Ingo; Dammeyer, Jesper
2016-01-01
university.Our analysis is based on data obtained using smartphones equipped with customdata collection software, complemented by questionnaire-based data. The networkof social contacts is represented as a weighted multilayer network constructedfrom different channels of telecommunication as well as data......The weight of links in a network is often related to the similarity of thenodes. Here, we introduce a simple tunable measure for analysing the similarityof nodes across different link weights. In particular, we use the measure toanalyze homophily in a group of 659 freshman students at a large...... might bepresent in one layer of the multilayer network and simultaneously be absent inthe other layers. For a variable such as gender, our measure reveals atransition from similarity between nodes connected with links of relatively lowweight to dis-similarity for the nodes connected by the strongest...
Measure of Node Similarity in Multilayer Networks
DEFF Research Database (Denmark)
Møllgaard, Anders; Zettler, Ingo; Dammeyer, Jesper
2016-01-01
The weight of links in a network is often related to the similarity of thenodes. Here, we introduce a simple tunable measure for analysing the similarityof nodes across different link weights. In particular, we use the measure toanalyze homophily in a group of 659 freshman students at a large...... university.Our analysis is based on data obtained using smartphones equipped with customdata collection software, complemented by questionnaire-based data. The networkof social contacts is represented as a weighted multilayer network constructedfrom different channels of telecommunication as well as data...
A NEURAL OSCILLATOR-NETWORK MODEL OF TEMPORAL PATTERN GENERATION
Schomaker, Lambert
Most contemporary neural network models deal with essentially static, perceptual problems of classification and transformation. Models such as multi-layer feedforward perceptrons generally do not incorporate time as an essential dimension, whereas biological neural networks are inherently temporal
Multilayer motif analysis of brain networks
Battiston, Federico; Nicosia, Vincenzo; Chavez, Mario; Latora, Vito
2017-04-01
In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively, obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between the structure and function in the human brain.
Measure of Node Similarity in Multilayer Networks.
Directory of Open Access Journals (Sweden)
Anders Mollgaard
Full Text Available The weight of links in a network is often related to the similarity of the nodes. Here, we introduce a simple tunable measure for analysing the similarity of nodes across different link weights. In particular, we use the measure to analyze homophily in a group of 659 freshman students at a large university. Our analysis is based on data obtained using smartphones equipped with custom data collection software, complemented by questionnaire-based data. The network of social contacts is represented as a weighted multilayer network constructed from different channels of telecommunication as well as data on face-to-face contacts. We find that even strongly connected individuals are not more similar with respect to basic personality traits than randomly chosen pairs of individuals. In contrast, several socio-demographics variables have a significant degree of similarity. We further observe that similarity might be present in one layer of the multilayer network and simultaneously be absent in the other layers. For a variable such as gender, our measure reveals a transition from similarity between nodes connected with links of relatively low weight to dis-similarity for the nodes connected by the strongest links. We finally analyze the overlap between layers in the network for different levels of acquaintanceships.
Multilayer Statistical Intrusion Detection in Wireless Networks
Directory of Open Access Journals (Sweden)
Noureddine Boudriga
2008-12-01
Full Text Available The rapid proliferation of mobile applications and services has introduced new vulnerabilities that do not exist in fixed wired networks. Traditional security mechanisms, such as access control and encryption, turn out to be inefficient in modern wireless networks. Given the shortcomings of the protection mechanisms, an important research focuses in intrusion detection systems (IDSs. This paper proposes a multilayer statistical intrusion detection framework for wireless networks. The architecture is adequate to wireless networks because the underlying detection models rely on radio parameters and traffic models. Accurate correlation between radio and traffic anomalies allows enhancing the efficiency of the IDS. A radio signal fingerprinting technique based on the maximal overlap discrete wavelet transform (MODWT is developed. Moreover, a geometric clustering algorithm is presented. Depending on the characteristics of the fingerprinting technique, the clustering algorithm permits to control the false positive and false negative rates. Finally, simulation experiments have been carried out to validate the proposed IDS.
The application of deep confidence network in the problem of image recognition
Directory of Open Access Journals (Sweden)
Chumachenko О.І.
2016-12-01
Full Text Available In order to study the concept of deep learning, in particular the substitution of multilayer perceptron on the corresponding network of deep confidence, computer simulations of the learning process to test voters was carried out. Multi-layer perceptron has been replaced by a network of deep confidence, consisting of successive limited Boltzmann machines. After training of a network of deep confidence algorithm of layer-wise training it was found that the use of networks of deep confidence greatly improves the accuracy of multilayer perceptron training by method of reverse distribution errors.
Design considerations for energy efficient, resilient, multi-layer networks
DEFF Research Database (Denmark)
Fagertun, Anna Manolova; Hansen, Line Pyndt; Ruepp, Sarah Renée
2016-01-01
This work investigates different network design considerations with respect to energy-efficiency, under green-field resilient multi-layer network deployment. The problem of energy efficient, reliable multi-layer network design is known to result in different trade-offs between key performance...... in multi-layer networks and performance measures such as network resource utilization, availability, agility to traffic fluctuations and energy consumption. A green-field network deployment scenario is considered, where different resiliency methods, design methodologies and grooming strategies are applied...
Neural network simulation of the industrial producer price index dynamical series
Soshnikov, L. E.
2013-01-01
This paper is devoted the simulation and forecast of dynamical series of the economical indicators. Multilayer perceptron and Radial basis function neural networks have been used. The neural networks model results are compared with the econometrical modeling.
Identifying key nodes in multilayer networks based on tensor decomposition
Wang, Dingjie; Wang, Haitao; Zou, Xiufen
2017-06-01
The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.
Spatial Dynamics of Multilayer Cellular Neural Networks
Wu, Shi-Liang; Hsu, Cheng-Hsiung
2018-02-01
The purpose of this work is to study the spatial dynamics of one-dimensional multilayer cellular neural networks. We first establish the existence of rightward and leftward spreading speeds of the model. Then we show that the spreading speeds coincide with the minimum wave speeds of the traveling wave fronts in the right and left directions. Moreover, we obtain the asymptotic behavior of the traveling wave fronts when the wave speeds are positive and greater than the spreading speeds. According to the asymptotic behavior and using various kinds of comparison theorems, some front-like entire solutions are constructed by combining the rightward and leftward traveling wave fronts with different speeds and a spatially homogeneous solution of the model. Finally, various qualitative features of such entire solutions are investigated.
Raj, Retheep; Sivanandan, K S
2017-01-01
Estimation of elbow dynamics has been the object of numerous investigations. In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals. Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm. The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347. The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.
Clustering network layers with the strata multilayer stochastic block model.
Stanley, Natalie; Shai, Saray; Taylor, Dane; Mucha, Peter J
2016-01-01
Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisely extract information from a multilayer network, we propose to identify and combine sets of layers with meaningful similarities in community structure. In this paper, we describe the "strata multilayer stochastic block model" (sMLSBM), a probabilistic model for multilayer community structure. The central extension of the model is that there exist groups of layers, called "strata", which are defined such that all layers in a given stratum have community structure described by a common stochastic block model (SBM). That is, layers in a stratum exhibit similar node-to-community assignments and SBM probability parameters. Fitting the sMLSBM to a multilayer network provides a joint clustering that yields node-to-community and layer-to-stratum assignments, which cooperatively aid one another during inference. We describe an algorithm for separating layers into their appropriate strata and an inference technique for estimating the SBM parameters for each stratum. We demonstrate our method using synthetic networks and a multilayer network inferred from data collected in the Human Microbiome Project.
Ferreira, B D L; Sebastião, R C O; Yoshida, M I; Mussel, W N; Fialho, S L; Barbosa, J
2016-01-01
Kinetic study by thermal decomposition of antiretroviral drugs, Efavirenz (EFV) and Lamivudine (3TC), usually present in the HIV cocktail, can be done by individual adjustment of the solid decomposition models. However, in some cases unacceptable errors are found using this methodology. To circumvent this problem, here is proposed to use a multilayer perceptron neural network (MLP), with an appropriate algorithm, which constitutes a linearization of the network by setting weights between the input layer and the intermediate one and the use of Kinetic models as activation functions of neurons in the hidden layer. The interconnection weights between that intermediate layer and output layer determines the contribution of each model in the overall fit of the experimental data. Thus, the decomposition is assumed to be a phenomenon that can occur following different kinetic processes. In the investigated data, the kinetic thermal decomposition process was best described by R1 and D4 model for all temperatures to EF...
Wave transmission prediction of multilayer floating breakwater using neural network
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Patil, S.G.; Hegde, A.V.
in unison to solve a specific problem. The network learns through examples, so it requires good examples to train properly and further a trained network model can be used for prediction purpose. Proceedings of ICOE 2009 Wave transmission... prediction of multilayer floating breakwater using neural network 577 In order to allow the network to learn both non-linear and linear relationships between input nodes and output nodes, multiple-layer neural networks are often used...
Vibration Based Damage Assessment of a Cantilever using a Neural Network
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Rytter, A.
In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with with the Backpropagation Algorithm as a non-destructive damage assessment technique to locate and quantify a damage in structures is investigated.......In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with with the Backpropagation Algorithm as a non-destructive damage assessment technique to locate and quantify a damage in structures is investigated....
Directory of Open Access Journals (Sweden)
Emin AVCI
2007-06-01
Full Text Available Especially for the last decade, the neural network models have been applied to solve financial problems like portfolio construction and stock market forecasting. Among the alternative neural network models, the multilayer perceptron models are expected to be effective and widely applied in financial forecasting. This study examines the forecasting power multilayer perceptron models for daily and sessional returns of ISE-100 index. The findings imply that the multilayer perceptron models presented promising performance in forecasting the ISE-100 index returns. However, further emphasis should be placed on different input variables and model architectures in order to improve the forecasting performances.
Heuristic urban transportation network design method, a multilayer coevolution approach
Ding, Rui; Ujang, Norsidah; Hamid, Hussain bin; Manan, Mohd Shahrudin Abd; Li, Rong; Wu, Jianjun
2017-08-01
The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users' route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends.
Optimization of multilayer neural network parameters for speaker recognition
Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka
2016-05-01
This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.
Authentication Based on Multilayer Clustering in Ad Hoc Networks
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Suh Heyi-Sook
2005-01-01
Full Text Available In this paper, we describe a secure cluster-routing protocol based on a multilayer scheme in ad hoc networks. This work provides scalable, threshold authentication scheme in ad hoc networks. We present detailed security threats against ad hoc routing protocols, specifically examining cluster-based routing. Our proposed protocol, called "authentication based on multilayer clustering for ad hoc networks" (AMCAN, designs an end-to-end authentication protocol that relies on mutual trust between nodes in other clusters. The AMCAN strategy takes advantage of a multilayer architecture that is designed for an authentication protocol in a cluster head (CH using a new concept of control cluster head (CCH scheme. We propose an authentication protocol that uses certificates containing an asymmetric key and a multilayer architecture so that the CCH is achieved using the threshold scheme, thereby reducing the computational overhead and successfully defeating all identified attacks. We also use a more extensive area, such as a CCH, using an identification protocol to build a highly secure, highly available authentication service, which forms the core of our security framework.
Vulnerability of multi-layer networks under malware spreading
Vida, Rafael; Cuenda, Sara
2013-01-01
Computer viruses are evolving by developing spreading mechanisms based on the use of multiple vectors of propagation. The use of the social network as an extra vector of attack to penetrate the security measures in IP networks is improving the effectiveness of the malware, and have therefore been used by the most aggressive viruses, like Conficker and Stuxnet. In this work we use multi-layer networks to model the propagation of these kind of viruses. In particular, we study the propagation of a SIS model on a multi-layer network where the state of each node is layer-independent and the dynamics in each network follows either a contact process or a reactive process, with different propagation rates. We show that the interplay between the layers leads to a non-trivial dynamics, with an epidemic onset that may trigger before it triggers in each isolated network. We apply this study to the case of a real two-layer network: a Spanish scientific community of Statistical Physics, formed by a social network of scient...
Deciphering versatility and cooperation in multilayer social networks
Sarkar, Camellia; Jalan, Sarika
2015-01-01
Despite large scale availability of social data, our understanding of the basic laws governing human behaviour remains limited, owing to the lack of a proper framework which can capture the interplay of various interdependent factors affecting social interactions. In the recent years, multilayer networks has increasingly been realized to provide an efficient framework for understanding the intricacies of complex real world systems. The present study encompasses the multilayer network analysis of Bollywood, the largest film industry of the world, comprising of a massive time-varying social data. Making around 1500 films annually, Bollywood has emerged as a globally recognized and appreciated platform for cultural exchange. This film industry acts as a mirror of the society and the rapidly changing nature of the society is reflected in the depictions of films. This renders this model system to provide a ripe platform to understand social behaviour by analyzing the patterns of evolution and the success of indivi...
Analysis of Natural Gradient Descent for Multilayer Neural Networks
Rattray, Magnus; Saad, David
1999-01-01
Natural gradient descent is a principled method for adapting the parameters of a statistical model on-line using an underlying Riemannian parameter space to redefine the direction of steepest descent. The algorithm is examined via methods of statistical physics which accurately characterize both transient and asymptotic behavior. A solution of the learning dynamics is obtained for the case of multilayer neural network training in the limit of large input dimension. We find that natural gradie...
Aryadoust, Vahid; Baghaei, Purya
2016-01-01
This study aims to examine the relationship between reading comprehension and lexical and grammatical knowledge among English as a foreign language students by using an Artificial Neural Network (ANN). There were 825 test takers administered both a second-language reading test and a set of psychometrically validated grammar and vocabulary tests.…
Management issues in multilayered network architectures
Piergigli, Mauro; Vercelli, Roberto; Zucchinali, A.
1996-12-01
The importance of managing networks and services is finally being accepted by both operators/service providers and HW/SW manufacturers. Standards are thus coming for managing solutions based on any new emerging technology. However standards and commercial solutions have different level of completeness and performance depending on the related managed technology. Thus, being real systems often composed of a mix of different technologies, the quality of their overall management is limited by the less mature and effective single technology management system. Today broadband networks can be seen as a layered structure including ATM, SDH and WDM optical layers. While the management of SDH is quite mature and significant results are reached for ATM management, the management of the WDM optical layer is still in its infancy. Therefore the WDM layer management is a critical factor in the development of the overall broadband network management. The paper, after few considerations on general public network management issues, presents some solutions currently under study for the WDM optical layer management. Although the improvement of the weaker single technology management system surely improves the quality of the overall layered network management, this is just one step along the path for optimizing it. The objective is in fact the integration of the single layer management systems into a global one, avoiding duplications and improving efficiency and effectiveness. The paper presents at the end some considerations about this topic that is rapidly getting hot because of the potential economical benefits such as integration can offer.
Global multi-layer network of human mobility.
Belyi, Alexander; Bojic, Iva; Sobolevsky, Stanislav; Sitko, Izabela; Hawelka, Bartosz; Rudikova, Lada; Kurbatski, Alexander; Ratti, Carlo
2017-07-03
Recent availability of geo-localized data capturing individual human activity together with the statistical data on international migration opened up unprecedented opportunities for a study on global mobility. In this paper, we consider it from the perspective of a multi-layer complex network, built using a combination of three datasets: Twitter, Flickr and official migration data. Those datasets provide different, but equally important insights on the global mobility - while the first two highlight short-term visits of people from one country to another, the last one - migration - shows the long-term mobility perspective, when people relocate for good. The main purpose of the paper is to emphasize importance of this multi-layer approach capturing both aspects of human mobility at the same time. On the one hand, we show that although the general properties of different layers of the global mobility network are similar, there are important quantitative differences among them. On the other hand, we demonstrate that consideration of mobility from a multi-layer perspective can reveal important global spatial patterns in a way more consistent with those observed in other available relevant sources of international connections, in comparison to the spatial structure inferred from each network layer taken separately.
A declarative approach to multi-layer path finding based on semantic network descriptions
Xu, L.; Dijkstra, F.; Marchal, D.; Taal, A.; Grosso, P.; de Laat, C.
2009-01-01
With the increasing demand for dynamic network connections between multiple research networks, a number of issues on multi-layer hybrid networks need to be addressed, such as network representation, path finding, and path provisioning. This paper focuses on solving the multi-layer path finding
Learning and coordinating in a multilayer network
Lugo, Haydee
2014-01-01
We introduce a two layer network model for social coordination incorporating two relevant ingredients: a) different networks of interaction to learn and to obtain a payoff , and b) decision making processes based both on social and strategic motivations. Two populations of agents are distributed in two layers with intralayer learning processes and playing interlayer a coordination game. We find that the skepticism about the wisdom of crowd and the local connectivity are the driving forces to accomplish full coordination of the two populations, while polarized coordinated layers are only possible for all-to-all interactions. Local interactions also allow for full coordination in the socially efficient Pareto-dominant strategy in spite of being the riskier one.
Learning and coordinating in a multilayer network
Lugo, Haydée; Miguel, Maxi San
2015-01-01
We introduce a two layer network model for social coordination incorporating two relevant ingredients: a) different networks of interaction to learn and to obtain a pay-off, and b) decision making processes based both on social and strategic motivations. Two populations of agents are distributed in two layers with intralayer learning processes and playing interlayer a coordination game. We find that the skepticism about the wisdom of crowd and the local connectivity are the driving forces to accomplish full coordination of the two populations, while polarized coordinated layers are only possible for all-to-all interactions. Local interactions also allow for full coordination in the socially efficient Pareto-dominant strategy in spite of being the riskier one.
Competitive dynamics of lexical innovations in multi-layer networks
Javarone, Marco Alberto
2014-04-01
We study the introduction of lexical innovations into a community of language users. Lexical innovations, i.e. new term added to people's vocabulary, plays an important role in the process of language evolution. Nowadays, information is spread through a variety of networks, including, among others, online and offline social networks and the World Wide Web. The entire system, comprising networks of different nature, can be represented as a multi-layer network. In this context, lexical innovations diffusion occurs in a peculiar fashion. In particular, a lexical innovation can undergo three different processes: its original meaning is accepted; its meaning can be changed or misunderstood (e.g. when not properly explained), hence more than one meaning can emerge in the population. Lastly, in the case of a loan word, it can be translated into the population language (i.e. defining a new lexical innovation or using a synonym) or into a dialect spoken by part of the population. Therefore, lexical innovations cannot be considered simply as information. We develop a model for analyzing this scenario using a multi-layer network comprising a social network and a media network. The latter represents the set of all information systems of a society, e.g. television, the World Wide Web and radio. Furthermore, we identify temporal directed edges between the nodes of these two networks. In particular, at each time-step, nodes of the media network can be connected to randomly chosen nodes of the social network and vice versa. In doing so, information spreads through the whole system and people can share a lexical innovation with their neighbors or, in the event they work as reporters, by using media nodes. Lastly, we use the concept of "linguistic sign" to model lexical innovations, showing its fundamental role in the study of these dynamics. Many numerical simulations have been performed to analyze the proposed model and its outcomes.
Advances in Artificial Neural Networks - Methodological Development and Application
Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...
Community detection, link prediction, and layer interdependence in multilayer networks
De Bacco, Caterina; Power, Eleanor A.; Larremore, Daniel B.; Moore, Cristopher
2017-04-01
Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substring material between genes of the malaria parasite.
Electroencephalography epilepsy classifications using hybrid cuckoo search and neural network
Pratiwi, A. B.; Damayanti, A.; Miswanto
2017-07-01
Epilepsy is a condition that affects the brain and causes repeated seizures. This seizure is episodes that can vary and nearly undetectable to long periods of vigorous shaking or brain contractions. Epilepsy often can be confirmed with an electrocephalography (EEG). Neural Networks has been used in biomedic signal analysis, it has successfully classified the biomedic signal, such as EEG signal. In this paper, a hybrid cuckoo search and neural network are used to recognize EEG signal for epilepsy classifications. The weight of the multilayer perceptron is optimized by the cuckoo search algorithm based on its error. The aim of this methods is making the network faster to obtained the local or global optimal then the process of classification become more accurate. Based on the comparison results with the traditional multilayer perceptron, the hybrid cuckoo search and multilayer perceptron provides better performance in term of error convergence and accuracy. The purpose methods give MSE 0.001 and accuracy 90.0 %.
Aphasia Classification Using Neural Networks
DEFF Research Database (Denmark)
Axer, H.; Jantzen, Jan; Berks, G.
2000-01-01
A web-based software model (http://fuzzy.iau.dtu.dk/aphasia.nsf) was developed as an example for classification of aphasia using neural networks. Two multilayer perceptrons were used to classify the type of aphasia (Broca, Wernicke, anomic, global) according to the results in some subtests...
Castet, Jean-Francois; Saleh, Joseph H
2013-01-01
This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the
Directory of Open Access Journals (Sweden)
Jean-Francois Castet
Full Text Available This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also
Artificial neural networks in predicting current in electric arc furnaces
Panoiu, M.; Panoiu, C.; Iordan, A.; Ghiormez, L.
2014-03-01
The paper presents a study of the possibility of using artificial neural networks for the prediction of the current and the voltage of Electric Arc Furnaces. Multi-layer perceptron and radial based functions Artificial Neural Networks implemented in Matlab were used. The study is based on measured data items from an Electric Arc Furnace in an industrial plant in Romania.
Comparing multilayer brain networks between groups: Introducing graph metrics and recommendations.
Mandke, Kanad; Meier, Jil; Brookes, Matthew J; O'Dea, Reuben D; Van Mieghem, Piet; Stam, Cornelis J; Hillebrand, Arjan; Tewarie, Prejaas
2017-11-11
There is an increasing awareness of the advantages of multi-modal neuroimaging. Networks obtained from different modalities are usually treated in isolation, which is however contradictory to accumulating evidence that these networks show non-trivial interdependencies. Even networks obtained from a single modality, such as frequency-band specific functional networks measured from magnetoencephalography (MEG) are often treated independently. Here, we discuss how a multilayer network framework allows for integration of multiple networks into a single network description and how graph metrics can be applied to quantify multilayer network organisation for group comparison. We analyse how well-known biases for single layer networks, such as effects of group differences in link density and/or average connectivity, influence multilayer networks, and we compare four schemes that aim to correct for such biases: the minimum spanning tree (MST), effective graph resistance cost minimisation, efficiency cost optimisation (ECO) and a normalisation scheme based on singular value decomposition (SVD). These schemes can be applied to the layers independently or to the multilayer network as a whole. For correction applied to whole multilayer networks, only the SVD showed sufficient bias correction. For correction applied to individual layers, three schemes (ECO, MST, SVD) could correct for biases. By using generative models as well as empirical MEG and functional magnetic resonance imaging (fMRI) data, we further demonstrated that all schemes were sensitive to identify network topology when the original networks were perturbed. In conclusion, uncorrected multilayer network analysis leads to biases. These biases may differ between centres and studies and could consequently lead to unreproducible results in a similar manner as for single layer networks. We therefore recommend using correction schemes prior to multilayer network analysis for group comparisons. Copyright © 2017 Elsevier
System Identification Using Multilayer Differential Neural Networks: A New Result
Directory of Open Access Journals (Sweden)
J. Humberto Pérez-Cruz
2012-01-01
Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.
Evaluating the impact of interdisciplinary research: a multilayer network approach
Omodei, Elisa; Arenas, Alex
2016-01-01
Nowadays, scientific challenges usually require approaches that cross traditional boundaries between academic disciplines, driving many researchers towards interdisciplinarity. Despite its obvious importance, there is a lack of studies on how to quantify the influence of interdisciplinarity on the research impact, posing uncertainty in a proper evaluation for hiring and funding purposes. Here we propose a method based on the analysis of bipartite interconnected multilayer networks of citations and disciplines, to assess scholars, institutions and countries interdisciplinary importance. Using data about physics publications and US patents, we show that our method allows to reveal, using a quantitative approach, that being more interdisciplinary causes -- in the Granger sense -- benefits in scientific productivity and impact. The proposed method could be used by funding agencies, universities and scientific policy decision makers for hiring and funding purposes, and to complement existing methods to rank univer...
Directory of Open Access Journals (Sweden)
Schwindling Jerome
2010-04-01
Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.
Energy management and multi-layer control of networked microgrids
Zamora, Ramon
Networked microgrids is a group of neighboring microgrids that has ability to interchange power when required in order to increase reliability and resiliency. Networked microgrid can operate in different possible configurations including: islanded microgrid, a grid-connected microgrid without a tie-line converter, a grid-connected microgrid with a tie-line converter, and networked microgrids. These possible configurations and specific characteristics of renewable energy offer challenges in designing control and management algorithms for voltage, frequency and power in all possible operating scenarios. In this work, control algorithm is designed based on large-signal model that enables microgrid to operate in wide range of operating points. A combination between PI controller and feed-forward measured system responses will compensate for the changes in operating points. The control architecture developed in this work has multi-layers and the outer layer is slower than the inner layer in time response. The main responsibility of the designed controls are to regulate voltage magnitude and frequency, as well as output power of the DG(s). These local controls also integrate with a microgrid level energy management system or microgrid central controller (MGCC) for power and energy balance for. the entire microgrid in islanded, grid-connected, or networked microgid mode. The MGCC is responsible to coordinate the lower level controls to have reliable and resilient operation. In case of communication network failure, the decentralized energy management will operate locally and will activate droop control. Simulation results indicate the superiority of designed control algorithms compared to existing ones.
Collaborative multi-layer network coding for cellular cognitive radio networks
Sorour, Sameh
2013-06-01
In this paper, we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in underlay cellular cognitive radio networks. This scheme allows the collocated primary and cognitive radio base-stations to collaborate with each other, in order to minimize their own and each other\\'s packet recovery overheads, and thus improve their throughput, without any coordination between them. This non-coordinated collaboration is done using a novel multi-layer instantly decodable network coding scheme, which guarantees that each network\\'s help to the other network does not result in any degradation in its own performance. It also does not cause any violation to the primary networks interference thresholds in the same and adjacent cells. Yet, our proposed scheme both guarantees the reduction of the recovery overhead in collocated primary and cognitive radio networks, and allows early recovery of their packets compared to non-collaborative schemes. Simulation results show that a recovery overhead reduction of 15% and 40% can be achieved by our proposed scheme in the primary and cognitive radio networks, respectively, compared to the corresponding non-collaborative scheme. © 2013 IEEE.
A multi-layered network of the (Colombian) sovereign securities market
Renneboog, Luc; Leon Rincon, Carlos; Pérez, Jhonatan; Alexandrova-Kabadjova, Bilana; Diehl, Martin; Heuver, Richard; Martinez-Jaramillo, Serafín
2015-01-01
We study the network of Colombian sovereign securities settlements. With data from the settlement market infrastructure we study financial institutions’ transactions from three different trading and registering individual networks that we combine into a multi-layer network. Examining this network of
Astegiano, Julia; Altermatt, Florian; Massol, François
2017-11-13
Species establish different interactions (e.g. antagonistic, mutualistic) with multiple species, forming multilayer ecological networks. Disentangling network co-structure in multilayer networks is crucial to predict how biodiversity loss may affect the persistence of multispecies assemblages. Existing methods to analyse multilayer networks often fail to consider network co-structure. We present a new method to evaluate the modular co-structure of multilayer networks through the assessment of species degree co-distribution and network module composition. We focus on modular structure because of its high prevalence among ecological networks. We apply our method to two Lepidoptera-plant networks, one describing caterpillar-plant herbivory interactions and one representing adult Lepidoptera nectaring on flowers, thereby possibly pollinating them. More than 50% of the species established either herbivory or visitation interactions, but not both. These species were over-represented among plants and lepidopterans, and were present in most modules in both networks. Similarity in module composition between networks was high but not different from random expectations. Our method clearly delineates the importance of interpreting multilayer module composition similarity in the light of the constraints imposed by network structure to predict the potential indirect effects of species loss through interconnected modular networks.
An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.
Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen
2016-01-01
The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.
Vibration Based Damage Assessment of a Civil Engineering Structures using a Neural Networks
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Rytter, A.
In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with the Backpropagation Algorith as a non-destructive damage assessment technique to locate and quantify a damage in Civil Engineering structures is investigated. Since artificial neural networks are proving...
Directory of Open Access Journals (Sweden)
Vânia Medianeira Flores Costa
2012-04-01
Full Text Available When investors decide to “adventure” through stock markets they search for a method to provide safety on making decision. In fact, there is no precise way to know which stocks will became a profitable investiment. Technical analysis is a discipline that support the investors on making decisions. Such a discipline uses a set of tools and statistical methods to forecast the market’s movement. Such a paper presents the develpment of a robotical Trade System, using a heuristic method. The system has a Neural Network multilayer perceptron, trained with an algorithm for back propagation error. Thus, approaching to the technical analysis without emotional aspects, using the Neural Network forecast on supporting the decisions of a investor on stock market. In analyzing the results of the neural network can be seen that the neural network got a result of 42.6% higher than the diagnostic of the technical analysis.Quando investidores decidem se “aventurar” pelo mercado de renda variável, como pelo mercado de ações, buscam um método de ter mais segurança na tomada de decisão. Na prática, não há como saber quais ativos tornar-se-ão um investimento lucrativo. No mercado acionário, a Análise Técnica procura auxiliar o investidor na tomada de decisão. Para isso, utiliza-se de ferramentas e de métodos estatísticos para tentar predizer os movimentos do mercado. Este artigo apresenta o desenvolvimento de um Trade System robótico, utilizando um método heurístico. O sistema conta com uma rede neural multilayer perceptron, treinada com o algoritmo de retro propagação de erro, aproximando-se da análise técnica sem o fator emoção. Ao avaliar os resultados da rede neural, pode ser visto que a mesma obteve um resultado de 42,6% maior do que o diagnóstico da análise técnica.
Collaborative Multi-Layer Network Coding in Hybrid Cellular Cognitive Radio Networks
Moubayed, Abdallah J.
2015-05-01
In this paper, as an extension to [1], we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated collaboration between the collocated primary and cognitive radio base-stations in order to minimize their own as well as each other\\'s packet recovery overheads, thus by improving their throughput. The proposed scheme ensures that each network\\'s performance is not degraded by its help to the other network. Moreover, it guarantees that the primary network\\'s interference threshold is not violated in the same and adjacent cells. Yet, the scheme allows the reduction of the recovery overhead in the collocated primary and cognitive radio networks. The reduction in the cognitive radio network is further amplified due to the perfect detection of spectrum holes which allows the cognitive radio base station to transmit at higher power without fear of violating the interference threshold of the primary network. For the secondary network, simulation results show reductions of 20% and 34% in the packet recovery overhead, compared to the non-collaborative scheme, for low and high probabilities of primary packet arrivals, respectively. For the primary network, this reduction was found to be 12%. © 2015 IEEE.
Can we neglect the multi-layer structure of functional networks?
Zanin, Massimiliano
2015-07-01
Functional networks, i.e. networks representing dynamic relationships between the components of a complex system, have been instrumental for our understanding of, among others, the human brain. Due to limited data availability, the multi-layer nature of numerous functional networks has hitherto been neglected, and nodes are endowed with a single type of links even when multiple relationships coexist at different physical levels. A relevant problem is the assessment of the benefits yielded by studying a multi-layer functional network, against the simplicity guaranteed by the reconstruction and use of the corresponding single layer projection. Here, I tackle this issue by using as a test case, the functional network representing the dynamics of delay propagation through European airports. Neglecting the multi-layer structure of a functional network has dramatic consequences on our understanding of the underlying system, a fact to be taken into account when a projection is the only available information.
Yasami, Yasser; Safaei, Farshad
2018-02-01
The traditional complex network theory is particularly focused on network models in which all network constituents are dealt with equivalently, while fail to consider the supplementary information related to the dynamic properties of the network interactions. This is a main constraint leading to incorrect descriptions of some real-world phenomena or incomplete capturing the details of certain real-life problems. To cope with the problem, this paper addresses the multilayer aspects of dynamic complex networks by analyzing the properties of intrinsically multilayered co-authorship networks, DBLP and Astro Physics, and presenting a novel multilayer model of dynamic complex networks. The model examines the layers evolution (layers birth/death process and lifetime) throughout the network evolution. Particularly, this paper models the evolution of each node's membership in different layers by an Infinite Factorial Hidden Markov Model considering feature cascade, and thereby formulates the link generation process for intra-layer and inter-layer links. Although adjacency matrixes are useful to describe the traditional single-layer networks, such a representation is not sufficient to describe and analyze the multilayer dynamic networks. This paper also extends a generalized mathematical infrastructure to address the problems issued by multilayer complex networks. The model inference is performed using some Markov Chain Monte Carlo sampling strategies, given synthetic and real complex networks data. Experimental results indicate a tremendous improvement in the performance of the proposed multilayer model in terms of sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, F1-score, Matthews correlation coefficient, and accuracy for two important applications of missing link prediction and future link forecasting. The experimental results also indicate the strong predictivepower of the proposed model for the application of
Multilayer network of language: A unified framework for structural analysis of linguistic subsystems
Martinčić-Ipšić, Sanda; Margan, Domagoj; Meštrović, Ana
2016-09-01
Recently, the focus of complex networks' research has shifted from the analysis of isolated properties of a system toward a more realistic modeling of multiple phenomena - multilayer networks. Motivated by the prosperity of multilayer approach in social, transport or trade systems, we introduce the multilayer networks for language. The multilayer network of language is a unified framework for modeling linguistic subsystems and their structural properties enabling the exploration of their mutual interactions. Various aspects of natural language systems can be represented as complex networks, whose vertices depict linguistic units, while links model their relations. The multilayer network of language is defined by three aspects: the network construction principle, the linguistic subsystem and the language of interest. More precisely, we construct a word-level (syntax and co-occurrence) and a subword-level (syllables and graphemes) network layers, from four variations of original text (in the modeled language). The analysis and comparison of layers at the word and subword-levels are employed in order to determine the mechanism of the structural influences between linguistic units and subsystems. The obtained results suggest that there are substantial differences between the networks' structures of different language subsystems, which are hidden during the exploration of an isolated layer. The word-level layers share structural properties regardless of the language (e.g. Croatian or English), while the syllabic subword-level expresses more language dependent structural properties. The preserved weighted overlap quantifies the similarity of word-level layers in weighted and directed networks. Moreover, the analysis of motifs reveals a close topological structure of the syntactic and syllabic layers for both languages. The findings corroborate that the multilayer network framework is a powerful, consistent and systematic approach to model several linguistic subsystems
Collaborative Multi-Layer Network Coding For Hybrid Cellular Cognitive Radio Networks
Moubayed, Abdallah J.
2014-05-01
In this thesis, as an extension to [1], we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated collaboration between the collocated primary and cognitive radio base-stations in order to minimize their own as well as each other’s packet recovery overheads, thus by improving their throughput. The proposed scheme ensures that each network’s performance is not degraded by its help to the other network. Moreover, it guarantees that the primary network’s interference threshold is not violated in the same and adjacent cells. Yet, the scheme allows the reduction of the recovery overhead in the collocated primary and cognitive radio networks. The reduction in the cognitive radio network is further amplified due to the perfect detection of spectrum holes which allows the cognitive radio base station to transmit at higher power without fear of violating the interference threshold of the primary network. For the secondary network, simulation results show reductions of 20% and 34% in the packet recovery overhead, compared to the non-collaborative scheme, for low and high probabilities of primary packet arrivals, respectively. For the primary network, this reduction was found to be 12%. Furthermore, with the use of fractional cooperation, the average recovery overhead is further reduced by around 5% for the primary network and around 10% for the secondary network when a high fractional cooperation probability is used.
Use of a Neural Network for Damage Detection and Location in a Steel Member
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Rytter, A.
The paper explores the potential of using a Multilayer Perceptron (MLP) network trained with the Backpropagation algorithm for damage assessment of free-free cracked straight steel beam based on vibration measurements. The problem of damage assessment, i.e. detecting, locating and quantifying a d...
A multilayer network dataset of interaction and influence spreading in a virtual world
Jankowski, Jarosław; Michalski, Radosław; Bródka, Piotr
2017-10-01
Presented data contains the record of five spreading campaigns that occurred in a virtual world platform. Users distributed avatars between each other during the campaigns. The processes varied in time and range and were either incentivized or not incentivized. Campaign data is accompanied by events. The data can be used to build a multilayer network to place the campaigns in a wider context. To the best of the authors' knowledge, the study is the first publicly available dataset containing a complete real multilayer social network together, along with five complete spreading processes in it.
Collaborative-Hybrid Multi-Layer Network Control for Emerging Cyber-Infrastructures
Energy Technology Data Exchange (ETDEWEB)
Lehman, Tom [USC; Ghani, Nasir [UNM; Boyd, Eric [UCAID
2010-08-31
At a high level, there were four basic task areas identified for the Hybrid-MLN project. They are: o Multi-Layer, Multi-Domain, Control Plane Architecture and Implementation, including OSCARS layer2 and InterDomain Adaptation, Integration of LambdaStation and Terapaths with Layer2 dynamic provisioning, Control plane software release, Scheduling, AAA, security architecture, Network Virtualization architecture, Multi-Layer Network Architecture Framework Definition; o Heterogeneous DataPlane Testing; o Simulation; o Project Publications, Reports, and Presentations.
A multilayer network dataset of interaction and influence spreading in a virtual world.
Jankowski, Jarosław; Michalski, Radosław; Bródka, Piotr
2017-10-10
Presented data contains the record of five spreading campaigns that occurred in a virtual world platform. Users distributed avatars between each other during the campaigns. The processes varied in time and range and were either incentivized or not incentivized. Campaign data is accompanied by events. The data can be used to build a multilayer network to place the campaigns in a wider context. To the best of the authors' knowledge, the study is the first publicly available dataset containing a complete real multilayer social network together, along with five complete spreading processes in it.
Path finding using the multi-layer network description language
Dijkstra, F.; van der Ham, J.; Grosso, P.; de Laat, C.
2008-01-01
Research networks are providing their users with dedicated network connections. Users want these connects to go through different domains, so the research networks are challenged to find valid network connections through multiple domains. The challenge arises if the different network domains use
A Novel Single Neuron Perceptron with Universal Approximation and XOR Computation Properties
Directory of Open Access Journals (Sweden)
Ehsan Lotfi
2014-01-01
Full Text Available We propose a biologically motivated brain-inspired single neuron perceptron (SNP with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain’s nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP with gradient decent backpropagation (GDBP learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.
Multi-layer service function chaining scheduling based on auxiliary graph in IP over optical network
Li, Yixuan; Li, Hui; Liu, Yuze; Ji, Yuefeng
2017-10-01
Software Defined Optical Network (SDON) can be considered as extension of Software Defined Network (SDN) in optical networks. SDON offers a unified control plane and makes optical network an intelligent transport network with dynamic flexibility and service adaptability. For this reason, a comprehensive optical transmission service, able to achieve service differentiation all the way down to the optical transport layer, can be provided to service function chaining (SFC). IP over optical network, as a promising networking architecture to interconnect data centers, is the most widely used scenarios of SFC. In this paper, we offer a flexible and dynamic resource allocation method for diverse SFC service requests in the IP over optical network. To do so, we firstly propose the concept of optical service function (OSF) and a multi-layer SFC model. OSF represents the comprehensive optical transmission service (e.g., multicast, low latency, quality of service, etc.), which can be achieved in multi-layer SFC model. OSF can also be considered as a special SF. Secondly, we design a resource allocation algorithm, which we call OSF-oriented optical service scheduling algorithm. It is able to address multi-layer SFC optical service scheduling and provide comprehensive optical transmission service, while meeting multiple optical transmission requirements (e.g., bandwidth, latency, availability). Moreover, the algorithm exploits the concept of Auxiliary Graph. Finally, we compare our algorithm with the Baseline algorithm in simulation. And simulation results show that our algorithm achieves superior performance than Baseline algorithm in low traffic load condition.
Discrete Orthogonal Transforms and Neural Networks for Image Interpolation
Directory of Open Access Journals (Sweden)
J. Polec
1999-09-01
Full Text Available In this contribution we present transform and neural network approaches to the interpolation of images. From transform point of view, the principles from [1] are modified for 1st and 2nd order interpolation. We present several new interpolation discrete orthogonal transforms. From neural network point of view, we present interpolation possibilities of multilayer perceptrons. We use various configurations of neural networks for 1st and 2nd order interpolation. The results are compared by means of tables.
Multi-Layer Mobility Load Balancing in a Heterogeneous LTE Network
DEFF Research Database (Denmark)
Fotiadis, Panagiotis; Polignano, Michele; Laselva, Daniela
2012-01-01
This paper analyzes the behavior of a distributed Mobility Load Balancing (MLB) scheme in a multi-layer 3GPP (3rd Generation Partnership Project) Long Term Evolution (LTE) deployment with different User Equipment (UE) densities in certain network areas covered with pico cells. Target of the study...
Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems
Boccaletti, Stefano; Criado, Regino; Romance, Miguel; Torres, Joaquín J.
2016-06-01
In the last years, network scientists have directed their interest to the multi-layer character of real-world systems, and explicitly considered the structural and dynamical organization of graphs made of diverse layers between its constituents. Most complex systems include multiple subsystems and layers of connectivity and, in many cases, the interdependent components of systems interact through many different channels. Such a new perspective is indeed found to be the adequate representation for a wealth of features exhibited by networked systems in the real world. The contributions presented in this Focus Issue cover, from different points of view, the many achievements and still open questions in the field of multi-layer networks, such as: new frameworks and structures to represent and analyze heterogeneous complex systems, different aspects related to synchronization and centrality of complex networks, interplay between layers, and applications to logistic, biological, social, and technological fields.
Master stability functions reveal diffusion-driven instabilities in multi-layer networks
Brechtel, Andreas; Ritterskamp, Daniel; Drossel, Barbara; Gross, Thilo
2016-01-01
Many systems in science and technology can be described as multilayer networks, which are known to exhibit phenomena such as catastrophic failure cascades and pattern-forming instabilities. A particular class of multilayer networks describes systems where different interacting copies of a local network exist in different spatial locations, including for instance regulatory and metabolic networks of identical cells and interacting habitats of ecological populations. Here, we show that such systems can be analyzed by a master stability function (MSF) approach, which reveals conditions for diffusion-driven instabilities (DDIs). We demonstrate the methodology on the example of state-of-the-art meta-foodweb models, where it reveals diffusion-driven instabilities that lead to localized dynamics and spatial patterns. This type of approach can be applied to a variety of systems from nature, science and engineering to aid the understanding and design of complex self-organizing systems.
Energy Technology Data Exchange (ETDEWEB)
Vokkarane, Vinod [University of Massachusetts
2013-09-01
We intend to implement a Coordinated Multi-layer Multi-domain Optical Network (COMMON) Framework for Large-scale Science Applications. In the COMMON project, specific problems to be addressed include 1) anycast/multicast/manycast request provisioning, 2) deployable OSCARS enhancements, 3) multi-layer, multi-domain quality of service (QoS), and 4) multi-layer, multidomain path survivability. In what follows, we outline the progress in the above categories (Year 1, 2, and 3 deliverables).
Reference Architecture for Multi-Layer Software Defined Optical Data Center Networks
Directory of Open Access Journals (Sweden)
Casimer DeCusatis
2015-09-01
Full Text Available As cloud computing data centers grow larger and networking devices proliferate; many complex issues arise in the network management architecture. We propose a framework for multi-layer; multi-vendor optical network management using open standards-based software defined networking (SDN. Experimental results are demonstrated in a test bed consisting of three data centers interconnected by a 125 km metropolitan area network; running OpenStack with KVM and VMW are components. Use cases include inter-data center connectivity via a packet-optical metropolitan area network; intra-data center connectivity using an optical mesh network; and SDN coordination of networking equipment within and between multiple data centers. We create and demonstrate original software to implement virtual network slicing and affinity policy-as-a-service offerings. Enhancements to synchronous storage backup; cloud exchanges; and Fibre Channel over Ethernet topologies are also discussed.
A Deterministic and Polynomial Modified Perceptron Algorithm
Directory of Open Access Journals (Sweden)
Olof Barr
2006-01-01
Full Text Available We construct a modified perceptron algorithm that is deterministic, polynomial and also as fast as previous known algorithms. The algorithm runs in time O(mn3lognlog(1/ρ, where m is the number of examples, n the number of dimensions and ρ is approximately the size of the margin. We also construct a non-deterministic modified perceptron algorithm running in timeO(mn2lognlog(1/ρ.
Towards effective visual analytics on multiplex and multilayer networks
DEFF Research Database (Denmark)
Rossi, Luca; Magnani, Matteo
2015-01-01
In this article we discuss visualisation strategies for multiplex networks. Since Moreno’s early works on network analysis, visualisation has been one of the main ways to understand networks thanks to its ability to summarise a complex structure into a single representation highlighting multiple ...
A path finding implementation for multi-layer network
Dijkstra, F.; Ham, J.J. van der; Grosso, P.; Laat, C. de
2009-01-01
The goal of the OptIPuter project is to tightly couple research applications with dynamically allocated paths. Since OptIPuter is a multi-disciplinary project, the paths through the network often span multiple network domains, and the applications are challenged to find valid network connections
A path finding implementation for multi-layer networks
Dijkstra, F.; van der Ham, J.; Grosso, P.; de Laat, C.
2009-01-01
The goal of the OptIPuter project is to tightly couple research applications with dynamically allocated paths. Since OptIPuter is a multi-disciplinary project, the paths through the network often span multiple network domains, and the applications are challenged to find valid network connections
Multi-layer architecture for realization of network virtualization using MPLS technology
Directory of Open Access Journals (Sweden)
Ghasem Ahmadeyan Mazhin
2017-03-01
Full Text Available Network virtualization (NV increases Internet flexibility by separating policies from mechanisms. This makes developing new applications, managing the Internet, and supporting different applications much easier. In this study, we introduce a multi-layer architecture which combines multi-level multiprotocol label switching (MPLS technology with NV. The proposed architecture combines the high speed advantage of MPLS with the high flexibility of NV. We use MPLS in MPLS technique and encapsulate each MPLS packet within another when it encounters a new virtual network. Our architecture has the potential to improve Internet flexibility and pave the way for deployment and commercialization of NV in next generation networks.
Pareto distance for multi-layer network analysis
DEFF Research Database (Denmark)
Magnani, Matteo; Rossi, Luca
2013-01-01
new research problems related to the extension of traditional single-layer network measures. In this paper we take a step forward over existing approaches by defining a new concept of geodesic distance that includes heterogeneous networks and connections with very limited assumptions regarding...... the strength of the connections. This is achieved by exploiting the concept of Pareto efficiency to define a simple and at the same time powerful measure that we call Pareto distance, of which geodesic distance is a particular case when a single layer (or network) is analyzed. The limited assumptions...
The value of peripheral nodes in controlling multilayer networks
Zhang, Yan; Schweitzer, Frank
2015-01-01
We analyze the controllability of a two-layer network, where driver nodes can be chosen only from one layer. Each layer contains a scale-free network with directed links. The dynamics of nodes depends on the incoming links from other nodes (reputation dynamics). We find that the controllable part of the network is larger when choosing peripherial nodes to connect the two layers. The control is as efficient for peripherial nodes as driver nodes as it is for more central nodes. If we assume a cost to utilize nodes which is proportional to their degree, utilizing peripherial nodes to connect the two layers or to act as driver nodes is not only the most cost-efficient solution, it is also the one that gives us the best performance in controlling the two-layer network.
Person Movement Prediction Using Neural Networks
Vintan, Lucian; Gellert, Arpad; Petzold, Jan; Ungerer, Theo
2006-01-01
Ubiquitous systems use context information to adapt appliance behavior to human needs. Even more convenience is reached if the appliance foresees the user's desires and acts proactively. This paper proposes neural prediction techniques to anticipate a person's next movement. We focus on neural predictors (multi-layer perceptron with back-propagation learning) with and without pre-training. The optimal configuration of the neural network is determined by evaluating movement sequences of real p...
Directory of Open Access Journals (Sweden)
OMER MAHMOUD
2007-08-01
Full Text Available One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.
Multilayer Optimization of Heterogeneous Networks Using Grammatical Genetic Programming.
Fenton, Michael; Lynch, David; Kucera, Stepan; Claussen, Holger; O'Neill, Michael
2017-09-01
Heterogeneous cellular networks are composed of macro cells (MCs) and small cells (SCs) in which all cells occupy the same bandwidth. Provision has been made under the third generation partnership project-long term evolution framework for enhanced intercell interference coordination (eICIC) between cell tiers. Expanding on previous works, this paper instruments grammatical genetic programming to evolve control heuristics for heterogeneous networks. Three aspects of the eICIC framework are addressed including setting SC powers and selection biases, MC duty cycles, and scheduling of user equipments (UEs) at SCs. The evolved heuristics yield minimum downlink rates three times higher than a baseline method, and twice that of a state-of-the-art benchmark. Furthermore, a greater number of UEs receive transmissions under the proposed scheme than in either the baseline or benchmark cases.
Partial Information Community Detection in a Multilayer Network
2016-06-01
6 Figure 2.2 Visual representation of the Internet . . . . . . . . . . . . . . . . 6 Figure 2.3 Isomorphic...represent practically everything from the Internet to a human brain. Networks can be as simple or complex as one desires. We can also model terrorist...pay, as Apple said. That is, since once 1 such technology was invented , there is always a way for it to find its way into the wrong hands and be
Multilayer recovery mechanism for IP over WDM networks
Liu, Xin; Zeng, Qingji; Wang, Yun
2004-04-01
In this article, we mainly focus on the integrated dynamic survivability in IP/MPLS/WDM networks under the peer-modeled architecture. We systematically investigated various integrated dynamic protection and restoration schemes combining knowledge of resource and topology information in both the IP/MPLS and WDM layers. To allocate the restorable bandwidth-guaranteed paths, we propose an integrated routing algorithm: Load-Balanced Physical Resource Minimum Hop (LBPRMH) integrated routing algorithm. In this algorithm, we present a new link cost function used in Dijkstra"s algorithm considering both physical resource and load balancing factors and employ a parameter (relevant to traffic intensity) to control the influence of two kind of information. When network load is low (traffic intensity is small), the main factor in the proposed link cost function is physical resource, which leads to high resource utilization. When network load is high, the idea of load balance becomes more important in routing, which avoids connection requests conflict and increases the efficiency of resource usage. Then extensive simulations are done to analyze and compare the dynamic survival performance of various integrated survival strategies. Resource utilization, protection efficiency, restoration efficiency are employed as metrics to evaluate the survivable performance. We demonstrate that the proposed integrated routing algorithm performs better than some existing approaches in terms of the above three metrics. The simulation results show the high resource utilization in optical layer recovery with wavelength granularity and the high restoration flexibility in IP/MPLS layer recovery with finer granularity. If the recovery action is implemented first in lower layer and then higher layer if the first action failed, the integrated protection and restoration schemes can combine the advantages of both layers and perform better than recovery schemes in either single layer.
Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Petr Maca
2016-01-01
Full Text Available The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI and the standardized precipitation evaporation index (SPEI and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.
Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI
Olyaee, Saeed; Hamedi, Samaneh
2011-02-01
In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.
ML-IKE: a multi-layer IKE protocol for TCP performance enhancement in wireless networks
Zhang, Ya-Hang; Cheng, Bo-Wen; Qing, Si-Han; Zou, Guang-Nan; Wen, Wei-Ping
2009-12-01
To solve the conflict between TCP accelerating technology based on PEP middle node and IPSec protocol used in the Satellite Network, NASA and the Hughes Research Laboratory (HRL) each independently proposed a solution named Multilayer IPsec protocol which can integrate IPSec with TCP PEPs. The problem is: Traditional IKE protocol can't work with Multilayer IPSec protocol. In this study, the traditional IKE main mode and quick mode are enhanced for layered IPSec protocol, and an improved layered key distribution protocol: ML-IKE is proposed. This key distribution protocol is used for key exchange between peers and middle node, so that different nodes have different security associations (SA), and different security associations correspond to different IP packet fields, so different SA nodes have different authorization to different IP packet fields. ML-IKE protocol is suitable for layered IPSec, thus layered IPSec can be used for automatic key distribution and update.
Multilayer PV-storage Microgrids Algorithm for the Dispatch of Distributed Network
Directory of Open Access Journals (Sweden)
Yang Ping
2016-01-01
Full Text Available In recent years, due to the support of our country, PV-storage microgrid develops rapidly. However, the flexible network operation modes of PV-storage microgrid change flexibly and the operating characteristics with a large amout of sources is highly complicated. Based on the existing microgrid coordinate control methods, this paper proposes multilayer PV-storage microgrid algorithm for fitting dispatch of distributed network, which achieves maximum output of renewable energy when meeting the scheduling requirements of network, by building PV-storage microgrid type dynamic simulation system in a variety of conditions in PSCAD. Simulation results show that the heuristic algorithm proposed can achieve microgrid stable operation and satisfy the demands of the dispatch in distributed network.
Artificial neural networks (ANN): prediction of sensory measurements from instrumental data
Carvalho,Naiara Barbosa; Minim,Valéria Paula Rodrigues; Silva,Rita de Cássia dos Santos Navarro; Della Lucia,Suzana Maria; Minim,Luis Aantonio
2013-01-01
The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combination...
Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.
Chen, Chen; Tong, Hanghang; Xie, Lei; Ying, Lei; He, Qing
2017-08-01
The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm Fascinate that can reveal unobserved dependencies with linear complexity. Moreover, we derive Fascinate-ZERO, an online variant of Fascinate that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.
Noise Reduction Technique for Images using Radial Basis Function Neural Networks
Directory of Open Access Journals (Sweden)
Sander Ali Khowaja
2014-07-01
Full Text Available This paper presents a NN (Neural Network based model for reducing the noise from images. This is a RBF (Radial Basis Function network which is used to reduce the effect of noise and blurring from the captured images. The proposed network calculates the mean MSE (Mean Square Error and PSNR (Peak Signal to Noise Ratio of the noisy images. The proposed network has also been successfully applied to medical images. The performance of the trained RBF network has been compared with the MLP (Multilayer Perceptron Network and it has been demonstrated that the performance of the RBF network is better than the MLP network.
Prediction of littoral drift with artificial neural networks
Digital Repository Service at National Institute of Oceanography (India)
Singh, A.K.; Deo, M.C.; SanilKumar, V.
, arbitrary accuracy, and difficult choices related to train- ing schemes, architectures, learning algorithms, and control parameters. Any new application of the ANN that addresses these issues therefore deserves attention of the potential user community...). The current study was also based on the same. Both multi-layered perceptron network (MLP) as well as its variant radial basis function (RBF) was used. Training of the MLP was achieved with the help of alternative learn- ing schemes like Conjugate Gradient...
Stacked Heterogeneous Neural Networks for Time Series Forecasting
Directory of Open Access Journals (Sweden)
Florin Leon
2010-01-01
Full Text Available A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.
Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability.
Dawson, Michael R W; Gupta, Maya
2017-01-01
Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.
Chimera states in a multilayer network of coupled and uncoupled neurons
Majhi, Soumen; Perc, Matjaž; Ghosh, Dibakar
2017-07-01
We study the emergence of chimera states in a multilayer neuronal network, where one layer is composed of coupled and the other layer of uncoupled neurons. Through the multilayer structure, the layer with coupled neurons acts as the medium by means of which neurons in the uncoupled layer share information in spite of the absence of physical connections among them. Neurons in the coupled layer are connected with electrical synapses, while across the two layers, neurons are connected through chemical synapses. In both layers, the dynamics of each neuron is described by the Hindmarsh-Rose square wave bursting dynamics. We show that the presence of two different types of connecting synapses within and between the two layers, together with the multilayer network structure, plays a key role in the emergence of between-layer synchronous chimera states and patterns of synchronous clusters. In particular, we find that these chimera states can emerge in the coupled layer regardless of the range of electrical synapses. Even in all-to-all and nearest-neighbor coupling within the coupled layer, we observe qualitatively identical between-layer chimera states. Moreover, we show that the role of information transmission delay between the two layers must not be neglected, and we obtain precise parameter bounds at which chimera states can be observed. The expansion of the chimera region and annihilation of cluster and fully coherent states in the parameter plane for increasing values of inter-layer chemical synaptic time delay are illustrated using effective range measurements. These results are discussed in the light of neuronal evolution, where the coexistence of coherent and incoherent dynamics during the developmental stage is particularly likely.
Directory of Open Access Journals (Sweden)
Eduardo O. de Cerqueira
2001-12-01
Full Text Available Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the knowledge acquisition of the human brain. In last years its application in chemistry is increasing significantly, due the special characteristics for model complex systems. The basic principles of two types of neural networks, the multi-layer perceptrons and radial basis functions, are introduced, as well as, a pruning approach to architecture optimization. Two analytical applications based on near infrared spectroscopy are presented, the first one for determination of nitrogen content in wheat leaves using multi-layer perceptrons networks and second one for determination of BRIX in sugar cane juices using radial basis functions networks.
Shakeri, Heman; Sahneh, Faryad Darabi; Scoglio, Caterina; Poggi-Corradini, Pietro; Preciado, Victor M
2015-06-01
Launching a prevention campaign to contain the spread of infection requires substantial financial investments; therefore, a trade-off exists between suppressing the epidemic and containing costs. Information exchange among individuals can occur as physical contacts (e.g., word of mouth, gatherings), which provide inherent possibilities of disease transmission, and non-physical contacts (e.g., email, social networks), through which information can be transmitted but the infection cannot be transmitted. Contact network (CN) incorporates physical contacts, and the information dissemination network (IDN) represents non-physical contacts, thereby generating a multilayer network structure. Inherent differences between these two layers cause alerting through CN to be more effective but more expensive than IDN. The constraint for an epidemic to die out derived from a nonlinear Perron-Frobenius problem that was transformed into a semi-definite matrix inequality and served as a constraint for a convex optimization problem. This method guarantees a dying-out epidemic by choosing the best nodes for adopting preventive behaviors with minimum monetary resources. Various numerical simulations with network models and a real-world social network validate our method.
Shinde, Pramod; Jalan, Sarika
2015-12-01
Molecular networks act as the backbone of cellular activities, providing an excellent opportunity to understand the developmental changes in an organism. While network data usually constitute only stationary network graphs, constructing a multilayer PPI network may provide clues to the particular developmental role at each stage of life and may unravel the importance of these developmental changes. The developmental biology model of Caenorhabditis elegans analyzed here provides a ripe platform to understand the patterns of evolution during the life stages of an organism. In the present study, the widely studied network properties exhibit overall similar statistics for all the PPI layers. Further, the analysis of the degree-degree correlation and spectral properties not only reveals crucial differences in each PPI layer but also indicates the presence of the varying complexity among them. The PPI layer of the nematode life stage exhibits various network properties different to the rest of the PPI layers, indicating the specific role of cellular diversity and developmental transitions at this stage. The framework presented here provides a direction to explore and understand the developmental changes occurring in the different life stages of an organism.
Modeling Liner Shipping Service Selection and Container Flows using a Multi-layer Network
DEFF Research Database (Denmark)
Karsten, Christian Vad; Balakrishnan, Anant
We introduce a new formulation for the tactical planning problem facing container shipping companies of selecting the best subset of sailing routes from a given pool of candidate routes so as to maximize profit. Since most containers are sent directly or transshipped at most twice in current liner...... shipping networks, we impose limits on the number of transshipments for each container (which most previous models do not incorporate). Our multi-layer multi-commodity model associates one commodity with each container origin port, and decides the route for each commodity on a logical network layer whose...... arcs represent segments (pairs of ports between which a container can use a single service). This approach, combined with commodity flow variables that are indexed by segment sequence permits us to incorporate the transshipment limits while also tracking the commodity’s outflow from the system...
Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks
Directory of Open Access Journals (Sweden)
Dane Taylor
2017-09-01
Full Text Available Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős–Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection and which occur for a given community provided its size surpasses a detectability limit K^{*}. When layers are aggregated via a summation, we obtain K^{*}∝O(sqrt[NL]/T, where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L, then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than O(L^{-1/2}. Moreover, we find that thresholding the summation can, in some cases, cause K^{*} to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.
The Use of Artificial Neural Network for Prediction of Dissolution Kinetics
Directory of Open Access Journals (Sweden)
H. Elçiçek
2014-01-01
Full Text Available Colemanite is a preferred boron mineral in industry, such as boric acid production, fabrication of heat resistant glass, and cleaning agents. Dissolution of the mineral is one of the most important processes for these industries. In this study, dissolution of colemanite was examined in water saturated with carbon dioxide solutions. Also, prediction of dissolution rate was determined using artificial neural networks (ANNs which are based on the multilayered perceptron. Reaction temperature, total pressure, stirring speed, solid/liquid ratio, particle size, and reaction time were selected as input parameters to predict the dissolution rate. Experimental dataset was used to train multilayer perceptron (MLP networks to allow for prediction of dissolution kinetics. Developing ANNs has provided highly accurate predictions in comparison with an obtained mathematical model used through regression method. We conclude that ANNs may be a preferred alternative approach instead of conventional statistical methods for prediction of boron minerals.
Unified Multi-Layer among Software Defined Multi-Domain Optical Networks (Invited
Directory of Open Access Journals (Sweden)
Hui Yang
2015-06-01
Full Text Available The software defined networking (SDN enabled by OpenFlow protocol has gained popularity which can enable the network to be programmable and accommodate both fixed and flexible bandwidth services. In this paper, we present a unified multi-layer (UML architecture with multiple controllers and a dynamic orchestra plane (DOP for software defined multi-domain optical networks. The proposed architecture can shield the differences among various optical devices from multi-vendors and the details of connecting heterogeneous networks. The cross-domain services with on-demand bandwidth can be deployed via unified interfaces provided by the dynamic orchestra plane. Additionally, the globalization strategy and practical capture of signal processing are presented based on the architecture. The overall feasibility and efficiency of the proposed architecture is experimentally verified on the control plane of our OpenFlow-based testbed. The performance of globalization strategy under heavy traffic load scenario is also quantitatively evaluated based on UML architecture compared with other strategies in terms of blocking probability, average hops, and average resource consumption.
Artificial vision by multi-layered neural networks: neocognitron and its advances.
Fukushima, Kunihiko
2013-01-01
The neocognitron is a neural network model proposed by Fukushima (1980). Its architecture was suggested by neurophysiological findings on the visual systems of mammals. It is a hierarchical multi-layered network. It acquires the ability to robustly recognize visual patterns through learning. Although the neocognitron has a long history, modifications of the network to improve its performance are still going on. For example, a recent neocognitron uses a new learning rule, named add-if-silent, which makes the learning process much simpler and more stable. Nevertheless, a high recognition rate can be kept with a smaller scale of the network. Referring to the history of the neocognitron, this paper discusses recent advances in the neocognitron. We also show that various new functions can be realized by, for example, introducing top-down connections to the neocognitron: mechanism of selective attention, recognition and completion of partly occluded patterns, restoring occluded contours, and so on. Copyright © 2012 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Md. Abdullah-al-mamun
2015-08-01
Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.
Understanding cancer complexome using networks, spectral graph theory and multilayer framework
Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K.; Chowdhury, Rajdeep; Jalan, Sarika
2017-02-01
Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.
Understanding cancer complexome using networks, spectral graph theory and multilayer framework.
Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K; Chowdhury, Rajdeep; Jalan, Sarika
2017-02-03
Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.
Gholamreza Asadollahfardi; Azadeh Hemati; Saber Moradinejad; Rashin Asadollahfardi
2013-01-01
Considering the significance of the Sodium Adsorption Ratio (SAR) for growing plants, its prediction is essential for water quality management for irrigation. The SAR prediction in Chelghazy River in Kurdistan, northwest of Iran, using an Artificial Neural Network (ANN) was studied. The study applied the Multilayer Perceptron (MLP) of the ANN to average monthly data, which was collected by the water authority of the Kurdistan province for the period of 1998-2009. The input parameters of the M...
Neural Network on Photodegradation of Octylphenol using Natural and Artificial UV Radiation
Jäntschi, Lorentz; Letiţia OPREAN; Dana - Melania POPA
2011-01-01
The present paper comes up with an experimental design meant to point out the factors interferingin octylphenol’s degradation in surface waters under solar radiation, underlining each factor’sinfluence on the process observable (concentration of p-octylphenol). Multiple linear regressionanalysis and artificial neural network (Multi-Layer Perceptron type) were applied in order to obtaina mathematical model capable to explain the action of UV-light upon synthetic solutions of OP inultra-pure wa...
A numerical modeling of nonlinear load behavior using artificial neural networks
Panoiu, Manuela; Ghiormez, Loredana; Panoiu, Caius; Iordan, Anca
2013-10-01
In this paper it is performed a numerical study of the voltage-current characteristic of an electric arc. To predict voltages and currents values, a multi-layer perceptron Artificial Neural Networks was used under the Matlab 2012 environment. The study is based on actual recorded data obtained from a 100 tones AC Electric Arc Furnace. Results obtained by simulation are compared with the measured one.
Dong, Zhekang; Duan, Shukai; Hu, Xiaofang; Wang, Lidan
2014-01-01
In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme. PMID:25202723
Directory of Open Access Journals (Sweden)
Zhekang Dong
2014-01-01
Full Text Available In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.
A generalized voter model with time-decaying memory on a multilayer network
Zhong, Li-Xin; Xu, Wen-Juan; Chen, Rong-Da; Zhong, Chen-Yang; Qiu, Tian; Shi, Yong-Dong; Wang, Li-Liang
2016-09-01
By incorporating a multilayer network and time-decaying memory into the original voter model, we investigate the coupled effects of spatial and temporal accumulation of peer pressure on the consensus. Heterogeneity in peer pressure and the time-decaying mechanism are both shown to be detrimental to the consensus. We find the transition points below which a consensus can always be reached and above which two opposed opinions are more likely to coexist. Our mean-field analysis indicates that the phase transitions in the present model are governed by the cumulative influence of peer pressure and the updating threshold. We find a functional relation between the consensus threshold and the decay rate of the influence of peer is found. As to the pressure. The time required to reach a consensus is governed by the coupling of the memory length and the decay rate. An intermediate decay rate may greatly reduce the time required to reach a consensus.
Artificial neural network in cosmic landscape
Liu, Junyu
2017-12-01
In this paper we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal approximation theorem of the multilayer perceptron. A toy model in inflation with multiple light fields is investigated numerically as an example of such an application.
A Multilayer Improved RBM Network Based Image Compression Method in Wireless Sensor Networks
National Research Council Canada - National Science Library
Cheng, Chunling; Wang, Shu; Chen, Xingguo; Yang, Yanying
2016-01-01
The processing capacity and power of nodes in a Wireless Sensor Network (WSN) are limited. And most image compression algorithms in WSN are subject to random image content changes or have low image qualities after the images are decoded...
Digital Repository Service at National Institute of Oceanography (India)
Singh, Y.; Nair, R.R.; Singh, H.; Datta, P.; Jaiswal, P.; Dewangan, P.; Ramprasad, T.
Stepwise linear regression, multi-layer feed forward neural (MLFN) network method was used to predict the 2D distribution of P-wave velocity, resistivity, porosity, and gas hydrate saturation throughout seismic section NGHP-01 in the Krishna...
The No-Prop algorithm: a new learning algorithm for multilayer neural networks.
Widrow, Bernard; Greenblatt, Aaron; Kim, Youngsik; Park, Dookun
2013-01-01
A new learning algorithm for multilayer neural networks that we have named No-Propagation (No-Prop) is hereby introduced. With this algorithm, the weights of the hidden-layer neurons are set and fixed with random values. Only the weights of the output-layer neurons are trained, using steepest descent to minimize mean square error, with the LMS algorithm of Widrow and Hoff. The purpose of introducing nonlinearity with the hidden layers is examined from the point of view of Least Mean Square Error Capacity (LMS Capacity), which is defined as the maximum number of distinct patterns that can be trained into the network with zero error. This is shown to be equal to the number of weights of each of the output-layer neurons. The No-Prop algorithm and the Back-Prop algorithm are compared. Our experience with No-Prop is limited, but from the several examples presented here, it seems that the performance regarding training and generalization of both algorithms is essentially the same when the number of training patterns is less than or equal to LMS Capacity. When the number of training patterns exceeds Capacity, Back-Prop is generally the better performer. But equivalent performance can be obtained with No-Prop by increasing the network Capacity by increasing the number of neurons in the hidden layer that drives the output layer. The No-Prop algorithm is much simpler and easier to implement than Back-Prop. Also, it converges much faster. It is too early to definitively say where to use one or the other of these algorithms. This is still a work in progress. Copyright © 2012 Elsevier Ltd. All rights reserved.
2009-01-01
In this paper, we report an application of neural networks to simulate daily nitrate-nitrogen and suspended sediment fluxes from a small 7.1 km2 agricultural catchment (Melarchez), 70km east of Paris,France. Nitrate-nitrogen and sediment losses are only a few possible consequences of soil erosion and biochemical applications associated to human activities such as intensive agriculture. Stacked multilayer perceptrons models (MLPs) like the ones explored here are based on commonly available inp...
On the Combination of Multi-Layer Source Coding and Network Coding for Wireless Networks
DEFF Research Database (Denmark)
Krigslund, Jeppe; Fitzek, Frank; Pedersen, Morten Videbæk
2013-01-01
This paper introduces a mutually beneficial interplay between network coding and scalable video source coding in order to propose an energy-efficient video streaming approach accommodating multiple heterogeneous receivers, for which current solutions are either inefficient or insufficient. State...... support of multi-resolution video streaming....
Inter-layer synchronization in non-identical multi-layer networks
Leyva, I.; Sevilla-Escoboza, R.; Sendiña-Nadal, I.; Gutiérrez, R.; Buldú, J. M.; Boccaletti, S.
2017-04-01
Inter-layer synchronization is a dynamical process occurring in multi-layer networks composed of identical nodes. This process emerges when all layers are synchronized, while nodes in each layer do not necessarily evolve in unison. So far, the study of such inter-layer synchronization has been restricted to the case in which all layers have an identical connectivity structure. When layers are not identical, the inter-layer synchronous state is no longer a stable solution of the system. Nevertheless, when layers differ in just a few links, an approximate treatment is still feasible, and allows one to gather information on whether and how the system may wander around an inter-layer synchronous configuration. We report the details of an approximate analytical treatment for a two-layer multiplex, which results in the introduction of an extra inertial term accounting for structural differences. Numerical validation of the predictions highlights the usefulness of our approach, especially for small or moderate topological differences in the intra-layer coupling. Moreover, we identify a non-trivial relationship connecting the betweenness centrality of the missing links and the intra-layer coupling strength. Finally, by the use of multiplexed layers of electronic circuits, we study the inter-layer synchronization as a function of the removed links.
Memristor-based multilayer neural networks with online gradient descent training.
Soudry, Daniel; Di Castro, Dotan; Gal, Asaf; Kolodny, Avinoam; Kvatinsky, Shahar
2015-10-01
Learning in multilayer neural networks (MNNs) relies on continuous updating of large matrices of synaptic weights by local rules. Such locality can be exploited for massive parallelism when implementing MNNs in hardware. However, these update rules require a multiply and accumulate operation for each synaptic weight, which is challenging to implement compactly using CMOS. In this paper, a method for performing these update operations simultaneously (incremental outer products) using memristor-based arrays is proposed. The method is based on the fact that, approximately, given a voltage pulse, the conductivity of a memristor will increment proportionally to the pulse duration multiplied by the pulse magnitude if the increment is sufficiently small. The proposed method uses a synaptic circuit composed of a small number of components per synapse: one memristor and two CMOS transistors. This circuit is expected to consume between 2% and 8% of the area and static power of previous CMOS-only hardware alternatives. Such a circuit can compactly implement hardware MNNs trainable by scalable algorithms based on online gradient descent (e.g., backpropagation). The utility and robustness of the proposed memristor-based circuit are demonstrated on standard supervised learning tasks.
Extracting rules from neural networks as decision diagrams.
Chorowski, Jan; Zurada, Jacek M
2011-12-01
Rule extraction from neural networks (NNs) solves two fundamental problems: it gives insight into the logic behind the network and in many cases, it improves the network's ability to generalize the acquired knowledge. This paper presents a novel eclectic approach to rule extraction from NNs, named LOcal Rule Extraction (LORE), suited for multilayer perceptron networks with discrete (logical or categorical) inputs. The extracted rules mimic network behavior on the training set and relax this condition on the remaining input space. First, a multilayer perceptron network is trained under standard regime. It is then transformed into an equivalent form, returning the same numerical result as the original network, yet being able to produce rules generalizing the network output for cases similar to a given input. The partial rules extracted for every training set sample are then merged to form a decision diagram (DD) from which logic rules can be extracted. A rule format explicitly separating subsets of inputs for which an answer is known from those with an undetermined answer is presented. A special data structure, the decision diagram, allowing efficient partial rule merging is introduced. With regard to rules' complexity and generalization abilities, LORE gives results comparable to those reported previously. An algorithm transforming DDs into interpretable boolean expressions is described. Experimental running times of rule extraction are proportional to the network's training time.
Tierra, Alfonso; Romero, Ricardo
2014-12-01
Prior any satellite technology developments, the geodetic networks of a country were realized from a topocentric datum, and hence the respective cartography was performed. With availability of Global Navigation Satellite Systems-GNSS, cartography needs to be updated and referenced to a geocentric datum to be compatible with this technology. Cartography in Ecuador has been performed using the PSAD56 (Provisional South American Datum 1956) systems, nevertheless it's necessary to have inside the system SIRGAS (SIstema de Referencia Geocéntrico para las AmericaS). This transformation between PSAD56 to SIRGAS use seven transformation parameters calculated with the method Helmert. These parameters, in case of Ecuador are compatible for scales of 1:25 000 or less, that does not satisfy the requirements on applications for major scales. In this study, the technique of neural networks is demonstrated as an alternative for improving the processing of UTM planes coordinates E, N (East, North) from PSAD56 to SIRGAS. Therefore, from the coordinates E, N, of the two systems, four transformation parameters were calculated (two of translation, one of rotation, and one scale difference) using the technique bidimensional transformation. Additionally, the same coordinates were used to training Multilayer Artificial Neural Network -MANN, in which the inputs are the coordinates E, N in PSAD56 and output are the coordinates E, N in SIRGAS. Both the two-dimensional transformation and ANN were used as control points to determine the differences between the mentioned methods. The results imply that, the coordinates transformation obtained with the artificial neural network multilayer trained have been improving the results that the bidimensional transformation, and compatible to scales 1:5000. Dostęp do nowoczesnych technologii, w tym GNSS umożliwiły dokładniejsze zdefi niowanie systemów odniesień przestrzennych wykorzystywanych m.in. w defi niowaniu krajowych układów odniesień i
Neural networks and statistical learning
Du, Ke-Lin
2014-01-01
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...
Optimal properties of analog perceptrons with excitatory weights.
Directory of Open Access Journals (Sweden)
Claudia Clopath
Full Text Available The cerebellum is a brain structure which has been traditionally devoted to supervised learning. According to this theory, plasticity at the Parallel Fiber (PF to Purkinje Cell (PC synapses is guided by the Climbing fibers (CF, which encode an 'error signal'. Purkinje cells have thus been modeled as perceptrons, learning input/output binary associations. At maximal capacity, a perceptron with excitatory weights expresses a large fraction of zero-weight synapses, in agreement with experimental findings. However, numerous experiments indicate that the firing rate of Purkinje cells varies in an analog, not binary, manner. In this paper, we study the perceptron with analog inputs and outputs. We show that the optimal input has a sparse binary distribution, in good agreement with the burst firing of the Granule cells. In addition, we show that the weight distribution consists of a large fraction of silent synapses, as in previously studied binary perceptron models, and as seen experimentally.
Directory of Open Access Journals (Sweden)
Hussein Aly Kamel Rady
2011-11-01
Full Text Available Improving the efficiency and convergence rate of the Multilayer Backpropagation Neural Network Algorithms is an active area of research. The last years have witnessed an increasing attention to entropy based criteria in adaptive systems. Several principles were proposed based on the maximization or minimization of entropic cost functions. One way of entropy criteria in learning systems is to minimize the entropy of the error between two variables: typically one is the output of the learning system and the other is the target. In this paper, improving the efficiency and convergence rate of Multilayer Backpropagation (BP Neural Networks was proposed. The usual Mean Square Error (MSE minimization principle is substituted by the minimization of Shannon Entropy (SE of the differences between the multilayer perceptions output and the desired target. These two cost functions are studied, analyzed and tested with two different activation functions namely, the Cauchy and the hyperbolic tangent activation functions. The comparative approach indicates that the Degree of convergence using Shannon Entropy cost function is higher than its counterpart using MSE and that MSE speeds the convergence than Shannon Entropy.
MIMO transmit scheme based on morphological perceptron with competitive learning.
Valente, Raul Ambrozio; Abrão, Taufik
2016-08-01
This paper proposes a new multi-input multi-output (MIMO) transmit scheme aided by artificial neural network (ANN). The morphological perceptron with competitive learning (MP/CL) concept is deployed as a decision rule in the MIMO detection stage. The proposed MIMO transmission scheme is able to achieve double spectral efficiency; hence, in each time-slot the receiver decodes two symbols at a time instead one as Alamouti scheme. Other advantage of the proposed transmit scheme with MP/CL-aided detector is its polynomial complexity according to modulation order, while it becomes linear when the data stream length is greater than modulation order. The performance of the proposed scheme is compared to the traditional MIMO schemes, namely Alamouti scheme and maximum-likelihood MIMO (ML-MIMO) detector. Also, the proposed scheme is evaluated in a scenario with variable channel information along the frame. Numerical results have shown that the diversity gain under space-time coding Alamouti scheme is partially lost, which slightly reduces the bit-error rate (BER) performance of the proposed MP/CL-NN MIMO scheme. Copyright © 2016 Elsevier Ltd. All rights reserved.
Hybrid Multi-Layer Network Control for Emerging Cyber-Infrastructures
Energy Technology Data Exchange (ETDEWEB)
Summerhill, Richard [Internet2, Washington, DC (United States); Lehman, Tom [Univ. of Southern California, Los Angeles, CA (United States). Information Sciences Inst. (ISI); Ghani, Nasir [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical & Computer Engineering; Boyd, Eric [Univ. Corporation for Advanced Internet Development (UCAID), Washington, DC (United States)
2009-08-14
There were four basic task areas identified for the Hybrid-MLN project. They are: Multi-Layer, Multi-Domain, Control Plane Architecture and Implementation; Heterogeneous DataPlane Testing; Simulation; Project Publications, Reports, and Presentations.
Practical Application of Neural Networks in State Space Control
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon
theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train....... Then the controller is shown to work on a simulation example. We also address the potential problem of too rapidly fluctuating parameters by including regularization in the learning rule. Next we develop a direct adaptive certainty-equivalence controller based on neurofuzzy models. The control loop is proven...
Gao, Zhong-Ke; Dang, Wei-Dong; Li, Shan; Yang, Yu-Xuan; Wang, Hong-Tao; Sheng, Jing-Ran; Wang, Xiao-Fan
2017-07-14
Numerous irregular flow structures exist in the complicated multiphase flow and result in lots of disparate spatial dynamical flow behaviors. The vertical oil-water slug flow continually attracts plenty of research interests on account of its significant importance. Based on the spatial transient flow information acquired through our designed double-layer distributed-sector conductance sensor, we construct multilayer modality-based network to encode the intricate spatial flow behavior. Particularly, we calculate the PageRank versatility and multilayer weighted clustering coefficient to quantitatively explore the inferred multilayer modality-based networks. Our analysis allows characterizing the complicated evolution of oil-water slug flow, from the opening formation of oil slugs, to the succedent inter-collision and coalescence among oil slugs, and then to the dispersed oil bubbles. These properties render our developed method particularly powerful for mining the essential flow features from the multilayer sensor measurements.
The performance of immune-based neural network with financial time series prediction
Directory of Open Access Journals (Sweden)
Dhiya Al-Jumeily
2015-12-01
Full Text Available This paper presents the use of immune-based neural networks that include multilayer perceptron (MLP and functional neural network for the prediction of financial time series signals. Extensive simulations for the prediction of one- and five-steps-ahead of stationary and non-stationary time series were performed which indicate that immune-based neural networks in most cases demonstrated advantages in capturing chaotic movement in the financial signals with an improvement in the profit return and rapid convergence over MLPs.
Convolutional neural network-based data page classification for holographic memory.
Shimobaba, Tomoyoshi; Kuwata, Naoki; Homma, Mizuha; Takahashi, Takayuki; Nagahama, Yuki; Sano, Marie; Hasegawa, Satoki; Hirayama, Ryuji; Kakue, Takashi; Shiraki, Atsushi; Takada, Naoki; Ito, Tomoyoshi
2017-09-10
We propose a deep-learning-based classification of data pages used in holographic memory. We numerically investigated the classification performance of a conventional multilayer perceptron (MLP) and a deep neural network, under the condition that reconstructed page data are contaminated by some noise and are randomly laterally shifted. When data pages are randomly laterally shifted, the MLP was found to have a classification accuracy of 93.02%, whereas the deep neural network was able to classify data pages at an accuracy of 99.98%. The accuracy of the deep neural network is 2 orders of magnitude better than the MLP.
Using Artificial Neural Networks for ECG Signals Denoising
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Zoltán Germán-Salló
2010-12-01
Full Text Available The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1th sample from n previous samples To train and adjust the network weights, the backpropagation (BP algorithm was used. In this paper, prediction of ECG signals (as time series using multi-layer feedforward neural networks will be described. The results are evaluated through approximation error which is defined as the difference between the predicted and the original signal.The prediction procedure is carried out (simulated in MATLAB environment, using signals from MIT-BIH arrhythmia database. Preliminary results are encouraging enough to extend the proposed method for other types of data signals.
Energy Technology Data Exchange (ETDEWEB)
Bordieu, Ch.; Rebiere, D. [Bordeaux-1 Univ., Lab. IXL, UMR CNRS 5818, 33 (France); Pistre, J.; Planata, R. [Centre d' Etudes du Bouchet, 91 - Vert-le-Petit (France)
1999-07-01
The association of artificial neural networks (multilayer perceptrons) with a real time pattern recognition technique (shifting windows) allowed the development of systems for the detection and the quantification of gases. Shifting window technique is presented and offers an interesting way to improve the detection response time. The partial detector characterization with regard to its parameters was realized. Applications dealing with the detection of gas compounds using surface acoustic sensors permit to show the shifting window technique feasibility. (author)
Directory of Open Access Journals (Sweden)
Souza Reinaldo C.
2010-04-01
Full Text Available Artificial neural networks, especially multilayer perceptrons, have been recognised as being a powerful technique for forecasting nonlinear time series; however, cascade-correlation architecture is a strong competitor in this task due to it incorporating several advantages related to the statistical identification of multilayer perceptrons. This paper compares the accuracy of a cascade-co- rrelation neural network to the linear approach, multilayer perceptrons and dynamic architecture for artificial neural networks (DAN2 to determine whether the cascade-correlation network was able to forecast the time series being studied with more accu- racy. It was concluded that cascade-correlation was able to forecast time series with more accuracy than other approaches.Las redes neuronales, y en particular los perceptrones multicapa (MLP, han sido reconocidos como una de las más poderosas técnicas para estimar series de tiempo; sin embargo, la técnica de redes cascada-correlación (CC es un fuerte competidor para pronosticar series temporales pues incorpora mejoras a los problemas de identificabilidad estadística del modelo del MLP. En és- te artículo se compara el rendimiento de las redes CC respecto de otras técnicas, entre ellas el MLP, ANN y Arima, usando va- rias series de tiempo no lineales del mundo real, con el fin de determinar si las CC ofrecen buenos resultados en la práctica. Los resultados indican que las redes CC, en la mayoría de los casos, son superiores a los MLP, ANN y Arima, logrando errores me- nores en magnitud que los reportados en la literatura usando dichas técnicas, mientras que en relación a DAN2 se lograron e- rrores cercanos e incluso mejores.
APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION
Energy Technology Data Exchange (ETDEWEB)
Musson, John C. [JLAB; Seaton, Chad [JLAB; Spata, Mike F. [JLAB; Yan, Jianxun [JLAB
2012-11-01
Stripline BPM sensors contain inherent non-linearities, as a result of field distortions from the pickup elements. Many methods have been devised to facilitate corrections, often employing polynomial fitting. The cost of computation makes real-time correction difficult, particulalry when integer math is utilized. The application of neural-network technology, particularly the multi-layer perceptron algorithm, is proposed as an efficient alternative for electrode linearization. A process of supervised learning is initially used to determine the weighting coefficients, which are subsequently applied to the incoming electrode data. A non-linear layer, known as an activation layer, is responsible for the removal of saturation effects. Implementation of a perceptron in an FPGA-based software-defined radio (SDR) is presented, along with performance comparisons. In addition, efficient calculation of the sigmoidal activation function via the CORDIC algorithm is presented.
Advances in Artificial Neural Networks – Methodological Development and Application
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Yanbo Huang
2009-08-01
Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological
Directory of Open Access Journals (Sweden)
A. Y. Shamseldin
2002-01-01
Full Text Available The Multi-Layer Feed-Forward Neural Network (MLFFNN is applied in the context of river flow forecast combination, where a number of rainfall-runoff models are used simultaneously to produce an overall combined river flow forecast. The operation of the MLFFNN depends not only on its neuron configuration but also on the choice of neuron transfer function adopted, which is non-linear for the hidden and output layers. These models, each having a different structure to simulate the perceived mechanisms of the runoff process, utilise the information carrying capacity of the model calibration data in different ways. Hence, in a discharge forecast combination procedure, the discharge forecasts of each model provide a source of information different from that of the other models used in the combination. In the present work, the significance of the choice of the transfer function type in the overall performance of the MLFFNN, when used in the river flow forecast combination context, is investigated critically. Five neuron transfer functions are used in this investigation, namely, the logistic function, the bipolar function, the hyperbolic tangent function, the arctan function and the scaled arctan function. The results indicate that the logistic function yields the best model forecast combination performance. Keywords: River flow forecast combination, multi-layer feed-forward neural network, neuron transfer functions, rainfall-runoff models
Abiriand Bhekisipho Twala, Olufunminiyi
2017-08-01
In this paper, a multilayer feedforward neural network with Bayesian regularization constitutive model is developed for alloy 316L during high strain rate and high temperature plastic deformation. The input variables are strain rate, temperature and strain while the output value is the flow stress of the material. The results show that the use of Bayesian regularized technique reduces the potential of overfitting and overtraining. The prediction quality of the model is thereby improved. The model predictions are in good agreement with experimental measurements. The measurement data used for the network training and model comparison were taken from relevant literature. The developed model is robust as it can be generalized to deformation conditions slightly below or above the training dataset.
Using neural networks for prediction of nuclear parameters
Energy Technology Data Exchange (ETDEWEB)
Pereira Filho, Leonidas; Souto, Kelling Cabral, E-mail: leonidasmilenium@hotmail.com, E-mail: kcsouto@bol.com.br [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ (Brazil); Machado, Marcelo Dornellas, E-mail: dornemd@eletronuclear.gov.br [Eletrobras Termonuclear S.A. (GCN.T/ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear
2013-07-01
Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)
Artificial Neural Network Modelling of Vibration in the Milling of AZ91D Alloy
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Ireneusz Zagórski
2017-09-01
Full Text Available The paper reports the results of artificial neural network modelling of vibration in. a milling process of magnesium alloy AZ91D by a TiAlN-coated carbide tool. Vibrations in machining processes are regarded as an additional, absolute machinability index. The modelling was performed using the so-called “black box” model. The best fit was determined for the input and output data obtained from the machining process. The simulations were performed by the Statistica software using two types of neural networks: RBF (Radial Basis Function and MLP (Multi-Layered Perceptron.
Foreground removal from Planck Sky Model temperature maps using a MLP neural network
DEFF Research Database (Denmark)
Nørgaard-Nielsen, Hans Ulrik; Hebert, K.
2009-01-01
with no systematic errors. To demonstrate the feasibility of a simple multilayer perceptron (MLP) neural network for extracting the CMB temperature signal, we have analyzed a specific data set, namely the Planck Sky Model maps, developed for evaluation of different component separation methods before including them...... in the Planck data analysis pipeline. It is found that a MLP neural network can provide a CMB map of about 80% of the sky to a very high degree uncorrelated with the foreground components. Also the derived power spectrum shows little evidence for systematic errors....
Multi-layer holographic bifurcative neural network system for real-time adaptive EOS data analysis
Liu, Hua-Kuang; Huang, K. S.; Diep, J.
1993-01-01
Optical data processing techniques have the inherent advantage of high data throughout, low weight and low power requirements. These features are particularly desirable for onboard spacecraft in-situ real-time data analysis and data compression applications. the proposed multi-layer optical holographic neural net pattern recognition technique will utilize the nonlinear photorefractive devices for real-time adaptive learning to classify input data content and recognize unexpected features. Information can be stored either in analog or digital form in a nonlinear photofractive device. The recording can be accomplished in time scales ranging from milliseconds to microseconds. When a system consisting of these devices is organized in a multi-layer structure, a feedforward neural net with bifurcating data classification capability is formed. The interdisciplinary research will involve the collaboration with top digital computer architecture experts at the University of Southern California.
Sergeev, A. P.; Tarasov, D. A.; Buevich, A. G.; Subbotina, I. E.; Shichkin, A. V.; Sergeeva, M. V.; Lvova, O. A.
2017-06-01
The work deals with the application of neural networks residual kriging (NNRK) to the spatial prediction of the abnormally distributed soil pollutant (Cr). It is known that combination of geostatistical interpolation approaches (kriging) and neural networks leads to significantly better prediction accuracy and productivity. Generalized regression neural networks and multilayer perceptrons are classes of neural networks widely used for the continuous function mapping. Each network has its own pros and cons; however both demonstrated fast training and good mapping possibilities. In the work, we examined and compared two combined techniques: generalized regression neural network residual kriging (GRNNRK) and multilayer perceptron residual kriging (MLPRK). The case study is based on the real data sets on surface contamination by chromium at a particular location of the subarctic Novy Urengoy, Russia, obtained during the previously conducted screening. The proposed models have been built, implemented and validated using ArcGIS and MATLAB environments. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. MLRPK showed the best predictive accuracy comparing to the geostatistical approach (kriging) and even to GRNNRK.
THE USE OF NEURAL NETWORK TECHNOLOGY TO MODEL SWIMMING PERFORMANCE
Directory of Open Access Journals (Sweden)
António José Silva
2007-03-01
Full Text Available The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility, swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports
Directory of Open Access Journals (Sweden)
M. Seifouri
2012-06-01
Full Text Available The main goal in this paper is to design single-mode optical fibers for DWDM networks, which are used today in rapid communications. These networks require low dispersion in a wide range of wavelengths. So, in this paper, multi-layer optical fibers with low dispersion value and flat dispersion slope in wavelength range of (1.5-1.6µm are designed, using optimization algorithms.
Permeability prediction in shale gas reservoirs using Neural Network
Aliouane, Leila; Ouadfeul, Sid-Ali
2017-04-01
Here, we suggest the use of the artificial neural network for permeability prediction in shale gas reservoirs using artificial neural network. Prediction of Permeability in shale gas reservoirs is a complicated task that requires new models where Darcy's fluid flow model is not suitable. Proposed idea is based on the training of neural network machine using the set of well-logs data as an input and the measured permeability as an output. In this case the Multilayer Perceptron neural network machines is used with Levenberg Marquardt algorithm. Application to two horizontal wells drilled in the Barnett shale formation exhibit the power of neural network model to resolve such as problem. Keywords: Artificial neural network, permeability, prediction , shale gas.
Practical application of artificial neural networks in the neurosciences
Pinti, Antonio
1995-04-01
This article presents a practical application of artificial multi-layer perceptron (MLP) neural networks in neurosciences. The data that are processed are labeled data from the visual analysis of electrical signals of human sleep. The objective of this work is to automatically classify into sleep stages the electrophysiological signals recorded from electrodes placed on a sleeping patient. Two large data bases were designed by experts in order to realize this study. One data base was used to train the network and the other to test its generalization capacity. The classification results obtained with the MLP network were compared to a type K nearest neighbor Knn non-parametric classification method. The MLP network gave a better result in terms of classification than the Knn method. Both classification techniques were implemented on a transputer system. With both networks in their final configuration, the MLP network was 160 times faster than the Knn model in classifying a sleep period.
Directory of Open Access Journals (Sweden)
Lucky Lhaura Van FC
2016-02-01
Full Text Available AbstrakModalitas atau gaya belajar dalam proses pembelajaran mempunyai dominasi masing-masing. Ada seseorang yang dalam proses menerima informasi didominasi oleh mata dan disebut gaya belajar visual dan bila indera pendengaran yang mendominasi maka orang tersebut dikatakan memiliki gaya belajar auditori dan kalau sesorang lebih menyukai keterlibatan fisik maka dia mempunyai gaya belajar kinestetik. Apabila kita ingin mengidentifikasi gaya belajar mahasiswa pada kelas tertentu dengan cara yang lebih valid, maka sebaiknya diketahui melalui angket yang harus diisi oleh mahasiswa tersebut. Statement yang harus diisi pada angket mengarah kepada karakteristik dari masing-masing gaya belajar. Dari angket ini nanti akan ditemukan presentase mahasiswa yang mempunyai gaya belajar visual, auditori dan kinestetik. Kemudian diolah secara komputerisasi yang berbasis JST dengan metode perceptron.Kata kunci: Jaringan Syaraf Tiruan, Gaya Belajar, Metode PerceptronAbstractModalities or styles of learning in the learning process has dominance respectively. There is someone in the process of receiving information dominated by the eye and called a visual learning style and when the sense of hearing which dominates then the person is said to have an auditory learning style and if someone prefers physical involvement he has a kinesthetic learning style. If we want to identify the learning styles of students in a particular class in a manner that is valid, then it should be made known through a questionnaire to be completed by the student. Statement to be filled in the questionnaire leads to the characteristics of each learning style. From this questionnaire will be found the percentage of students who have learning styles of visual, auditory and kinesthetic. Then processed in a computerized neural network with perceptron method.Keywords: Artificial Neural Networks, Learning Styles, Perceptron Method
Perceptron ensemble of graph-based positive-unlabeled learning for disease gene identification.
Jowkar, Gholam-Hossein; Mansoori, Eghbal G
2016-10-01
Identification of disease genes, using computational methods, is an important issue in biomedical and bioinformatics research. According to observations that diseases with the same or similar phenotype have the same biological characteristics, researchers have tried to identify genes by using machine learning tools. In recent attempts, some semi-supervised learning methods, called positive-unlabeled learning, is used for disease gene identification. In this paper, we present a Perceptron ensemble of graph-based positive-unlabeled learning (PEGPUL) on three types of biological attributes: gene ontologies, protein domains and protein-protein interaction networks. In our method, a reliable set of positive and negative genes are extracted using co-training schema. Then, the similarity graph of genes is built using metric learning by concentrating on multi-rank-walk method to perform inference from labeled genes. At last, a Perceptron ensemble is learned from three weighted classifiers: multilevel support vector machine, k-nearest neighbor and decision tree. The main contributions of this paper are: (i) incorporating the statistical properties of gene data through choosing proper metrics, (ii) statistical evaluation of biological features, and (iii) noise robustness characteristic of PEGPUL via using multilevel schema. In order to assess PEGPUL, we have applied it on 12950 disease genes with 949 positive genes from six class of diseases and 12001 unlabeled genes. Compared with some popular disease gene identification methods, the experimental results show that PEGPUL has reasonable performance. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Murat Cuhadar
2014-03-01
Full Text Available Abstract Cruise ports emerged as an important sector for the economy of Turkey bordered on three sides by water. Forecasting cruise tourism demand ensures better planning, efficient preparation at the destination and it is the basis for elaboration of future plans. In the recent years, new techniques such as; artificial neural networks were employed for developing of the predictive models to estimate tourism demand. In this study, it is aimed to determine the forecasting method that provides the best performance when compared the forecast accuracy of Multi-layer Perceptron (MLP, Radial Basis Function (RBF and Generalized Regression neural network (GRNN to estimate the monthly inbound cruise tourism demand to İzmir via the method giving best results. We used the total number of foreign cruise tourist arrivals as a measure of inbound cruise tourism demand and monthly cruise tourist arrivals to İzmir Cruise Port in the period of January 2005 ‐December 2013 were utilized to appropriate model. Experimental results showed that radial basis function (RBF neural network outperforms multi-layer perceptron (MLP and the generalised regression neural networks (GRNN in terms of forecasting accuracy. By the means of the obtained RBF neural network model, it has been forecasted the monthly inbound cruise tourism demand to İzmir for the year 2014.
Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks
S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)
2000-01-01
textabstractWe demonstrate that spiking neural networks encoding information in spike times are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on
Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation
Directory of Open Access Journals (Sweden)
Ahmed M. Wefky
2010-04-01
Full Text Available It is accepted that the activity of the vehicle pedals (i.e., throttle, brake, clutch reflects the driver’s behavior, which is at least partially related to the fuel consumption and vehicle pollutant emissions. This paper presents a solution to estimate the driver activity regardless of the type, model, and year of fabrication of the vehicle. The solution is based on an alternative sensor system (regime engine, vehicle speed, frontal inclination and linear acceleration that reflects the activity of the pedals in an indirect way, to estimate that activity by means of a multilayer perceptron neural network with a single hidden layer.
Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task
2017-01-01
Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making. PMID:28961245
Directory of Open Access Journals (Sweden)
Hossein Naderi
2012-08-01
Full Text Available Stock market prediction is one of the most important interesting areas of research in business. Stock markets prediction is normally assumed as tedious task since there are many factors influencing the market. The primary objective of this paper is to forecast trend closing price movement of Tehran Stock Exchange (TSE using financial accounting ratios from year 2003 to year 2008. The proposed study of this paper uses two approaches namely Artificial Neural Networks and multi-layer perceptron. Independent variables are accounting ratios and dependent variable of stock price , so the latter was gathered for the industry of Motor Vehicles and Auto Parts. The results of this study show that neural networks models are useful tools in forecasting stock price movements in emerging markets but multi-layer perception provides better results in term of lowering error terms.
Power-Aware Multi-Layer Translucent Network Design: an Integrated OPEX/CAPEX Analysis
DEFF Research Database (Denmark)
Saldaña Cercos, Silvia; Resendo, Leandro C.; Ribeiro, Moisés R. N.
2014-01-01
We propose a three-phase network design model minimizing CAPEX and OPEX in IP-over-WDM architectures. By forbidding reconfiguration (accounting for 58\\% of the OPEX) we achieve only 4.2\\% increase in power consumption at no CAPEX expenses.......We propose a three-phase network design model minimizing CAPEX and OPEX in IP-over-WDM architectures. By forbidding reconfiguration (accounting for 58\\% of the OPEX) we achieve only 4.2\\% increase in power consumption at no CAPEX expenses....
Shamseldin, A. Y.; Nasr, A. E.; O'Connor, K. M.
The Multi-Layer Feed-Forward Neural Network (MLFFNN) is applied in the context of river flow forecast combination, where a number of rainfall-runoff models are used simultaneously to produce an overall combined river flow forecast. The operation of the MLFFNN depends not only on its neuron configuration but also on the choice of neuron transfer function adopted, which is non-linear for the hidden and output layers. These models, each having a different structure to simulate the perceived mechanisms of the runoff process, utilise the information carrying capacity of the model calibration data in different ways. Hence, in a discharge forecast combination procedure, the discharge forecasts of each model provide a source of information different from that of the other models used in the combination. In the present work, the significance of the choice of the transfer function type in the overall performance of the MLFFNN, when used in the river flow forecast combination context, is investigated critically. Five neuron transfer functions are used in this investigation, namely, the logistic function, the bipolar function, the hyperbolic tangent function, the arctan function and the scaled arctan function. The results indicate that the logistic function yields the best model forecast combination performance.
Directory of Open Access Journals (Sweden)
Bo Ye
2014-01-01
Full Text Available Accurate evaluation and characterization of defects in multilayered structures from eddy current nondestructive testing (NDT signals are a difficult inverse problem. There is scope for improving the current methods used for solving the inverse problem by incorporating information of uncertainty in the inspection process. Here, we propose to evaluate defects quantitatively from eddy current NDT signals using Bayesian networks (BNs. BNs are a useful method in handling uncertainty in the inspection process, eventually leading to the more accurate results. The domain knowledge and the experimental data are used to generate the BN models. The models are applied to predict the signals corresponding to different defect characteristic parameters or to estimate defect characteristic parameters from eddy current signals in real time. Finally, the estimation results are analyzed. Compared to the least squares regression method, BNs are more robust with higher accuracy and have the advantage of being a bidirectional inferential mechanism. This approach allows results to be obtained in the form of full marginal conditional probability distributions, providing more information on the defect. The feasibility of BNs presented and discussed in this paper has been validated.
PREDICTION OF BOD AND COD OF A REFINERY WASTEWATER USING MULTILAYER ARTIFICIAL NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
Eldon Raj Rene
2008-06-01
Full Text Available In the recent past, artificial neural networks (ANNs have shown the ability to learn and capture non-linear static or dynamic behaviour among variables based on the given set of data. Since the knowledge of internal procedure is not necessary, the modelling can take place with minimum previous knowledge about the process through proper training of the network. In the present study, 12 ANN based models were proposed to predict the Biochemical Oxygen Demand (BOD5 and Chemical Oxygen Demand (COD concentrations of wastewater generated from the effluent treatment plant of a petrochemical industry. By employing the standard back error propagation (BEP algorithm, the network was trained with 103 data points for water quality indices such as Total Suspended Solids (TSS, Total Dissolved Solids (TDS, Phenol concentration, Ammoniacal Nitrogen (AMN, Total Organic Carbon (TOC and Kjeldahl’s Nitrogen (KJN to predict BOD and COD. After appropriate training, the network was tested with a separate test data and the best model was chosen based on the sum square error (training and percentage average relative error (% ARE for testing. The results from this study reveal that ANNs can be accurate and efficacious in predicting unknown concentrations of water quality parameters through its versatile training process.
Training a multilayer neural network for the Euro-dollar (EUR/ USD) exchange rate
National Research Council Canada - National Science Library
Jaime Alberto Villamil Torres; Jesús Alberto Delgado Rivera
2010-01-01
... (as first approximation) a random walk. This paper reports the results of using ANNs for Euro/USD exchange rate trading and the usefulness of the algorithm for chemotaxis leading to training networks thereby maximising an objective function re predicting a trader’s profits. JEL: F310, C450.
Towards multi-layered intrusion detection in high-speed networks
Golling, Mario; Hofstede, R.J.; Koch, Robert
Traditional Intrusion Detection approaches rely on the inspection of individual packets, often referred to as Deep Packet Inspection (DPI), where individual packets are scanned for suspicious patterns. However, the rapid increase of link speeds and throughputs - especially in larger networks such as
Analysis of Mars Express Ionogram Data via a Multilayer Artificial Neural Network
Wilkinson, Collin; Potter, Arron; Palmer, Greg; Duru, Firdevs
2017-01-01
Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS), which is a low frequency radar on the Mars Express (MEX) Spacecraft, can provide electron plasma densities of the ionosphere local at the spacecraft in addition to densities obtained with remote sounding. The local electron densities are obtained, with a standard error of about 2%, by measuring the electron plasma frequencies with an electronic ruler on ionograms, which are plots of echo intensity as a function of time and frequency. This is done by using a tool created at the University of Iowa (Duru et al., 2008). This approach is time consuming due to the rapid accumulation of ionogram data. In 2013, results from an algorithm-based analysis of ionograms were reported by Andrews et al., but this method did not improve the human error. In the interest of fast, accurate data interpretation, a neural network (NN) has been created based on the Fast Artificial Neural Network C libraries. This NN consists of artificial neurons, with 4 layers of 12960, 10000, 1000 and 1 neuron(s) each, consecutively. This network was trained using 40 iterations of 1000 orbits. The algorithm-based method of Andrews et al. had a standard error of 40%, while the neural network has achieved error on the order of 20%.
A study on the forecasting of daily stream flow using the multilayer neural networks model
Energy Technology Data Exchange (ETDEWEB)
Kim, Sung-Won [Colorado State University, Fort Collins, CO(United States)
2000-10-31
In this study, Neural Networks models were used to forecast daily stream flow at Jindong station of the Nakdong River basin. Neural Networks models consist of CASE 1(5-5-1) and CASE 2(5-5-5-1). The criteria which separates two models is the number of hidden layers. Each model has Fletcher-Reeves Conjugate Gradient BackPropagation(FR-CGBP) and Scaled Conjugate Gradient BackPropagation(SCGBP) algorithms, which are better than original BackPropagation(BP) in convergence of global error and training tolerance. The data which are available for model training and validation were composed of wet, average, dry, wet+average, wet+dry, average+dry and wet+average+dry year respectively. During model training, the optimal connection weights and biases were determined using each data set and the daily stream flow was calculated at the same time. Except for wet+dry year, the results of training were good conditions by statistical analysis of forecast errors. And, model validation was carried out using the connection weights and biases which were calculated from model training. The results of validation were satisfactory like those of training. Daily stream flow forecasting using Neural Networks models were compared with those forecasted by Multiple Regression Analysis Model(MRAM). Neural Networks models were displayed slightly better results than MRAM in this study. Thus, Neural Networks models have much advantage to provide a more systematic approach, reduce model parameters, and shorten the time spent in the model development. (author). 22 refs., 9 tabs., 7 figs.
Multilayer cellular neural network and fuzzy C-mean classifiers: comparison and performance analysis
Trujillo San-Martin, Maite; Hlebarov, Vejen; Sadki, Mustapha
2004-11-01
Neural Networks and Fuzzy systems are considered two of the most important artificial intelligent algorithms which provide classification capabilities obtained through different learning schemas which capture knowledge and process it according to particular rule-based algorithms. These methods are especially suited to exploit the tolerance for uncertainty and vagueness in cognitive reasoning. By applying these methods with some relevant knowledge-based rules extracted using different data analysis tools, it is possible to obtain a robust classification performance for a wide range of applications. This paper will focus on non-destructive testing quality control systems, in particular, the study of metallic structures classification according to the corrosion time using a novel cellular neural network architecture, which will be explained in detail. Additionally, we will compare these results with the ones obtained using the Fuzzy C-means clustering algorithm and analyse both classifiers according to its classification capabilities.
Training a multilayer neural network for the Euro-dollar (EUR/ USD exchange rate
Directory of Open Access Journals (Sweden)
Jaime Alberto Villamil Torres
2010-04-01
Full Text Available A mathematical tool or model for predicting how an economic variable like the exchange rate (relative price between two currencies will respond is a very important need for investors and policy-makers. Most current techniques are based on statistics, particularly linear time series theory. Artificial neural networks (ANNs are mathematical models which try to emulate biological neural networks’ parallelism and nonlinearity; these models have been successfully applied in Economics and Engineering since the 1980s. ANNs appear to be an alternative for modelling the behaviour of financial variables which resemble (as first approximation a random walk. This paper reports the results of using ANNs for Euro/USD exchange rate trading and the usefulness of the algorithm for chemotaxis leading to training networks thereby maximising an objective function re predicting a trader’s profits. JEL: F310, C450.
Multi-layered Security Approaches for a Modular Open Network Architecture-based Satellite
Shirley, Brandon; Young, Quinn; Wegner, Peter; Christensen, Jacob; Janicik, Jeffrey
2014-01-01
A growing trend in satellite development includes shortening the development lifecycle for hardware and software, cost reduction, and promoting reuse for future missions. Department of Defense (DoD) acquisition policies mandate system providers use Open Systems Architecture (OSA) where feasible. Modular Open Network Architecture (MONA) is a subset of OSA and paves the way to achieve cost reduction and reuse during a reduced development lifecycle. MONA approaches provide opportunities to enhan...
Lobato, Justo; Cañizares, Pablo; Rodrigo, Manuel A.; Linares, José J.; Piuleac, Ciprian-George; Curteanu, Silvia
Neural network models represent an important tool of Artificial Intelligence for fuel cell researchers in order to help them to elucidate the processes within the cells, by allowing optimization of materials, cells, stacks, and systems and support control systems. In this work three types of neural networks, that have as common characteristic the supervised learning control (Multilayer Perceptron, Generalized Feedforward Network and Jordan and Elman Network), have been designed to model the performance of a polybenzimidazole-polymer electrolyte membrane fuel cells operating upon a temperature range of 100-175 °C. The influence of temperature of two periods was studied: the temperature in the conditioning period and temperature when the fuel cell was operating. Three inputs variables: the conditioning temperature, the operating temperature and current density were taken into account in order to evaluate their influence upon the potential, the cathode resistance and the ohmic resistance. The Multilayer Perceptron model provides good predictions for different values of operating temperatures and potential and, hence, it is the best choice among the study models, recommended to investigate the influence of process variables of PEMFCs.
Hierarchical Neural Network Structures for Phoneme Recognition
Vasquez, Daniel; Minker, Wolfgang
2013-01-01
In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.
Artificial neural networks (ANN: prediction of sensory measurements from instrumental data
Directory of Open Access Journals (Sweden)
Naiara Barbosa Carvalho
2013-12-01
Full Text Available The objective of this study was to predict by means of Artificial Neural Network (ANN, multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters. Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.
Directory of Open Access Journals (Sweden)
Mustafa Yıldız
2012-08-01
Full Text Available Introduction: We aimed to develop a classification method to discriminate ventricular septal defect and atrial septal defect by using severalhemodynamic parameters.Patients and Methods: Forty three patients (30 atrial septal defect, 13 ventricular septal defect; 26 female, 17 male with documentedhemodynamic parameters via cardiac catheterization are included to study. Such parameters as blood pressure values of different areas,gender, age and Qp/Qs ratios are used for classification. Parameters, we used in classification are determined by divergence analysismethod. Those parameters are; i pulmonary artery diastolic pressure, ii Qp/Qs ratio, iii right atrium pressure, iv age, v pulmonary arterysystolic pressure, vi left ventricular sistolic pressure, vii aorta mean pressure, viii left ventricular diastolic pressure, ix aorta diastolicpressure, x aorta systolic pressure. Those parameters detected from our study population, are uploaded to multi-layered artificial neuralnetwork and the network was trained by genetic algorithm.Results: Trained cluster consists of 14 factors (7 atrial septal defect and 7 ventricular septal defect. Overall success ratio is 79.2%, andwith a proper instruction of artificial neural network this ratio increases up to 89%.Conclusion: Parameters, belonging to artificial neural network, which are needed to be detected by the investigator in classical methods,can easily be detected with the help of genetic algorithms. During the instruction of artificial neural network by genetic algorithms, boththe topology of network and factors of network can be determined. During the test stage, elements, not included in instruction cluster, areassumed as in test cluster, and as a result of this study, we observed that multi-layered artificial neural network can be instructed properly,and neural network is a successful method for aimed classification.
Neural Networks for Non-linear Control
DEFF Research Database (Denmark)
Sørensen, O.
1994-01-01
This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....
Directory of Open Access Journals (Sweden)
Umut Okkan
2011-07-01
Full Text Available Recently, Artificial Neural Networks (ANN, which is mathematical modelingtools inspired by the properties of the biological neural system, has been typically used inthe studies of hydrological time series modeling. These modeling studies generally includethe standart feed forward backpropagation (FFBP algorithms such as gradient-descent,gradient-descent with momentum rate and, conjugate gradient etc. As the standart FFBPalgorithms have some disadvantages relating to the time requirement and slowconvergency in training, Newton and Levenberg-Marquardt algorithms, which arealternative approaches to standart FFBP algorithms, were improved and also used in theapplications. In this study, an application of Levenberg-Marquardt algorithm based ANN(LM-ANN for the modeling of monthly inflows of Demirkopru Dam, which is located inthe Gediz basin, was presented. The LM-ANN results were also compared with gradientdescentwith momentum rate algorithm based FFBP model (GDM-ANN. When thestatistics of the long-term and also seasonal-term outputs are compared, it can be seen thatthe LM-ANN model that has been developed, is more sensitive for prediction of theinflows. In addition, LM-ANN approach can be used for modeling of other hydrologicalcomponents in terms of a rapid assessment and its robustness.
Aitken-based acceleration methods for assessing convergence of multilayer neural networks.
Pilla, R S; Kamarthi, S V; Lindsay, B G
2001-01-01
This paper first develops the ideas of Aitken delta(2) method to accelerate the rate of convergence of an error sequence (value of the objective function at each step) obtained by training a neural network with a sigmoidal activation function via the backpropagation algorithm. The Aitken method is exact when the error sequence is exactly geometric. However, theoretical and empirical evidence suggests that the best possible rate of convergence obtainable for such an error sequence is log-geometric. This paper develops a new invariant extended-Aitken acceleration method for accelerating log-geometric sequences. The resulting accelerated sequence enables one to predict the final value of the error function. These predictions can in turn be used to assess the distance between the current and final solution and thereby provides a stopping criterion for a desired accuracy. Each of the techniques described is applicable to a wide range of problems. The invariant extended-Aitken acceleration approach shows improved acceleration as well as outstanding prediction of the final error in the practical problems considered.
Dual adaptive dynamic control of mobile robots using neural networks.
Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato
2009-02-01
This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.
Classifying epilepsy diseases using artificial neural networks and genetic algorithm.
Koçer, Sabri; Canal, M Rahmi
2011-08-01
In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.
A shallow convolutional neural network for blind image sharpness assessment.
Yu, Shaode; Wu, Shibin; Wang, Lei; Jiang, Fan; Xie, Yaoqin; Li, Leida
2017-01-01
Blind image quality assessment can be modeled as feature extraction followed by score prediction. It necessitates considerable expertise and efforts to handcraft features for optimal representation of perceptual image quality. This paper addresses blind image sharpness assessment by using a shallow convolutional neural network (CNN). The network takes single feature layer to unearth intrinsic features for image sharpness representation and utilizes multilayer perceptron (MLP) to rate image quality. Different from traditional methods, CNN integrates feature extraction and score prediction into an optimization procedure and retrieves features automatically from raw images. Moreover, its prediction performance can be enhanced by replacing MLP with general regression neural network (GRNN) and support vector regression (SVR). Experiments on Gaussian blur images from LIVE-II, CSIQ, TID2008 and TID2013 demonstrate that CNN features with SVR achieves the best overall performance, indicating high correlation with human subjective judgment.
Identifikasi Tanda Tangan Berdasarkan Grid Entropy Menggunakan Multi Layer Perceptron
Directory of Open Access Journals (Sweden)
Muhammad Zidny Naf'an
2017-05-01
Full Text Available Tanda tangan merupakan salah satu bukti pengesahan dokumen yang sering digunakan. Pentingnya mengenal bentuk tanda tangan seseorang diperlukan untuk melakukan verifikasi terhadap dokumen apakah benar yang memberikan tanda tangan adalah orang yang bersangkutan atau orang lain. Pada penelitian ini penulis mendesain sistem identifikasi tanda tangan dengan fitur yang digunakan adalah nilai entropy yang diambil dari grid image (sub-citra suatu citra tanda tangan. Model pelatihan dan pengujian menggunakan multi layer perceptron dan cross validation dengan tiga ukuran grid (4x4, 8x8, dan 16x16 dan dua jenis representasi citra (citra biner dan citra outline. Hasil pengujian terbaik adalah untuk pengujian ukuran grid sebanyak 8x8 dan menggunakan citra outline yaitu dengan tingkat akurasi sebesar 97.78%, nilai korelasi 0.981, dan nilai kappa 0.977.
Yoshida, Yuki; Karakida, Ryo; Okada, Masato; Amari, Shun-ichi
2017-04-01
Weight normalization, a newly proposed optimization method for neural networks by Salimans and Kingma (2016), decomposes the weight vector of a neural network into a radial length and a direction vector, and the decomposed parameters follow their steepest descent update. They reported that learning with the weight normalization achieves better converging speed in several tasks including image recognition and reinforcement learning than learning with the conventional parameterization. However, it remains theoretically uncovered how the weight normalization improves the converging speed. In this study, we applied a statistical mechanical technique to analyze on-line learning in single layer linear and nonlinear perceptrons with weight normalization. By deriving order parameters of the learning dynamics, we confirmed quantitatively that weight normalization realizes fast converging speed by automatically tuning the effective learning rate, regardless of the nonlinearity of the neural network. This property is realized when the initial value of the radial length is near the global minimum; therefore, our theory suggests that it is important to choose the initial value of the radial length appropriately when using weight normalization.
Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network
Directory of Open Access Journals (Sweden)
Mutasem Sh. Alkhasawneh
2013-01-01
Full Text Available Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are three main stages involved in this study. The first stage is the extraction of extra topographic factors. Previous landslide studies had identified mainly six topographic factors. Seven new additional factors have been proposed in this study. They are longitude curvature, tangential curvature, cross section curvature, surface area, diagonal line length, surface roughness, and rugosity. The second stage is the specification of the weight of each factor using two methods. The methods are multilayer perceptron (MLP network classification accuracy and Zhou's algorithm. At the third stage, the factors with higher weights were used to improve the MLP performance. Out of the thirteen factors, eight factors were considered as important factors, which are surface area, longitude curvature, diagonal length, slope angle, elevation, slope aspect, rugosity, and profile curvature. The classification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors.
Verification of the authenticity of handwritten signature using structure neural-network-type OCON
Molina, M. L.; Arias, N. A.; Gualdron, Oscar
2004-10-01
A method in order to carry out the verification of handwritten signatures is described. The method keeps in mind global features and local features that encode the shape and the dynamics of the signatures. Signatures are recorded with a digital tablet that can read the position and pressure of the pen. Input patterns are considered time and space dependent. Before extracting the information of the static features such as total length or height/width ratio, and the dynamic features such as speed or acceleration, the signature is normalized for position, size and orientation using its Fourier Descriptors. The comparison stage is carried out for algorithms of neurals networks. For each one of the sets of features a special two stage Perceptron OCON (one-class-one-network) classification structure has been implemented. In the first stage networks multilayer perceptron with few neurons are used. The classifier combines the decision results of the neural networks and the Euclidean distance obtained using the two feature sets. The results of the first-stage classifier feed a second-stage radial basis function (RBF) neural network structure, which makes the final decision. The entire system was extensively tested, 160 neurals networks has been implemented.
Prediction of caspase cleavage sites using Bayesian bio-basis function neural networks.
Yang, Zheng Rong
2005-05-01
Apoptosis has drawn the attention of researchers because of its importance in treating some diseases through finding a proper way to block or slow down the apoptosis process. Having understood that caspase cleavage is the key to apoptosis, we find novel methods or algorithms are essential for studying the specificity of caspase cleavage activity and this helps the effective drug design. As bio-basis function neural networks have proven to outperform some conventional neural learning algorithms, there is a motivation, in this study, to investigate the application of bio-basis function neural networks for the prediction of caspase cleavage sites. Thirteen protein sequences with experimentally determined caspase cleavage sites were downloaded from NCBI. Bayesian bio-basis function neural networks are investigated and the comparisons with single-layer perceptrons, multilayer perceptrons, the original bio-basis function neural networks and support vector machines are given. The impact of the sliding window size used to generate sub-sequences for modelling on prediction accuracy is studied. The results show that the Bayesian bio-basis function neural network with two Gaussian distributions for model parameters (weights) performed the best and the highest prediction accuracy is 97.15 +/- 1.13%. The package of Bayesian bio-basis function neural network can be obtained by request to the author.
Lin, Wei-Qi; Jiang, Jian-Hui; Zhou, Yan-Ping; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin
2007-01-30
Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks.
Foreground removal from WMAP 5 yr temperature maps using an MLP neural network
DEFF Research Database (Denmark)
Nørgaard-Nielsen, Hans Ulrik
2010-01-01
CMB signal makes it essential to minimize the systematic errors in the CMB temperature determinations. Methods. The feasibility of using simple neural networks to extract the CMB signal from detailed simulated data has already been demonstrated. Here, simple neural networks are applied to the WMAP 5...... yr temperature data without using any auxiliary data. Results. A simple multilayer perceptron neural network with two hidden layers provides temperature estimates over more than 75 per cent of the sky with random errors significantly below those previously extracted from these data. Also......, the systematic errors, i.e. errors correlated with the Galactic foregrounds, are very small. Conclusions. With these results the neural network method is well prepared for dealing with the high-quality CMB data from the ESA Planck Surveyor satellite. © ESO, 2010....
Putra, J. C. P.; Safrilah
2017-06-01
Artificial neural network approaches are useful to solve many complicated problems. It solves a number of problems in various areas such as engineering, medicine, business, manufacturing, etc. This paper presents an application of artificial neural network to predict a runway capacity at Juanda International Airport. An artificial neural network model of backpropagation and multi-layer perceptron is adopted to this research to learning process of runway capacity at Juanda International Airport. The results indicate that the training data is successfully recognizing the certain pattern of runway use at Juanda International Airport. Whereas, testing data indicate vice versa. Finally, it can be concluded that the approach of uniformity data and network architecture is the critical part to determine the accuracy of prediction results.
Energy Technology Data Exchange (ETDEWEB)
Marko, Daniel
2010-11-25
In the present work, the implications of ion irradiation on the magnetostatic and dynamic properties of soft magnetic Py/Ta (Py=Permalloy: Ni{sub 80}Fe{sub 20}) single and multilayer films have been investigated with the main objective of finding a way to determine their saturation magnetization. Both polar magneto-optical Kerr effect (MOKE) and vector network analyzer ferromagnetic resonance (VNA-FMR) measurements have proven to be suitable methods to determine {mu}{sub 0}M{sub S}, circumventing the problem of the unknown effective magnetic volume that causes conventional techniques such as SQUID or VSM to fail. Provided there is no perpendicular anisotropy contribution in the samples, the saturation magnetization can be determined even in the case of strong interfacial mixing due to an inherently high number of Py/Ta interfaces and/or ion irradiation with high fluences. Another integral part of this work has been to construct a VNA-FMR spectrometer capable of performing both azimuthal and polar angle-dependent measurements using a magnet strong enough to saturate samples containing iron. Starting from scratch, this comprised numerous steps such as developing a suitable coplanar waveguide design, and writing the control, evaluation, and fitting software. With both increasing ion fluence and number of Py/Ta interfaces, a decrease of saturation magnetization has been observed. In the case of the 10 x Py samples, an immediate decrease of {mu}{sub 0}M{sub S} already sets in at small ion fluences. However, for the 1 x Py and 5 x Py samples, the saturation magnetization remains constant up to a certain ion fluence, but then starts to rapidly decrease. Ne ion irradiation causes a mixing and broadening of the interfaces. Thus, the Py/Ta stacks undergo a transition from being polycrystalline to amorphous at a critical fluence depending on the number of interfaces. The saturation magnetization is found to vanish at a Ta concentration of about 10-15 at.% in the Py layers
Thrips (Thysanoptera) identification using artificial neural networks.
Fedor, P; Malenovský, I; Vanhara, J; Sierka, W; Havel, J
2008-10-01
We studied the use of a supervised artificial neural network (ANN) model for semi-automated identification of 18 common European species of Thysanoptera from four genera: Aeolothrips Haliday (Aeolothripidae), Chirothrips Haliday, Dendrothrips Uzel, and Limothrips Haliday (all Thripidae). As input data, we entered 17 continuous morphometric and two qualitative two-state characters measured or determined on different parts of the thrips body (head, pronotum, forewing and ovipositor) and the sex. Our experimental data set included 498 thrips specimens. A relatively simple ANN architecture (multilayer perceptrons with a single hidden layer) enabled a 97% correct simultaneous identification of both males and females of all the 18 species in an independent test. This high reliability of classification is promising for a wider application of ANN in the practice of Thysanoptera identification.
Neural networks for process control and optimization: two industrial applications.
Bloch, Gérard; Denoeux, Thierry
2003-01-01
The two most widely used neural models, multilayer perceptron (MLP) and radial basis function network (RBFN), are presented in the framework of system identification and control. The main steps for building such nonlinear black box models are regressor choice, selection of internal architecture, and parameter estimation. The advantages of neural network models are summarized: universal approximation capabilities, flexibility, and parsimony. Two applications are described in steel industry and water treatment, respectively, the control of alloying process in a hot dipped galvanizing line and the control of a coagulation process in a drinking water treatment plant. These examples highlight the interest of neural techniques, when complex nonlinear phenomena are involved, but the empirical knowledge of control operators can be learned.
A novel MLP network implementation in CMOL technology
Directory of Open Access Journals (Sweden)
Abdalhossein Rezai
2014-09-01
Full Text Available Hybrid CMOS/nanodevice technology is a well-known candidate to extend the exponential Moor-Law progress of microelectronics beyond the 10-nm frontier. This paper presents and evaluates a novel method for synaptic weights implementation of artificial neural networks in CMOL technology, a hybrid CMOS/nanodevice technology. In this novel method, the analog property of the I–V characteristic of the nanodevice is utilized to implement each neuromorphic synaptic weight. Each synaptic weight is also implemented by using one nanodevice instead of several nanodevices. Moreover, the proposed method is applied to the multilayer perceptron (MLP network in CMOL technology. Our analysis shows that the power consumption and speed are effectively improved in the proposed method compared to other methods at the expense of a reasonable overhead defect tolerance.
Energy Technology Data Exchange (ETDEWEB)
Guia, Jose G.C. da; Araujo, Adevid L. de [Universidade Federal de Campina Grande, PB (Brazil). Dept. de Engenharia Mecanica; Irmao, Marcos A. da Silva [Universidade Federal de Campina Grande, PB (Brazil). Dept. de Engenharia de Processos; Silva, Antonio A. [Universidade Federal de Campina Grande, PB (Brazil). Dept. de Engenharia Mecanica
2003-07-01
The condition monitoring and diagnostic of structural faults in pipelines are an important problem for the petroleum's industry, being necessary to develop supervisory systems for detection, prediction and evaluation of a fault in the pipelines to avoid environmental and financial damages. In this work, three types of Artificial Neural Networks (ANNs) are reviewed and used to detect and locate a fault in a simulated pipe. The simulated pipe was modeled through the Finite Elements Method. In Neural Networks' analysis, the first six natural frequencies of the pipe are used as networks' inputs. The used ANNs were the Multi-Layer Perceptron Network with backpropagation, the Probabilistic Neural Network and the Generalized Regression Neural Network. After the analysis, it was concluded that the ANN are a good computational tool in problems of faults detection on pipelines with a great precision. In the localization of the faults were obtained errors smaller than 5%. (author)
Comparing various artificial neural network types for water temperature prediction in rivers
Piotrowski, Adam P.; Napiorkowski, Maciej J.; Napiorkowski, Jaroslaw J.; Osuch, Marzena
2015-10-01
A number of methods have been proposed for the prediction of streamwater temperature based on various meteorological and hydrological variables. The present study shows a comparison of few types of data-driven neural networks (multi-layer perceptron, product-units, adaptive-network-based fuzzy inference systems and wavelet neural networks) and nearest neighbour approach for short time streamwater temperature predictions in two natural catchments (mountainous and lowland) located in temperate climate zone, with snowy winters and hot summers. To allow wide applicability of such models, autoregressive inputs are not used and only easily available measurements are considered. Each neural network type is calibrated independently 100 times and the mean, median and standard deviation of the results are used for the comparison. Finally, the ensemble aggregation approach is tested. The results show that simple and popular multi-layer perceptron neural networks are in most cases not outperformed by more complex and advanced models. The choice of neural network is dependent on the way the models are compared. This may be a warning for anyone who wish to promote own models, that their superiority should be verified in different ways. The best results are obtained when mean, maximum and minimum daily air temperatures from the previous days are used as inputs, together with the current runoff and declination of the Sun from two recent days. The ensemble aggregation approach allows reducing the mean square error up to several percent, depending on the case, and noticeably diminishes differences in modelling performance obtained by various neural network types.
Identification of Non-Linear Structures using Recurrent Neural Networks
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.
1995-01-01
Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....
Identification of Non-Linear Structures using Recurrent Neural Networks
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.
Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....
Predicting wettability behavior of fluorosilica coated metal surface using optimum neural network
Taghipour-Gorjikolaie, Mehran; Valipour Motlagh, Naser
2018-02-01
The interaction between variables, which are effective on the surface wettability, is very complex to predict the contact angles and sliding angles of liquid drops. In this paper, in order to solve this complexity, artificial neural network was used to develop reliable models for predicting the angles of liquid drops. Experimental data are divided into training data and testing data. By using training data and feed forward structure for the neural network and using particle swarm optimization for training the neural network based models, the optimum models were developed. The obtained results showed that regression index for the proposed models for the contact angles and sliding angles are 0.9874 and 0.9920, respectively. As it can be seen, these values are close to unit and it means the reliable performance of the models. Also, it can be inferred from the results that the proposed model have more reliable performance than multi-layer perceptron and radial basis function based models.
Foreground removal from CMB temperature maps using an MLP neural network
DEFF Research Database (Denmark)
Nørgaard-Nielsen, Hans Ulrik; Jørgensen, H.E.
2008-01-01
CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the Galactic foregrounds simple power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting...... the CMB temperature signal from the combined signal CMB and the foregrounds has been investigated. As a specific example, we have analysed simulated data, as expected from the ESA Planck CMB mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates over...... more than 80 per cent of the sky that are to a high degree uncorrelated with the foreground signals. A single network will be able to cover the dynamic range of the Planck noise level over the entire sky....
Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network
Directory of Open Access Journals (Sweden)
Kemal Fidanboylu
2009-09-01
Full Text Available Artificial neural network (ANN based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multilayer Perceptron (MLP with different training algorithms, Radial Basis Function (RBF network and General Regression Neural Network (GRNN are used as ANN models in this work. All of these models can predict the sensor responses with considerable errors. RBF has the best performance with the smallest mean square error (MSE values of training and test results. Among the MLP algorithms and GRNN the Levenberg-Marquardt algorithm has good results. These models successfully predict the sensor responses, hence ANNs can be used as useful tool in the design of more robust fiber optic sensors.
Lithofacies classification of the Barnett Shale gas reservoir using neural network
Aliouane, Leila; Ouadfeul, Sid-Ali
2017-04-01
Here, we show the contribution of the artificial intelligence such as neural network to predict the lithofacies in the lower Barnett shale gas reservoir. The Multilayer Perceptron (MLP) neural network with Hidden Weight Optimization Algorithm is used. The input is raw well-logs data recorded in a horizontal well drilled in the Lower Barnett shale formation, however the output is the concentration of the Clay and the Quartz calculated using the ELAN model and confirmed with the core rock measurement. After training of the MLP machine weights of connection are calculated, the raw well-logs data of two other horizontal wells drilled in the same reservoir are propagated though the neural machine and an output is calculated. Comparison between the predicted and measured clay and Quartz concentrations in these two horizontal wells shows the ability of neural network to improve shale gas reservoirs characterization.
Implementation of a spike-based Perceptron learning rule using TiO2-x memristors
Directory of Open Access Journals (Sweden)
Hesham eMostafa
2015-10-01
Full Text Available Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic 'cognitive' capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO$_{2-x}$ memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episode.
Wang, Fu; Liu, Bo; Zhang, Lijia; Zhang, Qi; Tian, Qinghua; Tian, Feng; Rao, Lan; Xin, Xiangjun
2017-07-01
Elastic software-defined optical networks greatly improve the flexibility of the optical switching network while it has brought challenges to the routing and spectrum assignment (RSA). A multilayer virtual topology model is proposed to solve RSA problems. Two RSA algorithms based on the virtual topology are proposed, which are the ant colony optimization (ACO) algorithm of minimum consecutiveness loss and the ACO algorithm of maximum spectrum consecutiveness. Due to the computing power of the control layer in the software-defined network, the routing algorithm avoids the frequent link-state information between routers. Based on the effect of the spectrum consecutiveness loss on the pheromone in the ACO, the path and spectrum of the minimal impact on the network are selected for the service request. The proposed algorithms have been compared with other algorithms. The results show that the proposed algorithms can reduce the blocking rate by at least 5% and perform better in spectrum efficiency. Moreover, the proposed algorithms can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness.
Directory of Open Access Journals (Sweden)
José Antonio Vázquez-López
2012-06-01
Full Text Available In this article the Perceptron artificial neural network is applied as a classifier system of out of control points, in the field of contrlol chart for individual measurements. The use of geometric properties of the Perceptron as a training method is introduced, replacing in consequence to the known training methods. Some experiments with numerical databases contaminated with altered data in global average was performed, and the ability of the detection of \\out of control points" of the control chart with the implementation of the Perceptron trained by geometry was compared. The results reveal greater capacity in the Perceptron. This approach can be generalized to other types of control charts and patterns of natural and special variation, not considered in this research. // RESUMEN: En este artículo se aplica la red neuronal artificial Perceptrón como sistema clasificador de puntos fuera de control en el ámbito de la carta de control de mediciones individuales. Se introduce el uso de las propiedades geométricas de la Perceptrón como método de entrenamiento para sustituir, en consecuencia, a los métodos de entrenamiento conocidos. Se experimentó con bases de datos numéricas contaminadas con datos alterados en su media global y se comparó la capacidad de la detección de puntos fuera de control de la carta de control con la aplicación de la Perceptrón entrenada por geometría. Los resultados revelan mayor capacidad en la Perceptrón en diferentes porcentajes de contaminación. Esta propuesta puede ser generalizada a otros tipos de gráficos de control y a patrones de variación especial y natural no considerados en esta investigación.
Directory of Open Access Journals (Sweden)
Palukuru NAGENDRA
2010-12-01
Full Text Available In this study, the use of artificial neural network (ANN based model, multi-layer perceptron (MLP network, to compute the transfer capabilities in a multi-area power system was explored. The input for the ANN is load status and the outputs are the transfer capability among the system areas, voltage magnitudes and voltage angles at concerned buses of the areas under consideration. The repeated power flow (RPF method is used in this paper for calculating the power transfer capability, voltage magnitudes and voltage angles necessary for the generation of input-output patterns for training the proposed MLP neural network. Preliminary investigations on a three area 30-bus system reveal that the proposed model is computationally faster than the conventional method.
Directory of Open Access Journals (Sweden)
Mahmoud Akbarian
2015-07-01
Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9%, 80.0%, and 94.1% respectively and for the total data were 97.3%, 93.5%, and 99.0% respectively. Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP neural network with scaled conjugate gradient (trainscg back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth among pregnant women with lupus by using identified effective variables.
An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems
Ranganayaki, V.; Deepa, S. N.
2016-01-01
Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature. PMID:27034973
Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China
Directory of Open Access Journals (Sweden)
C. W. Dawson
2002-01-01
Full Text Available While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP, and the radial basis function network (RBF. Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting
An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.
Ranganayaki, V; Deepa, S N
2016-01-01
Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.
significant wave height estimation from wind speed by perceptron Kalman filtering? by A Altunkaynak and M Ozger, Ocean Engineering, 2004, 31, 1245-1255 Discussion by S Mandal* Ocean Engineering Division, National Institute of Oceanography, Dona Paula... of neural network in the study of wave transformation. REFERENCES Deo, M.C. and Naidu, C.S., 1999. Real time wave forecasting using neural networks. Ocean Engineering, 26, 191-203. Mandal, S and Prabaharan, N, 2003. An overview of the numerical...
Financial time series prediction using spiking neural networks.
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David Reid
Full Text Available In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.
Prediction of Bladder Cancer Recurrences Using Artificial Neural Networks
Zulueta Guerrero, Ekaitz; Garay, Naiara Telleria; Lopez-Guede, Jose Manuel; Vilches, Borja Ayerdi; Iragorri, Eider Egilegor; Castaños, David Lecumberri; de La Hoz Rastrollo, Ana Belén; Peña, Carlos Pertusa
Even if considerable advances have been made in the field of early diagnosis, there is no simple, cheap and non-invasive method that can be applied to the clinical monitorisation of bladder cancer patients. Moreover, bladder cancer recurrences or the reappearance of the tumour after its surgical resection cannot be predicted in the current clinical setting. In this study, Artificial Neural Networks (ANN) were used to assess how different combinations of classical clinical parameters (stage-grade and age) and two urinary markers (growth factor and pro-inflammatory mediator) could predict post surgical recurrences in bladder cancer patients. Different ANN methods, input parameter combinations and recurrence related output variables were used and the resulting positive and negative prediction rates compared. MultiLayer Perceptron (MLP) was selected as the most predictive model and urinary markers showed the highest sensitivity, predicting correctly 50% of the patients that would recur in a 2 year follow-up period.
Dynamic model of a PEM electrolyser based on artificial neural networks
Energy Technology Data Exchange (ETDEWEB)
Chavez-Ramirez, A.U.; Munoz-Guerrero, R.; Sanchez-Huerta, V.; Ramirez-Arredondo, Juan M.; Ornelas, R.; Arriaga, L.G.; Siracusano, S.; Brunaccini, G.; Napoli, G.; Antonucci, V.; Arico, A.S.
2011-01-15
Hydrogen production by electrolysis is emerging as a promising way to meet future fuel demand, and developing models capable of simulating the operation of electrolysis devices is indispensable to efficiently design power generation systems, reduce manufacturing costs and save resources. The nonlinear nature of the Artificial Neural Network (ANN) plays a key role in developing models predicting the performance of complex systems. The behaviour of a Polymer Electrolyte Membrane (PEM) Electrolyser of three cell stack was modelled successfully using a Multilayer Perceptron Network (MLP). This dynamic model was trained to learn the internal relationships of this electrolysis device and predict its behaviour without physical equations. Electric current supply and operation temperature were used as input vector able to predict each cell voltage behaviour. An accuracy (< 2%) was reached after comparing the single cell performance of the real electrolyser versus the ANN based model. This predictive model can be used as a virtual device into a more complex energy system.
Directory of Open Access Journals (Sweden)
Petr Maca
2014-01-01
Full Text Available The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.
Energy Technology Data Exchange (ETDEWEB)
Flores, J.L. [Dpto de Ingeniería Eléctrica y Térmica, Universidad de Huelva (Spain); Martel, I. [Dpto de Física Aplicada, Universidad de Huelva (Spain); CERN, ISOLDE, CH 1211 Geneva, 23 (Switzerland); Jiménez, R. [Dpto de Ingeniería Electrónica, Sist. Informáticos y Automática, Universidad de Huelva (Spain); Galán, J., E-mail: jgalan@diesia.uhu.es [Dpto de Ingeniería Electrónica, Sist. Informáticos y Automática, Universidad de Huelva (Spain); Salmerón, P. [Dpto de Ingeniería Eléctrica y Térmica, Universidad de Huelva (Spain)
2016-09-11
The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from {sup 12}C up to {sup 84}Kr, yielding higher discrimination rates than any other previously reported.
Neural Network on Photodegradation of Octylphenol using Natural and Artificial UV Radiation
Directory of Open Access Journals (Sweden)
Lorentz JÄNTSCHI
2011-09-01
Full Text Available The present paper comes up with an experimental design meant to point out the factors interferingin octylphenol’s degradation in surface waters under solar radiation, underlining each factor’sinfluence on the process observable (concentration of p-octylphenol. Multiple linear regressionanalysis and artificial neural network (Multi-Layer Perceptron type were applied in order to obtaina mathematical model capable to explain the action of UV-light upon synthetic solutions of OP inultra-pure water (MilliQ type. Neural network proves to be the most efficient method in predictingthe evolution of OP concentration during photodegradation process. Thus, determination in neuralnetwork’s case has almost double value versus the regression analysis.
Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling.
Ntalampiras, Stavros
2015-09-01
This paper proposes a holistic modeling scheme for fault identification in distributed sensor networks. The proposed scheme is based on modeling the relationship between two datastreams by means of a hidden Markov model (HMM) trained on the parameters of linear time-invariant dynamic systems, which estimate the specific relationship over consecutive time windows. Every system state, including the nominal one, is represented by an HMM and the novel data are categorized according to the model producing the highest likelihood. The system is able to understand whether the novel data belong to the fault dictionary, are fault-free, or represent a new fault type. We extensively evaluated the discrimination capabilities of the proposed approach and contrasted it with a multilayer perceptron using data coming from the Barcelona water distribution network. Nine system states are present in the dataset and the recognition rates are provided in the confusion matrix form.
Neural networks and principle component analysis approaches to predict pile capacity in sand
Directory of Open Access Journals (Sweden)
Benali A
2018-01-01
Full Text Available Determination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development of a neural network model that provides a more accurate prediction of axial load bearing capacity based on the SPT test data. The retropropagation multi-layer perceptron with Bayesian regularization (RB was used in this model. This was established by the incorporation of about 260 data, obtained from the published literature, of experimental programs for large displacement driven piles. The PCA method is proposed for compression and suppression of the correlation between these data. This will improve the performance of generalization of the model.
Directory of Open Access Journals (Sweden)
Володимир Леонідович Бакулевський
2015-11-01
Full Text Available A model for the calculation of technical losses of electricity in the air lines with voltage of 6-35 kV based on neural networks with due regard to meteorological factors has been worked out; the main components of the model have been considered and researched; the best ones being selected, that is: a set of input variables, volume of excerpts (training, control and testing, architecture and network activation function, network learning algorithm was proposed. Simulation was conducted in OS STATISTICA Neural Networks. Input variables are: transmission line (TL active load, transmission line rated voltage, transmission line cross section and length of wire, average air temperature, wind speed, rainfall availability; output variable – that is technical losses in electric transmission line. To select the optimal input vector model the data selection methods were used: variables testing using trial and error method, variables stepped inclusion and exclusion algorithm. It has been proved that the most important variables are TL active load and average air temperature. all input variables under review should be included in the created artificial neural network (ANN. It was determined that the optimal volume for ANN training set given parameters made 250 observations, control and test excerpts volume were respectively 250 and 332 observations. It has been proved that the best type of architecture is multilayer perceptron ANN that being compared to radial basis functions and generalized regression network is characterized by minimal errors and complexity of the network. ANN of the following architecture: multilayer perceptron, 7 neurons in the input layer, 5 neurons in the hidden layer, 1 output neuron, logistics as activation function – has been taken optimal
Directory of Open Access Journals (Sweden)
Xiaohu Li
2013-01-01
Full Text Available Being difficult to attain the precise mathematical models, traditional control methods such as proportional integral (PI and proportional integral differentiation (PID cannot meet the demands for real time and robustness when applied in some nonlinear systems. The neural network controller is a good replacement to overcome these shortcomings. However, the performance of neural network controller is directly determined by neural network model. In this paper, a new neural network model is constructed with a structure topology between the regular and random connection modes based on complex network, which simulates the brain neural network as far as possible, to design a better neural network controller. Then, a new controller is designed under small-world neural network model and is investigated in both linear and nonlinear systems control. The simulation results show that the new controller basing on small-world network model can improve the control precision by 30% in the case of system with random disturbance. Besides the good performance of the new controller in tracking square wave signals, which is demonstrated by the experiment results of direct drive electro-hydraulic actuation position control system, it works well on anti-interference performance.
Perrone, Antonio L.; Basti, Gianfranco
1995-04-01
With respect to Rosenblatt linear perceptron, two classical limitation theorems demonstrated by M. Minsky and S. Papert are discussed. These two theorems, `(Psi) One-in-a-box' and `(Psi) Parity,' ultimately concern the intrinsic limitations of parallel calculations in pattern recognition problems. We demonstrate a possible solution of these limitation problems by substituting the static definition of characteristic functions and of their domains in the `geometrical' perceptron, with their dynamic definition. This dynamic consists in the mutual redefinition of the characteristic function and of its domain depending on the matching with the input.
Neural networks forecast in small catchments with transfer of network parameters
Maca, P.; Havlicek, V.; Hermanovsky, M.; Horacek, S.; Pech, P.
2009-04-01
This contribution deals with neural network approach for short term forecast on small catchments. The applied methodology is based on theory of multilayer perceptron (MLP), feed forward neural network with back propagation optimization procedure was tested in order to explore the possibilities to transfer parameters between different catchments. Supervised optimization of network parameters and structure was investigated. A software tool was created for these research and operative purposes. The hourly discharges and rainfall data of real flood events served as an input to MLP. Seven catchments with areas, which range from 10 to 250 square kilometres and which are situated in the east part of the Czech Republic, were selected. The input data were normalized by parametric method. Variable configuration of neural network was tested in number of modes represented by different combination of learning and testing data sets. The analysis focuses on ability of the model to forecast the flood event with different peak discharge magnitudes. This should be achieved in both application steps - MLP learning and testing within given catchment and in step of parameter transfer of well learned network to another catchment. The length of prediction ranged from one hour to six hours ahead. The results showed that the model is capable to provide satisfying short term discharge forecast for the most of studied cases, including successful parameter transfer among different catchments. This was accomplished by using optimization of parameters which determine not only the structure and behaviour of applied network but also the transformation of input data.
Bruno, L. S.; Rodrigo, B. P.; Lucio, A. de C. Jorge
2016-10-01
This paper presents a system developed by an application of a neural network Multilayer Perceptron for drone acquired agricultural image segmentation. This application allows a supervised user training the classes that will posteriorly be interpreted by neural network. These classes will be generated manually with pre-selected attributes in the application. After the attribute selection a segmentation process is made to allow the relevant information extraction for different types of images, RGB or Hyperspectral. The application allows extracting the geographical coordinates from the image metadata, geo referencing all pixels on the image. In spite of excessive memory consume on hyperspectral images regions of interest, is possible to perform segmentation, using bands chosen by user that can be combined in different ways to obtain different results.
Johnson, Cameron; Venayagamoorthy, Ganesh Kumar; Mitra, Pinaki
2009-01-01
The application of a spiking neural network (SNN) and a multi-layer perceptron (MLP) for online identification of generator dynamics in a multimachine power system are compared in this paper. An integrate-and-fire model of an SNN which communicates information via the inter-spike interval is applied. The neural network identifiers are used to predict the speed and terminal voltage deviations one time-step ahead of generators in a multimachine power system. The SNN is developed in two steps: (i) neuron centers determined by offline k-means clustering and (ii) output weights obtained by online training. The sensitivity of the SNN to the neuron centers determined in the first step is evaluated on generators of different ratings and parameters. Performances of the SNN and MLP are compared to evaluate robustness on the identification of generator dynamics under small and large disturbances, and to illustrate that SNNs are capable of learning nonlinear dynamics of complex systems.
Jung, Jun-Young; Heo, Wonho; Yang, Hyundae; Park, Hyunsub
2015-01-01
An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases. PMID:26528986
Directory of Open Access Journals (Sweden)
Jun-Young Jung
2015-10-01
Full Text Available An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases.
Jung, Jun-Young; Heo, Wonho; Yang, Hyundae; Park, Hyunsub
2015-10-30
An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases.
Directory of Open Access Journals (Sweden)
Abdulaziz Y. Barnawi
2016-01-01
Full Text Available Maximizing wireless sensor networks (WSNs lifetime is a primary objective in the design of these networks. Intelligent energy management models can assist designers to achieve this objective. These models aim to reduce the number of selected sensors to report environmental measurements and, hence, achieve higher energy efficiency while maintaining the desired level of accuracy in the reported measurement. In this paper, we present a comparative study of three intelligent models based on Naive Bayes, Multilayer Perceptrons (MLP, and Support Vector Machine (SVM classifiers. Simulation results show that Linear-SVM selects sensors that produce higher energy efficiency compared to those selected by MLP and Naive Bayes for the same WSNs Lifetime Extension Factor.
Near and long-term load prediction using radial basis function networks
Energy Technology Data Exchange (ETDEWEB)
Hancock, M.F. [Rollins College, Winter Park, FL (United States)
1995-12-31
A number of researchers have investigated the application of multi-layer perceptrons (MLP`s), a variety of neural network, to the problem of short-term load forecasting for electric utilities (e.g., Rahman & Hazin, IEEE Trans. Power Systems, May 1993). {open_quotes}Short-term{close_quotes} in this context typically means {open_quotes}next day{close_quotes}. These forecasts have been based upon previous day actual loads and meteorological factors (e.g., max-min temperature, relative humidity). We describe the application of radial basis function networks (RBF`s) to the {open_quotes}long-term{close_quotes} (next year) load forecasting problem. The RBF network performs a two-stage classification based upon annual average loads and meteorological data. During stage 1, discrete classification is performed using radius-limited elements. During stage 2, a multi-layer perceptron may be applied. The quantized output is used to correct a prediction template. The stage 1 classifier is trained by maximizing an objective function (the {open_quotes}disambiguity{close_quotes}). The stage 2 MLP`s are trained by standard back-propagation. This work uses 12 months of hourly meteorological data, and the corresponding hourly load data for both commercial and residential feeders. At the current stage of development, the RBF machine can train on 20% of the weather/load data (selected by simple linear sampling), and estimate the hourly load for an entire year (8,760 data points) with 9.1% error (RMS, relative to daily peak load). (By comparison, monthly mean profiles perform at c. 12% error.) The best short-term load forecasters operate in the 2% error range. The current system is an engineering prototype, and development is continuing.
Energy Technology Data Exchange (ETDEWEB)
Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br
2009-07-01
This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)
Poledna, S.; Molina-Borboa, J.L.; Martínez-Jaramillo, S.; van der Leij, M.; Thurner, S.
2015-01-01
The inability to see and quantify systemic financial risk comes at an immense social cost. Systemic risk in the financial system arises to a large extent as a consequence of the interconnectedness of its institutions, which are linked through networks of different types of financial contracts, such
Directory of Open Access Journals (Sweden)
Andres Schmidt
2014-01-01
Full Text Available Wind turbines play an important role in providing electrical energy for an ever-growing demand. Due to climate change driven by anthropogenic emissions of greenhouse gases, the exploration and use of sustainable energy sources is essential with wind energy covering a significant portion. Data of existing wind turbines is needed to reduce the uncertainty of model predictions of future energy yields for planned wind farms. Due to maintenance routines and technical issues, data gaps of reference wind parks are unavoidable. Here, we present real-world case studies using multilayer perceptron networks and radial basis function networks to reproduce electrical energy outputs of wind turbines at 3 different locations in Germany covering a range of landscapes with varying topographic complexity. The results show that the energy output values of the turbines could be modeled with high correlations ranging from 0.90 to 0.99. In complex terrain, the RBF networks outperformed the MLP networks. In addition, rare extreme values were better captured by the RBF networks in most cases. By using wind meteorological variables and operating data recorded by the wind turbines in addition to the daily energy output values, the error could be further reduced to more than 20%.
Paganini, Michela; de Oliveira, Luke; Nachman, Benjamin
2018-01-01
The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements. The most computationally expensive step in the simulation pipeline of a typical experiment at the Large Hadron Collider (LHC) is the detailed modeling of the full complexity of physics processes that govern the motion and evolution of particle showers inside calorimeters. We introduce CaloGAN, a new fast simulation technique based on generative adversarial networks (GANs). We apply these neural networks to the modeling of electromagnetic showers in a longitudinally segmented calorimeter and achieve speedup factors comparable to or better than existing full simulation techniques on CPU (100 ×-1000 × ) and even faster on GPU (up to ˜105× ). There are still challenges for achieving precision across the entire phase space, but our solution can reproduce a variety of geometric shower shape properties of photons, positrons, and charged pions. This represents a significant stepping stone toward a full neural network-based detector simulation that could save significant computing time and enable many analyses now and in the future.
Neurodynamics of learning and network performance
Wilson, Charles L.; Blue, James L.; Omidvar, Omid M.
1997-07-01
A simple dynamic model of a neural network is presented. Using the dynamic model of a neural network, we improve the performance of a three-layer multilayer perceptron (MLP). The dynamic model of a MLP is used to make fundamental changes in the network optimization strategy. These changes are: neuron activation functions are used, which reduces the probability of singular Jacobians; successive regularization is used to constrain the volume of the weight space being minimized; Boltzmann pruning is used to constrain the dimension of the weight space; and prior class probabilities are used to normalize all error calculations, so that statistically significant samples of rare but important classes can be included without distortion of the error surface. All four of these changes are made in the inner loop of a conjugate gradient optimization iteration and are intended to simplify the training dynamics of the optimization. On handprinted digits and fingerprint classification problems, these modifications improve error-reject performance by factors between 2 and 4 and reduce network size by 40 to 60%.
A novel nature inspired firefly algorithm with higher order neural network: Performance analysis
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Janmenjoy Nayak
2016-03-01
Full Text Available The applications of both Feed Forward Neural network and Multilayer perceptron are very diverse and saturated. But the linear threshold unit of feed forward networks causes fast learning with limited capabilities, while due to multilayering, the back propagation of errors exhibits slow training speed in MLP. So, a higher order network can be constructed by correlating between the input variables to perform nonlinear mapping using the single layer of input units for overcoming the above drawbacks. In this paper, a Firefly based higher order neural network has been proposed for data classification for maintaining fast learning and avoids the exponential increase of processing units. A vast literature survey has been conducted to review the state of the art of the previous developed models. The performance of the proposed method has been tested with various benchmark datasets from UCI machine learning repository and compared with the performance of other established models. Experimental results imply that the proposed method is fast, steady, reliable and provides better classification accuracy than others.
A new approach for sizing stand alone photovoltaic systems based in neural networks
Energy Technology Data Exchange (ETDEWEB)
Hontoria, L.; Aguilera, J. [Universidad de Jaen, Dept. de Electronica, Jaen (Spain); Zufiria, P. [UPM Ciudad Universitaria, Dept. de Matematica Aplicada a las Tecnologias de la Informacion, Madrid (Spain)
2005-02-01
Several methods for sizing stand alone photovoltaic (pv) systems has been developed. The more simplistic are called intuitive methods. They are a useful tool for a first approach in sizing stand alone photovoltaic systems. Nevertheless they are very inaccurate. Analytical methods use equations to describe the pv system size as a function of reliability. These ones are more accurate than the previous ones but they are also not accurate enough for sizing of high reliability. In a third group there are methods which use system simulations. These ones are called numerical methods. Many of the analytical methods employ the concept of reliability of the system or the complementary term: loss of load probability (LOLP). In this paper an improvement for obtaining LOLP curves based on the neural network called Multilayer Perceptron (MLP) is presented. A unique MLP for many locations of Spain has been trained and after the training, the MLP is able to generate LOLP curves for any value and location. (Author)
Artificial neural Network-Based modeling and monitoring of photovoltaic generator
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H. MEKKI
2015-03-01
Full Text Available In this paper, an artificial neural network based-model (ANNBM is introduced for partial shading detection losses in photovoltaic (PV panel. A Multilayer Perceptron (MLP is used to estimate the electrical outputs (current and voltage of the photovoltaic module using the external meteorological data: solar irradiation G (W/m2 and the module temperature T (°C. Firstly, a database of the BP150SX photovoltaic module operating without any defect has been used to train the considered MLP. Subsequently, in the first case of this study, the developed model is used to estimate the output current and voltage of the PV module considering the partial shading effect. Results confirm the good ability of the ANNBM to detect the partial shading effect in the photovoltaic module with logical accuracy. The proposed strategy could also be used for the online monitoring and supervision of PV modules.
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S.C. Nayak
2017-09-01
Full Text Available The underlying system models of time series prediction are complex and not known a priori, hence, accurate and unbiased estimation cannot be always achieved using well known linear techniques. The estimation process requires more advanced prediction algorithms, such as multilayer perceptrons (MLPs. This paper presents an artificial chemical reaction neural network (ACRNN, which uses artificial chemical reaction optimization (ACRO to train the MLP models for forecasting the stock market indices. The underlying motivation for using ACRO is the ability to overcome the issues of convergence, parameter setting and overfitting and to accurately forecast financial time series data even when the underlying system processes are typically nonlinear. Historical data of seven different stock indices have been collected for 15 years to test the performance of the ACRNN approach. After extensive experimentation, it is observed that the ACRNN technique demonstrates significant improvements in prediction accuracy over the MLP approach.
Fast converging minimum probability of error neural network receivers for DS-CDMA communications.
Matyjas, John D; Psaromiligkos, Ioannis N; Batalama, Stella N; Medley, Michael J
2004-03-01
We consider a multilayer perceptron neural network (NN) receiver architecture for the recovery of the information bits of a direct-sequence code-division-multiple-access (DS-CDMA) user. We develop a fast converging adaptive training algorithm that minimizes the bit-error rate (BER) at the output of the receiver. The adaptive algorithm has three key features: i) it incorporates the BER, i.e., the ultimate performance evaluation measure, directly into the learning process, ii) it utilizes constraints that are derived from the properties of the optimum single-user decision boundary for additive white Gaussian noise (AWGN) multiple-access channels, and iii) it embeds importance sampling (IS) principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.
Performance Evaluation of Neural Network Based Pulse-Echo Weld Defect Classifiers
Seyedtabaii, S.
2012-10-01
Pulse-echo ultrasonic signal is used to detect weld defects with high probability. However, utilizing echo signal for defects classification is another issue that has attracted attention of many researchers who have devised algorithms and tested them against their own databases. In this paper, a study is conducted to score the performance of various algorithms against a single echo signal database. Algorithms tested the use of Wavelet Transform (WT), Fast Fourier Transform (FFT) and time domain echo signal features and employed several NN’s architectures such as Multi-Layer Perceptron Neural Network (MLP), Self Organizing Map (SOM) and others known to be good classifiers. The average performance of all can be viewed fair (90%) while some algorithms render success rate of about 94%. It seems that acquiring higher success rates out of a single fixed angle probe pulseecho set up needs new arrangements of data collection, which is under investigation.
Static sign language recognition using 1D descriptors and neural networks
Solís, José F.; Toxqui, Carina; Padilla, Alfonso; Santiago, César
2012-10-01
A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificial neural networks is presented in this work. The 1D descriptors were computed by two methods, first one consists in a correlation rotational operator.1 and second is based on contour analysis of hand shape. One of the main problems in sign language recognition is segmentation; most of papers report a special color in gloves or background for hand shape analysis. In order to avoid the use of gloves or special clothing, a thermal imaging camera was used to capture images. Static signs were picked up from 1 to 9 digits of American Sign Language, a multilayer perceptron reached 100% recognition with cross-validation.
Traceability of honey origin based on volatiles pattern processing by artificial neural networks.
Cajka, Tomas; Hajslova, Jana; Pudil, Frantisek; Riddellova, Katerina
2009-02-27
Head-space solid-phase microextraction (HS-SPME)-based procedure, coupled to comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GCxGC-TOF-MS), was employed for fast characterisation of honey volatiles. In total, 374 samples were collected over two production seasons in Corsica (n=219) and other European countries (n=155) with the emphasis to confirm the authenticity of the honeys labelled as "Corsica" (protected denomination of origin region). For the chemometric analysis, artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction (94.5%) and classification (96.5%) abilities of the ANN-MLP model were obtained when the data from two honey harvests were aggregated in order to improve the model performance compared to separate year harvests.
CSIR Research Space (South Africa)
Salmon, BP
2011-12-01
Full Text Available sliding window used, an overall change detection accuracy between 83% and 90% was achieved. It is shown that a sliding window of 6 months using all 7 bands of MODIS data is sufficient to detect land cover change reliably. Window sizes of 18 months...
Oracle separation of complexity classes and lower bounds for perceptrons solving separation problems
Vereshchagin, N. K.
1995-12-01
In the first part of the paper we prove that, relative to a random oracle, the class NP contains infinite sets having no infinite Co-NP-subsets (Co-NP-immune sets). In the second part we prove that perceptrons separating Boolean matrices in which each row contains at least one 1 from matrices in which many rows (say 99% of them) have no 1's must have either large size or large order. This result partially strengthens the "one-in-a-box"' theorem of Minsky and Papert [16] which states that perceptrons of small order cannot decide if every row of a given Boolean matrix has a 1. As a corollary, we prove that AM {}\\cap{}Co-AM {}\
SISTEM CERDAS DIAGNOSA PENYAKIT DALAM MENGGUNAKAN JARINGAN SYARAF TIRUAN DENGAN METODE PERCEPTRON
Directory of Open Access Journals (Sweden)
Usman Usman
2017-12-01
Full Text Available Aplikasi Jaringan Syaraf Tiruan (JST untuk diagnosa Penyakit Dalam dengan metode perceptron. Aplikasi ini dibuat untuk mengetahui keakuratan diagnosa Penyakit Dalam menggunakan JST perceptron dan mengimplementasikan JST perceptron berdasarkan gejala-gejala Penyakit Dalam ke dalam matlab dengan tampilan Graphical User Interface (GUI. Penyakit Dalam yang dibahas sebanyak 9 Penyakit Dalam. Yaitu penyakit Asma bronchial, Anemia, Demam berdarah, Diabetes mellitus, Gagal Jantung, Tetanus, Hipertensi, Hepatitis, dan Tuberkolosis paru. Gejala penyakit dalam yang diambil berdasarkan data rekam medis penderita Penyakit Dalam dan data pasien yang berobat di Poli Penyakit Dalam RSUD Puri Husada Tembilahan. Dari hasil pelatihan (training terhadap 48 data, kecocokan keluaran jaringan dan target yang di inginkan yaitu penyakit dalam 100% dapat dikenali/sesuai dengan target yang di inginkan. Dan hasil pengujian data baru sebanyak 10 kali pengujian menghasilkan keluaran sekitar 78,9 % yang sesuai dengan target dan 21,1% yang tidak sesuai dengan target. Hasil pengujian dan pelatihan di implementasikan ke dalam GUI sebagai aplikasi dan user interface.
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Hugo Valenzuela-García
2014-02-01
Full Text Available Over the last decade, the tourist destination of Lloret de Mar (Gerona, Spain has experienced an increasing concentration of souvenir stores owned by a population of Indian origin. Although Lloret de Mar is already a multicultural spot, Indians represent a small minority in Spain. In order to explain such a remarkable concentration and economic specialization in a low profit sector, we suggest the emergence of an ethnic enclave based on a triple articulation (entrepreneurs, employees and the local community. Our findings provide support for the mixed-embeddedness hypothesis, which contends that ethnic entrepreneurs in Europe need to rely on both the co-ethnic social networks and the linkages with the host society in order to run their business successfully. However, differential degrees of social integration amongst Indians themselves—clearly shown by the composition of their social networks—and between local dwellers and Indians suggest confronting interests amongst social agents in a highly complex and micro-social touristic site undergoing deep economic crisis. Throughout, a mixed-methods approach in the paper reveals the unequal social structure of the enclave, providing a better theoretical understanding of the difficulties and backgrounds in which minority migrant groups develop and expand their social relationships in the “host society”.
Reliability analysis of C-130 turboprop engine components using artificial neural network
Qattan, Nizar A.
In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine
Martín, M L; Turias, I J; González, F J; Galindo, P L; Trujillo, F J; Puntonet, C G; Gorriz, J M
2008-01-01
The region of the Bay of Algeciras is a very industrialized area where very few air pollution studies have been carried out. The main objective of this work has been the use of artificial neural networks (ANNs) as a predictive tool of high levels of ambient carbon monoxide (CO). Two approaches have been used: multilayer perceptron models (MLPs) with backpropagation learning rule and k-Nearest Neighbours (k-nn) classifiers, in order to predict future peaks of carbon monoxide. A resampling strategy with twofold cross-validation allowed the statistical comparison of the different topologies and models considered in the study. The procedure of random resampling permits an adequate and robust multiple comparisons of the tested models and allow us to select a group of best models.
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J. Prakash Maran
2013-09-01
Full Text Available In this study, a comparative approach was made between artificial neural network (ANN and response surface methodology (RSM to predict the mass transfer parameters of osmotic dehydration of papaya. The effects of process variables such as temperature, osmotic solution concentration and agitation speed on water loss, weight reduction, and solid gain during osmotic dehydration were investigated using a three-level three-factor Box-Behnken experimental design. Same design was utilized to train a feed-forward multilayered perceptron (MLP ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of root mean square error (RMSE, mean absolute error (MAE, standard error of prediction (SEP, model predictive error (MPE, chi square statistic (χ2, and coefficient of determination (R2 based on the validation data set. The results showed that properly trained ANN model is found to be more accurate in prediction as compared to RSM model.
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Elayne Rubio Delgado
2018-01-01
Full Text Available This research identifies the factors influencing the reduction of autopsies in a hospital of Veracruz. The study is based on the application of data mining techniques such as association rules and Bayesian networks in data sets obtained from opinions of physicians. We analyzed, for the exploration and extraction of the knowledge, algorithms like Apriori, FPGrowth, PredictiveApriori, Tertius, J48, NaiveBayes, MultilayerPerceptron, and BayesNet, all of them provided by the API of WEKA. To generate mining models and present the new knowledge in natural language, we also developed a web application. The results presented in this study are those obtained from the best-evaluated algorithms, which have been validated by specialists in the field of pathology.
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Fatma Zohra Chelali
2015-01-01
Full Text Available Face recognition has received a great attention from a lot of researchers in computer vision, pattern recognition, and human machine computer interfaces in recent years. Designing a face recognition system is a complex task due to the wide variety of illumination, pose, and facial expression. A lot of approaches have been developed to find the optimal space in which face feature descriptors are well distinguished and separated. Face representation using Gabor features and discrete wavelet has attracted considerable attention in computer vision and image processing. We describe in this paper a face recognition system using artificial neural networks like multilayer perceptron (MLP and radial basis function (RBF where Gabor and discrete wavelet based feature extraction methods are proposed for the extraction of features from facial images using two facial databases: the ORL and computer vision. Good recognition rate was obtained using Gabor and DWT parameterization with MLP classifier applied for computer vision dataset.
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J. C. Ochoa-Rivera
2002-01-01
Full Text Available A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain, while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2 model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation. Keywords: neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series..
Real-Time Analysis of Online Product Reviews by Means of Multi-Layer Feed-Forward Neural Networks
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Reinhold Decker
2014-11-01
Full Text Available In the recent past, the quantitative analysis of online product reviews (OPRs has become a popular manifestation of marketing intelligence activities focusing on products that are frequently subject to electronic word-of-mouth (eWOM. Typical elements of OPRs are overall star ratings, product at- tribute scores, recommendations, pros and cons, and free texts. The first three elements are of pa r- ticular interest because they provide an aggregate view of reviewers’ opinions about the products of interest. However, the significance of individual product attributes in the overall evaluation pro c- ess can vary in the course of time. Accordingly, ad hoc analyses of OPRs that have been downloaded at a certain point in time are of limited value for dynamic eWOM monitoring because of their snapshot character. On the other hand, opinion platforms can increase the meaningfulness of the OPRs posted there and, therewith, the usefulness of the platform as a whole, by directing eWOM activities to those product attributes that really matter at present. This paper therefore in- troduces a neural network-based approach that allows the dynamic tracking of the influence the posted scores of product attributes have on the overall star ratings of the concerning products. By using an elasticity measure, this approach supports the identification of those attributes that tend to lose or gain significance in the product evaluation process over time. The usability of this ap- proach is demonstrated using real OPR data on digital cameras and hotels.
Review On Applications Of Neural Network To Computer Vision
Li, Wei; Nasrabadi, Nasser M.
1989-03-01
Neural network models have many potential applications to computer vision due to their parallel structures, learnability, implicit representation of domain knowledge, fault tolerance, and ability of handling statistical data. This paper demonstrates the basic principles, typical models and their applications in this field. Variety of neural models, such as associative memory, multilayer back-propagation perceptron, self-stabilized adaptive resonance network, hierarchical structured neocognitron, high order correlator, network with gating control and other models, can be applied to visual signal recognition, reinforcement, recall, stereo vision, motion, object tracking and other vision processes. Most of the algorithms have been simulated on com-puters. Some have been implemented with special hardware. Some systems use features, such as edges and profiles, of images as the data form for input. Other systems use raw data as input signals to the networks. We will present some novel ideas contained in these approaches and provide a comparison of these methods. Some unsolved problems are mentioned, such as extracting the intrinsic properties of the input information, integrating those low level functions to a high-level cognitive system, achieving invariances and other problems. Perspectives of applications of some human vision models and neural network models are analyzed.
Probing many-body localization with neural networks
Schindler, Frank; Regnault, Nicolas; Neupert, Titus
2017-06-01
We show that a simple artificial neural network trained on entanglement spectra of individual states of a many-body quantum system can be used to determine the transition between a many-body localized and a thermalizing regime. Specifically, we study the Heisenberg spin-1/2 chain in a random external field. We employ a multilayer perceptron with a single hidden layer, which is trained on labeled entanglement spectra pertaining to the fully localized and fully thermal regimes. We then apply this network to classify spectra belonging to states in the transition region. For training, we use a cost function that contains, in addition to the usual error and regularization parts, a term that favors a confident classification of the transition region states. The resulting phase diagram is in good agreement with the one obtained by more conventional methods and can be computed for small systems. In particular, the neural network outperforms conventional methods in classifying individual eigenstates pertaining to a single disorder realization. It allows us to map out the structure of these eigenstates across the transition with spatial resolution. Furthermore, we analyze the network operation using the dreaming technique to show that the neural network correctly learns by itself the power-law structure of the entanglement spectra in the many-body localized regime.
Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks
Energy Technology Data Exchange (ETDEWEB)
Ziaul Huque
2007-08-31
This is the final technical report for the project titled 'Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks'. The aim of the project was to develop an efficient chemistry model for combustion simulations. The reduced chemistry model was developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) was used via a new network topology known as Non-linear Principal Components Analysis (NPCA). A commonly used Multilayer Perceptron Neural Network (MLP-NN) was modified to implement NPCA-NN. The training rate of NPCA-NN was improved with the GEneralized Regression Neural Network (GRNN) based on kernel smoothing techniques. Kernel smoothing provides a simple way of finding structure in data set without the imposition of a parametric model. The trajectory data of the reaction mechanism was generated based on the optimization techniques of genetic algorithm (GA). The NPCA-NN algorithm was then used for the reduction of Dimethyl Ether (DME) mechanism. DME is a recently discovered fuel made from natural gas, (and other feedstock such as coal, biomass, and urban wastes) which can be used in compression ignition engines as a substitute for diesel. An in-house two-dimensional Computational Fluid Dynamics (CFD) code was developed based on Meshfree technique and time marching solution algorithm. The project also provided valuable research experience to two graduate students.
Stress Compensating Multilayers
Broadway, David M.; Ramsey, Brian D.; O'dell, Stephen; Gurgew, Danielle
2017-01-01
We present in-situ stress measurement results for single and multilayer thin-films deposited by magnetron sputtering. In particular, we report on the influence of the material interfaces on the ensuing stress in both the transient and steady-state regimes of film growth. This behavior is used to determine the appropriate thicknesses of the constituent layers that will result in a net tensile stress in multilayers composed of various material combinations. These multilayers can then be used to compensate the compressive integrated stress in single and multilayer EUV and x-ray optical coatings. The use of multilayers to compensate the integrated stress might be advantageous because, unlike single layers of chromium, the roughness is not expected to increase with the total thickness of the multilayer. In this paper, we demonstrate the technique for W/Si and Mo/Si multilayers and discuss its application to other material combinations.
A Simple Neural Network System for Wisconsin Card Sorting Test
National Research Council Canada - National Science Library
Kaplan, Gulay
2001-01-01
.... A simple model based on winner take all network and multi layer perceptron suffices to model the affect of frontal lobe damage, which leads to perseveration as diminishing the influence of reinforcement...
Safavi, Hamid R; Malek Ahmadi, Kian
2015-01-01
Although drought impacts on water quantity are widely recognized, the impacts on water quality are less known. The Zayandehrud River basin in the west-central part of Iran plateau witnessed an increased contamination during the recent droughts and low flows. The river has been receiving wastewater and effluents from the villages, a number of small and large industries, and irrigation drainage systems along its course. What makes the situation even worse is the drought period the river basin has been going through over the last decade. Therefore, a river quality management model is required to include the adverse effects of industrial development in the region and the destructive effects of droughts which affect the river's water quality and its surrounding environment. Developing such a model naturally presupposes investigations into pollution effects in terms of both quality and quantity to be used in such management tools as mathematical models to predict the water quality of the river and to prevent pollution escalation in the environment. The present study aims to investigate electrical conductivity of the Zayandehrud River as a water quality parameter and to evaluate the effect of this parameter under drought conditions. For this purpose, artificial neural networks are used as a modeling tool to derive the relationship between electrical conductivity and the hydrological parameters of the Zayandehrud River. The models used in this research include multi-layer perceptron and radial basis function. Finally, these two models are compared in terms of their performance using the time series of electrical conductivity at eight monitoring-hydrometric stations during drought periods between the years 1997-2012. Results show that artificial neural networks can be used for modeling the relationship between electrical conductivity and hydrological parameters under drought conditions. It is further shown that radial basis function works better for the upstream stretches
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available An electrocardiogram (ECG beat classification scheme based on multiple signal classification (MUSIC algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP neural network and a probabilistic neural network (PNN, are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.
Directory of Open Access Journals (Sweden)
Mariane Gonçalves Ferreira
Full Text Available ABSTRACT Germplasm classification by species requires specific knowledge on/of the culture of interest. Therefore, efforts aimed at automation of this process are necessary for the efficient management of collections. Automation of germplasm classification through artificial neural networks may be a viable and less laborious strategy. The aims of this study were to verify the classification potential of Capsicum accessions regarding/ the species based on morphological descriptors and artificial neural networks, and to establish the most important descriptors and the best network architecture for this purpose. Five hundred and sixty-four plants from 47 Brazilian Capsicum accessions were evaluated. Neural networks of multilayer perceptron type were used in order to automate the species identification through 17 morphological descriptors. Six network architectures were evaluated, and the number of neurons in the hidden layer ranged from 1 to 6. The relative importance of morphological descriptors in the classification process was established by Garson's method. Corolla color, corolla spot color, calyx annular constriction, fruit shape at pedicel attachment, and fruit color at mature stage were the most important descriptors. The network architecture with 6 neurons in the hidden layer is the most appropriate in this study. The possibility of classifying Capsicum plants regarding/ the species through artificial neural networks with 100 % accuracy was verified.
Naghsh-Nilchi, Ahmad R.; Kadkhodamohammadi, A. Rahim
2009-12-01
An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.
Yeşilkanat, Cafer Mert; Kobya, Yaşar; Taşkın, Halim; Çevik, Uğur
2017-09-01
The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by using both artificial neural network (ANN) and fuzzy logic (FL) methods, compare the performances of methods, make dose estimations for intermediate stations with no previous measurements and create dose rate risk maps of the study area. In order to determine the dose distribution by using artificial neural networks, two main networks and five different network structures were used; feed forward ANN; Multi-layer perceptron (MLP), Radial basis functional neural network (RBFNN), Quantile regression neural network (QRNN) and recurrent ANN; Jordan networks (JN), Elman networks (EN). In the evaluation of estimation performance obtained for the test data, all models appear to give similar results. According to the cross-validation results obtained for explaining AGDR distribution, Pearson's r coefficients were calculated as 0.94, 0.91, 0.89, 0.91, 0.91 and 0.92 and RMSE values were calculated as 34.78, 43.28, 63.92, 44.86, 46.77 and 37.92 for MLP, RBFNN, QRNN, JN, EN and FL, respectively. In addition, spatial risk maps showing distributions of AGDR of the study area were created by all models and results were compared with geological, topological and soil structure. Copyright © 2017 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Valérie Bourdès
2010-01-01
Full Text Available The aim of this study was to compare multilayer perceptron neural networks (NNs with standard logistic regression (LR to identify key covariates impacting on mortality from cancer causes, disease-free survival (DFS, and disease recurrence using Area Under Receiver-Operating Characteristics (AUROC in breast cancer patients. From 1996 to 2004, 2,535 patients diagnosed with primary breast cancer entered into the study at a single French centre, where they received standard treatment. For specific mortality as well as DFS analysis, the ROC curves were greater with the NN models compared to LR model with better sensitivity and specificity. Four predictive factors were retained by both approaches for mortality: clinical size stage, Scarff Bloom Richardson grade, number of invaded nodes, and progesterone receptor. The results enhanced the relevance of the use of NN models in predictive analysis in oncology, which appeared to be more accurate in prediction in this French breast cancer cohort.
Lashkari, Negin; Poshtan, Javad; Azgomi, Hamid Fekri
2015-11-01
The three-phase shift between line current and phase voltage of induction motors can be used as an efficient fault indicator to detect and locate inter-turn stator short-circuit (ITSC) fault. However, unbalanced supply voltage is one of the contributing factors that inevitably affect stator currents and therefore the three-phase shift. Thus, it is necessary to propose a method that is able to identify whether the unbalance of three currents is caused by ITSC or supply voltage fault. This paper presents a feedforward multilayer-perceptron Neural Network (NN) trained by back propagation, based on monitoring negative sequence voltage and the three-phase shift. The data which are required for training and test NN are generated using simulated model of stator. The experimental results are presented to verify the superior accuracy of the proposed method. Copyright © 2015. Published by Elsevier Ltd.
Kodama, Takumi; Makino, Shoji
2017-07-01
In the presented study we conduct the off-line ERP classification using the convolutional neural network (CNN) classifier for somatosensory ERP intervals acquired in the full- body tactile P300-based Brain-Computer Interface paradigm (fbBCI). The main objective of the study is to enhance fbBCI stimulus pattern classification accuracies by applying the CNN classifier. A 60 × 60 squared input volume transformed by one-dimensional somatosensory ERP intervals in each electrode channel is input to the convolutional architecture for a filter training. The flattened activation maps are evaluated by a multilayer perceptron with one-hidden-layer in order to calculate classification accuracy results. The proposed method reveals that the CNN classifier model can achieve a non-personal- training ERP classification with the fbBCI paradigm, scoring 100 % classification accuracy results for all the participated ten users.
VoIP attacks detection engine based on neural network
Safarik, Jakub; Slachta, Jiri
2015-05-01
The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.
Using Visualization Techniques in Multilayer Traffic Modeling
Bragg, Arnold
We describe visualization techniques for multilayer traffic modeling - i.e., traffic models that span several protocol layers, and traffic models of protocols that cross layers. Multilayer traffic modeling is challenging, as one must deal with disparate traffic sources; control loops; the effects of network elements such as IP routers; cross-layer protocols; asymmetries in bandwidth, session lengths, and application behaviors; and an enormous number of complex interactions among the various factors. We illustrate by using visualization techniques to identify relationships, transformations, and scaling; to smooth simulation and measurement data; to examine boundary cases, subtle effects and interactions, and outliers; to fit models; and to compare models with others that have fewer parameters. Our experience suggests that visualization techniques can provide practitioners with extraordinary insight about complex multilayer traffic effects and interactions that are common in emerging next-generation networks.
Neural Network Control of Asymmetrical Multilevel Converters
Directory of Open Access Journals (Sweden)
Patrice WIRA
2009-12-01
Full Text Available This paper proposes a neural implementation of a harmonic eliminationstrategy (HES to control a Uniform Step Asymmetrical Multilevel Inverter(USAMI. The mapping between the modulation rate and the requiredswitching angles is learned and approximated with a Multi-Layer Perceptron(MLP neural network. After learning, appropriate switching angles can bedetermined with the neural network leading to a low-computational-costneural controller which is well suited for real-time applications. Thistechnique can be applied to multilevel inverters with any number of levels. Asan example, a nine-level inverter and an eleven-level inverter are consideredand the optimum switching angles are calculated on-line. Comparisons to thewell-known sinusoidal pulse-width modulation (SPWM have been carriedout in order to evaluate the performance of the proposed approach. Simulationresults demonstrate the technical advantages of the proposed neuralimplementation over the conventional method (SPWM in eliminatingharmonics while controlling a nine-level and eleven-level USAMI. Thisneural approach is applied for the supply of an asynchronous machine andresults show that it ensures a highest quality torque by efficiently cancelingthe harmonics generated by the inverters.
Milling tool wear diagnosis by feed motor current signal using an artificial neural network
Energy Technology Data Exchange (ETDEWEB)
Khajavi, Mehrdad Nouri; Nasernia, Ebrahim; Rostaghi, Mostafa [Dept. of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran (Iran, Islamic Republic of)
2016-11-15
In this paper, a Multi-layer perceptron (MLP) neural network was used to predict tool wear in face milling. For this purpose, a series of experiments was conducted using a milling machine on a CK45 work piece. Tool wear was measured by an optical microscope. To improve the accuracy and reliability of the monitoring system, tool wear state was classified into five groups, namely, no wear, slight wear, normal wear, severe wear and broken tool. Experiments were conducted with the aforementioned tool wear states, and different machining conditions and data were extracted. An increase in current amplitude was observed as the tool wear increased. Furthermore, effects of parameters such as tool wear, feed, and cut depth on motor current consumption were analyzed. Considering the complexity of the wear state classification, a multi-layer neural network was used. The root mean square of motor current, feed, cut depth, and tool rpm were chosen as the input and amount of flank wear as the output of MLP. Results showed good performance of the designed tool wear monitoring system.
Combining neural networks and genetic algorithms for hydrological flow forecasting
Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr
2010-05-01
We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and
Neural-network classifiers for automatic real-world aerial image recognition.
Greenberg, S; Guterman, H
1996-08-10
We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.
Neural-network classifiers for automatic real-world aerial image recognition
Greenberg, Shlomo; Guterman, Hugo
1996-08-01
We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.
Okut, Hayrettin; Gianola, Daniel; Rosa, Guilherme J M; Weigel, Kent A
2011-06-01
Bayesian regularization of artificial neural networks (BRANNs) were used to predict body mass index (BMI) in mice using single nucleotide polymorphism (SNP) markers. Data from 1896 animals with both phenotypic and genotypic (12 320 loci) information were used for the analysis. Missing genotypes were imputed based on estimated allelic frequencies, with no attempt to reconstruct haplotypes based on family information or linkage disequilibrium between markers. A feed-forward multilayer perceptron network consisting of a single output layer and one hidden layer was used. Training of the neural network was done using the Bayesian regularized backpropagation algorithm. When the number of neurons in the hidden layer was increased, the number of effective parameters, γ, increased up to a point and stabilized thereafter. A model with five neurons in the hidden layer produced a value of γ that saturated the data. In terms of predictive ability, a network with five neurons in the hidden layer attained the smallest error and highest correlation in the test data although differences among networks were negligible. Using inherent weight information of BRANN with different number of neurons in the hidden layer, it was observed that 17 SNPs had a larger impact on the network, indicating their possible relevance in prediction of BMI. It is concluded that BRANN may be at least as useful as other methods for high-dimensional genome-enabled prediction, with the advantage of its potential ability of capturing non-linear relationships, which may be useful in the study of quantitative traits under complex gene action.
Application of Artificial Neural Networks for Efficient High-Resolution 2D DOA Estimation
Directory of Open Access Journals (Sweden)
M. Agatonović
2012-12-01
Full Text Available A novel method to provide high-resolution Two-Dimensional Direction of Arrival (2D DOA estimation employing Artificial Neural Networks (ANNs is presented in this paper. The observed space is divided into azimuth and elevation sectors. Multilayer Perceptron (MLP neural networks are employed to detect the presence of a source in a sector while Radial Basis Function (RBF neural networks are utilized for DOA estimation. It is shown that a number of appropriately trained neural networks can be successfully used for the high-resolution DOA estimation of narrowband sources in both azimuth and elevation. The training time of each smaller network is significantly re¬duced as different training sets are used for networks in detection and estimation stage. By avoiding the spectral search, the proposed method is suitable for real-time ap¬plications as it provides DOA estimates in a matter of seconds. At the same time, it demonstrates the accuracy comparable to that of the super-resolution 2D MUSIC algorithm.
Qi, Donglian; Liu, Meiqin; Qiu, Meikang; Zhang, Senlin
2010-08-01
This brief studies exponential H(infinity) synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H(infinity) control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays, but also reduce the effect of external disturbance on the synchronization error to a minimal H(infinity) norm constraint. The proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities. Most discrete-time chaotic systems with or without time delays, such as Hopfield neural networks, cellular neural networks, bidirectional associative memory networks, recurrent multilayer perceptrons, Cohen-Grossberg neural networks, Chua's circuits, etc., can be transformed into this general chaotic neural network to be H(infinity) synchronization controller designed in a unified way. Finally, some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods.
Nimura, Yukitaka; Hayashi, Yuichiro; Kitasaka, Takayuki; Mori, Kensaku
2015-03-01
This paper presents a method for torso organ segmentation from abdominal CT images using structured perceptron and dual decomposition. A lot of methods have been proposed to enable automated extraction of organ regions from volumetric medical images. However, it is necessary to adjust empirical parameters of them to obtain precise organ regions. This paper proposes an organ segmentation method using structured output learning. Our method utilizes a graphical model and binary features which represent the relationship between voxel intensities and organ labels. Also we optimize the weights of the graphical model by structured perceptron and estimate the best organ label for a given image by dynamic programming and dual decomposition. The experimental result revealed that the proposed method can extract organ regions automatically using structured output learning. The error of organ label estimation was 4.4%. The DICE coeﬃcients of left lung, right lung, heart, liver, spleen, pancreas, left kidney, right kidney, and gallbladder were 0.91, 0.95, 0.77, 0.81, 0.74, 0.08, 0.83, 0.84, and 0.03, respectively.
DEFF Research Database (Denmark)
Fotiadis, Panagiotis; Polignano, Michele; Gimenez, Lucas Chavarria
2013-01-01
This paper investigates the potentials of traffic steering in the Radio Resource Control (RRC) Idle state by evaluating the Absolute Priorities (AP) framework in a multilayer Long Term Evolution (LTE) macrocell scenario. Frequency priorities are broadcast on the system information and RRC Idle...... signaling. The priority adjustment is based on both the Composite Available Capacity (CAC) and the radio conditions of the candidate layers. Compared to broadcast AP, the proposed scheme achieves better load balancing performance and improves network capacity, given that the User Equipment (UE) inactivity...
Neural network stochastic simulation applied for quantifying uncertainties
Directory of Open Access Journals (Sweden)
N Foudil-Bey
2016-09-01
Full Text Available Generally the geostatistical simulation methods are used to generate several realizations of physical properties in the sub-surface, these methods are based on the variogram analysis and limited to measures correlation between variables at two locations only. In this paper, we propose a simulation of properties based on supervised Neural network training at the existing drilling data set. The major advantage is that this method does not require a preliminary geostatistical study and takes into account several points. As a result, the geological information and the diverse geophysical data can be combined easily. To do this, we used a neural network with multi-layer perceptron architecture like feed-forward, then we used the back-propagation algorithm with conjugate gradient technique to minimize the error of the network output. The learning process can create links between different variables, this relationship can be used for interpolation of the properties on the one hand, or to generate several possible distribution of physical properties on the other hand, changing at each time and a random value of the input neurons, which was kept constant until the period of learning. This method was tested on real data to simulate multiple realizations of the density and the magnetic susceptibility in three-dimensions at the mining camp of Val d'Or, Québec (Canada.
Classification of images acquired with colposcopy using artificial neural networks.
Simões, Priscyla W; Izumi, Narjara B; Casagrande, Ramon S; Venson, Ramon; Veronezi, Carlos D; Moretti, Gustavo P; da Rocha, Edroaldo L; Cechinel, Cristian; Ceretta, Luciane B; Comunello, Eros; Martins, Paulo J; Casagrande, Rogério A; Snoeyer, Maria L; Manenti, Sandra A
2014-01-01
To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.
Estimating Type Ia Supernova Metallicities Using Neural Networks
Villar, V. Ashley
2017-01-01
Normal Type Ia supernovae (SNe) can be used as standardizable candles because their progenitors, white dwarfs, are a fairly homogenous class of objects. However, intrinsic variability in these events arise from a number of factors, including metallicity. Recent studies have investigated the effects of metallicity on Type Ia SNe observables from both a theoretical approach, by tuning model metallicity to analyze spectral features, and an observational approach, by studying the effect of host metallicity on light curves. In this work, we take a new, data-driven approach to the problem. Inspired by the success of neural networks in the field of image processing, we aim to estimate the metallicities of Type Ia SNe progenitors from their near-maximum spectra using feed-forward neural networks. We first collect a sample of near-maximum Type Ia SNe spectra from the literature to be smoothed and down-sampled. We then estimate the metallicities of the SNe hosts using the B-band magnitudes. We build a multilayer perceptron to generate a model that takes as input the down-sampled spectra and returns a scalar metallicity. Finally, we discuss basic considerations to be taken when working with spectral (as opposed to image) data using neural networks.
Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon
2017-07-03
Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells, and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae species, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network. Neural network architectures of multilayer perceptron (MLP) and radial basis function architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.
Artificial neural networks in analysis of indinavir and its degradation products retention.
Jancić-Stojanović, B; Ivanović, D; Malenović, A; Medenica, M
2009-04-15
Artificial neural networks (ANN) are biologically inspired computer programs designed to simulate the way in which the human brain processes the information. In the past few years, coupling of experimental design (ED) and ANN became useful tool in the method optimization. This paper presents the application of ED-ANN in analysis of chromatographic behavior of indinavir and its degradation products. According to preliminary study, full factorial design 2(4) was chosen to set input variables for network training. Experimental data (inputs) and results for retention factors from experiments (outputs) were used to train the ANN with aim to define correlation among variables. For networks training multi-layer perceptron (MLP) with back propagation (BP) algorithm was used. Network with the lowest root mean square (RMS) had 4-8-3 topology. Predicted data were in good agreement with experimental data (correlation was higher than 0.9713 for training set). Regression statistics confirmed good ability of trained network to predict compounds retention.
A new source difference artificial neural network for enhanced positioning accuracy
Bhatt, Deepak; Aggarwal, Priyanka; Devabhaktuni, Vijay; Bhattacharya, Prabir
2012-10-01
Integrated inertial navigation system (INS) and global positioning system (GPS) units provide reliable navigation solution compared to standalone INS or GPS. Traditional Kalman filter-based INS/GPS integration schemes have several inadequacies related to sensor error model and immunity to noise. Alternatively, multi-layer perceptron (MLP) neural networks with three layers have been implemented to improve the position accuracy of the integrated system. However, MLP neural networks show poor accuracy for low-cost INS because of the large inherent sensor errors. For the first time the paper demonstrates the use of knowledge-based source difference artificial neural network (SDANN) to improve navigation performance of low-cost sensor, with or without external aiding sources. Unlike the conventional MLP or artificial neural networks (ANN), the structure of SDANN consists of two MLP neural networks called the coarse model and the difference model. The coarse model learns the input-output data relationship whereas the difference model adds knowledge to the system and fine-tunes the coarse model output by learning the associated training or estimation error. Our proposed SDANN model illustrated a significant improvement in navigation accuracy of up to 81% over conventional MLP. The results demonstrate that the proposed SDANN method is effective for GPS/INS integration schemes using low-cost inertial sensors, with and without GPS.
Temporal difference learning for the game Tic-Tac-Toe 3D: Applying structure to neural networks
Van De Steeg, Michiel; Drugan, Madalina M.; Wiering, Marco
2016-01-01
When reinforcement learning is applied to large state spaces, such as those occurring in playing board games, the use of a good function approximator to learn to approximate the value function is very important. In previous research, multi-layer perceptrons have often been quite successfully used as
Yoon, Hyun Joong; Chung, Seong Youb
2013-12-01
This paper addresses the emotion recognition problem from electroencephalogram signals, in which emotions are represented on the valence and arousal dimensions. Fast Fourier transform analysis is used to extract features and the feature selection based on Pearson correlation coefficient is applied. This paper proposes a probabilistic classifier based on Bayes' theorem and a supervised learning using a perceptron convergence algorithm. To verify the proposed methodology, we use an open database. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the average accuracy of the valence and arousal estimation is 70.9% and 70.1%, respectively. For the three-level class case, the average accuracy is 55.4% and 55.2%, respectively. © 2013 Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Siamak Khorram
2009-07-01
Full Text Available This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM neural network module, and a supervised Multilayer Perceptron (MLP neural network module using the Backpropagation (BP training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning algorithm which incorporated Simulated Annealing (SA. The ability of our automated ANN system to perform Land-Use/Land-Cover (LU/LC classifications of a Landsat Thematic Mapper (TM image was tested using a supervised MLP network, an unsupervised SOM network, and a combination of SOM with SA network. Our case study demonstrated that the ANN classification system fulfilled the tasks of network training pattern creation, network training, and network generalization. The results from the three networks were assessed via a comparison with reference data derived from the high spatial resolution Digital Colour Infrared (CIR Digital Orthophoto Quarter Quad (DOQQ data. The supervised MLP network obtained the most accurate classification accuracy as compared to the two unsupervised SOM networks. Additionally, the classification performance of the refined SOM network was found to be significantly better than that of the standard SOM network essentially due to the incorporation of SA. This is mainly due to the SA-assisted classification utilizing the scheduling cooling scheme. It is concluded that our automated ANN classification system can be utilized for LU/LC applications and will be particularly useful when traditional statistical classification methods are not suitable due to a statistically abnormal distribution of the input data.
Conley, Ray; Liu, Chian; Qian, Jun; Kewish, Cameron M.; Macrander, Albert T.; Yan, Hanfei; Kang, Hyon Chol; Maser, Jörg; Stephenson, G. Brian
2008-05-01
A multilayer Laue lens (MLL) is an x-ray focusing optic fabricated from a multilayer structure consisting of thousands of layers of two different materials produced by thin-film deposition. The sequence of layer thicknesses is controlled to satisfy the Fresnel zone plate law and the multilayer is sectioned to form the optic. An improved MLL geometry can be created by growing each layer with an in-plane thickness gradient to form a wedge, so that every interface makes the correct angle with the incident beam for symmetric Bragg diffraction. The ultimate hard x-ray focusing performance of a wedged MLL has been predicted to be significantly better than that of a nonwedged MLL, giving subnanometer resolution with high efficiency. Here, we describe a method to deposit the multilayer structure needed for an ideal wedged MLL and report our initial deposition results to produce these structures.
Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks
Directory of Open Access Journals (Sweden)
M. Bazazzadeh
2011-01-01
Full Text Available This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc. are obtained. These parameters provide a precious database, which train a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with FLC illustrate that the proposed controller achieves the desired performance and stability.
Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models
Directory of Open Access Journals (Sweden)
Sungwon Kim
2015-06-01
Full Text Available The objective of this study is to develop artificial neural network (ANN models, including multilayer perceptron (MLP and Kohonen self-organizing feature map (KSOFM, for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop and 11-3-1 (Levenberg-Marquardt were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop and 1-3-11 (Levenberg–Marquardt, which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.
A comparison study between MLP and convolutional neural network models for character recognition
Ben Driss, S.; Soua, M.; Kachouri, R.; Akil, M.
2017-05-01
Optical Character Recognition (OCR) systems have been designed to operate on text contained in scanned documents and images. They include text detection and character recognition in which characters are described then classified. In the classification step, characters are identified according to their features or template descriptions. Then, a given classifier is employed to identify characters. In this context, we have proposed the unified character descriptor (UCD) to represent characters based on their features. Then, matching was employed to ensure the classification. This recognition scheme performs a good OCR Accuracy on homogeneous scanned documents, however it cannot discriminate characters with high font variation and distortion.3 To improve recognition, classifiers based on neural networks can be used. The multilayer perceptron (MLP) ensures high recognition accuracy when performing a robust training. Moreover, the convolutional neural network (CNN), is gaining nowadays a lot of popularity for its high performance. Furthermore, both CNN and MLP may suffer from the large amount of computation in the training phase. In this paper, we establish a comparison between MLP and CNN. We provide MLP with the UCD descriptor and the appropriate network configuration. For CNN, we employ the convolutional network designed for handwritten and machine-printed character recognition (Lenet-5) and we adapt it to support 62 classes, including both digits and characters. In addition, GPU parallelization is studied to speed up both of MLP and CNN classifiers. Based on our experimentations, we demonstrate that the used real-time CNN is 2x more relevant than MLP when classifying characters.
METHODS OF TEXT INFORMATION CLASSIFICATION ON THE BASIS OF ARTIFICIAL NEURAL AND SEMANTIC NETWORKS
L. V. Serebryanaya; V. V. Potaraev
2016-01-01
The article covers the use of perseptron, Hopfild artificial neural network and semantic network for classification of text information. Network training algorithms are studied. An algorithm of inverse mistake spreading for perceptron network and convergence algorithm for Hopfild network are implemented. On the basis of the offered models and algorithms automatic text classification software is developed and its operation results are evaluated.
Marcos, J V; Hornero, R; Alvarez, D; Nabney, I T; Del Campo, F; Zamarrón, C
2010-03-01
In the present study, multilayer perceptron (MLP) neural networks were applied to help in the diagnosis of obstructive sleep apnoea syndrome (OSAS). Oxygen saturation (SaO(2)) recordings from nocturnal pulse oximetry were used for this purpose. We performed time and spectral analysis of these signals to extract 14 features related to OSAS. The performance of two different MLP classifiers was compared: maximum likelihood (ML) and Bayesian (BY) MLP networks. A total of 187 subjects suspected of suffering from OSAS took part in the study. Their SaO(2) signals were divided into a training set with 74 recordings and a test set with 113 recordings. BY-MLP networks achieved the best performance on the test set with 85.58% accuracy (87.76% sensitivity and 82.39% specificity). These results were substantially better than those provided by ML-MLP networks, which were affected by overfitting and achieved an accuracy of 76.81% (86.42% sensitivity and 62.83% specificity). Our results suggest that the Bayesian framework is preferred to implement our MLP classifiers. The proposed BY-MLP networks could be used for early OSAS detection. They could contribute to overcome the difficulties of nocturnal polysomnography (PSG) and thus reduce the demand for these studies.
Statistical process control using optimized neural networks: a case study.
Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid
2014-09-01
The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Automatic localization of vertebrae based on convolutional neural networks
Shen, Wei; Yang, Feng; Mu, Wei; Yang, Caiyun; Yang, Xin; Tian, Jie
2015-03-01
Localization of the vertebrae is of importance in many medical applications. For example, the vertebrae can serve as the landmarks in image registration. They can also provide a reference coordinate system to facilitate the localization of other organs in the chest. In this paper, we propose a new vertebrae localization method using convolutional neural networks (CNN). The main advantage of the proposed method is the removal of hand-crafted features. We construct two training sets to train two CNNs that share the same architecture. One is used to distinguish the vertebrae from other tissues in the chest, and the other is aimed at detecting the centers of the vertebrae. The architecture contains two convolutional layers, both of which are followed by a max-pooling layer. Then the output feature vector from the maxpooling layer is fed into a multilayer perceptron (MLP) classifier which has one hidden layer. Experiments were performed on ten chest CT images. We used leave-one-out strategy to train and test the proposed method. Quantitative comparison between the predict centers and ground truth shows that our convolutional neural networks can achieve promising localization accuracy without hand-crafted features.
Modeling of surface dust concentrations using neural networks and kriging
Buevich, Alexander G.; Medvedev, Alexander N.; Sergeev, Alexander P.; Tarasov, Dmitry A.; Shichkin, Andrey V.; Sergeeva, Marina V.; Atanasova, T. B.
2016-12-01
Creating models which are able to accurately predict the distribution of pollutants based on a limited set of input data is an important task in environmental studies. In the paper two neural approaches: (multilayer perceptron (MLP)) and generalized regression neural network (GRNN)), and two geostatistical approaches: (kriging and cokriging), are using for modeling and forecasting of dust concentrations in snow cover. The area of study is under the influence of dust emissions from a copper quarry and a several industrial companies. The comparison of two mentioned approaches is conducted. Three indices are used as the indicators of the models accuracy: the mean absolute error (MAE), root mean square error (RMSE) and relative root mean square error (RRMSE). Models based on artificial neural networks (ANN) have shown better accuracy. When considering all indices, the most precision model was the GRNN, which uses as input parameters for modeling the coordinates of sampling points and the distance to the probable emissions source. The results of work confirm that trained ANN may be more suitable tool for modeling of dust concentrations in snow cover.
Controlling light with plasmonic multilayers
DEFF Research Database (Denmark)
Orlov, Alexey A.; Zhukovsky, Sergei; Iorsh, Ivan V.
2014-01-01
Recent years have seen a new wave of interest in layered media - namely, plasmonic multilayers - in several emerging applications ranging from transparent metals to hyperbolic metamaterials. In this paper, we review the optical properties of such subwavelength metal-dielectric multilayered...... metamaterials and describe their use for light manipulation at the nanoscale. While demonstrating the recently emphasized hallmark effect of hyperbolic dispersion, we put special emphasis to the comparison between multilayered hyperbolic metamaterials and more broadly defined plasmonic-multilayer metamaterials...
Laosiritaworn, Yongjua; Laosiritaworn, Yongyut; Laosiritaworn, Wimalin S.
2017-09-01
In this work, the disease spreading under SIR framework (susceptible-infected-recovered) agent-based model was investigated via magnetic spin model, stochastic Monte Carlo simulation, and Neural Network analysis. The defined systems were two-dimensional lattice-like, where the spins (representing susceptible, infected, and recovered agents) were allocated on lattice cells. The lattice size, spin density, and infectious period were varied to observe its influence on disease spreading period. In the simulation, each spin was randomly allocated on the lattice and interacted with its first neighbouring spins for disease spreading. The subgroup magnetization profiles were recorded. From the results, numbers of agents in each subgroup as a function of time was found to depend on all considered parameters. Specifically, the disease spreading period slightly increases with increasing system size, decreases with increasing spin density, and exponentially decays with increasing infectious period. Due to many degrees of freedom associated, Neural Network was used to establish complex relationship among parameters. Multi-layer perceptron was considered, where optimized network architecture of 3-19-15-1 was found. Good agreement between predicted and actual outputs was evident. This confirms the validity of using Neural Network as supplements in modelling SIR disease spreading and provides profound database for future deployment.
Directory of Open Access Journals (Sweden)
M.R. Mosavi
2016-01-01
Full Text Available This paper presents a new method to estimate the time of important earthquakes in Hormozgan region with magnitude greater than 5.5 based on the Radial Basis Function (RBF Neural Network (NN models. Input vector to the network is composed of different seismicity rates between main events that are calculated in convenient and reliable way to create optimized training methods. It helps network with a limited number of training data to estimation. It is common for earthquakes modeling by data-driven methods in this case. In addition, the proposed method is combined with Rosenberg cluster method to remove aftershocks events from the history of catalog for NN to better process the data. The results show that created RBF model successfully estimates the interevent times between large and sequence earthquakes that can be used as a tool to predict earthquake, so that comparison with other NN structure, for example Multi-Layer Perceptron (MLP NN, reveals the superiority of the proposed method. Because of superiority proposed method has higher accuracy, lower costs and simpler network structure.
Romani, Santina; Cevoli, Chiara; Fabbri, Angelo; Alessandrini, Laura; Dalla Rosa, Marco
2012-09-01
An electronic nose (EN) based on an array of 10 metal oxide semiconductor sensors was used, jointly with an artificial neural network (ANN), to predict coffee roasting degree. The flavor release evolution and the main physicochemical modifications (weight loss, density, moisture content, and surface color: L*, a*), during the roasting process of coffee, were monitored at different cooking times (0, 6, 8, 10, 14, 19 min). Principal component analysis (PCA) was used to reduce the dimensionality of sensors data set (600 values per sensor). The selected PCs were used as ANN input variables. Two types of ANN methods (multilayer perceptron [MLP] and general regression neural network [GRNN]) were used in order to estimate the EN signals. For both neural networks the input values were represented by scores of sensors data set PCs, while the output values were the quality parameter at different roasting times. Both the ANNs were able to well predict coffee roasting degree, giving good prediction results for both roasting time and coffee quality parameters. In particular, GRNN showed the highest prediction reliability. Actually the evaluation of coffee roasting degree is mainly a manned operation, substantially based on the empirical final color observation. For this reason it requires well-trained operators with a long professional skill. The coupling of e-nose and artificial neural networks (ANNs) may represent an effective possibility to roasting process automation and to set up a more reproducible procedure for final coffee bean quality characterization. © 2012 Institute of Food Technologists®
Rezaee, M. R.; Jafari, A.; Kazemzadeh, E.
2006-12-01
Accurate estimation of permeability from other data has been a challenge for many years. The aim of this study was to establish relationships between permeability, porosity and pore throat size, and apply these relationships in a predictive sense. Regression analysis was utilized to achieve a set of relationships between permeability, porosity and pore throat size for 144 carbonate samples. These relationships can be used to estimate permeability from porosity and pore throat radii. Also in this study, a fully-connected multi-layer perceptron network was used to predict permeability from porosity and pore throat radii. An artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving complex problems. These characteristics have enabled artificial neural networks to be more successful in predicting permeability when compared to regression analysis. This study also indicates that pore throat radii corresponding to a mercury saturation of 50% (r50) is the best permeability predictor for carbonates with complex pore networks.
Artificial neural network as the tool in prediction rheological features of raw minced meat.
Balejko, Jerzy A; Nowak, Zbigniew; Balejko, Edyta
2012-01-01
The aim of the study was to elaborate a method of modelling and forecasting rheological features which could be applied to raw minced meat at the stage of mixture preparation with a given ingredient composition. The investigated material contained pork and beef meat, pork fat, fat substitutes, ice and curing mixture in various proportions. Seven texture parameters were measured for each sample of raw minced meat. The data obtained were processed using the artificial neural network module in Statistica 9.0 software. The model that reached the lowest training error was a multi-layer perceptron MLP with three neural layers and architecture 7:7-11-7:7. Correlation coefficients between the experimental and calculated values in training, verification and testing subsets were similar and rather high (around 0.65) which indicated good network performance. High percentage of the total variance explained in PCA analysis (73.5%) indicated that the percentage composition of raw minced meat can be successfully used in the prediction of its rheological features. Statistical analysis of the results revealed, that artificial neural network model is able to predict rheological parameters and thus a complete texture profile of raw minced meat.
Veronezi, Carlos Cassiano Denipotti; de Azevedo Simões, Priscyla Waleska Targino; Dos Santos, Robson Luiz; da Rocha, Edroaldo Lummertz; Meláo, Suelen; de Mattos, Merisandra Côrtes; Cechinel, Cristian
2011-01-01
To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies.
Lo, Benjamin W Y; Macdonald, R Loch; Baker, Andrew; Levine, Mitchell A H
2013-01-01
The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.
Power prediction in mobile communication systems using an optimal neural-network structure.
Gao, X M; Gao, X Z; Tanskanen, J A; Ovaska, S J
1997-01-01
Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.
Ialongo, Cristiano; Pieri, Massimo; Bernardini, Sergio
2017-02-01
Diluting a sample to obtain a measure within the analytical range is a common task in clinical laboratories. However, for urgent samples, it can cause delays in test reporting, which can put patients' safety at risk. The aim of this work is to show a simple artificial neural network that can be used to make it unnecessary to predilute a sample using the information available through the laboratory information system. Particularly, the Multilayer Perceptron neural network built on a data set of 16,106 cardiac troponin I test records produced a correct inference rate of 100% for samples not requiring predilution and 86.2% for those requiring predilution. With respect to the inference reliability, the most relevant inputs were the presence of a cardiac event or surgery and the result of the previous assay. Therefore, such an artificial neural network can be easily implemented into a total automation framework to sensibly reduce the turnaround time of critical orders delayed by the operation required to retrieve, dilute, and retest the sample.
Determination of relaxation modulus of time-dependent materials using neural networks
Aulova, Alexandra; Govekar, Edvard; Emri, Igor
2017-08-01
Health monitoring systems for plastic based structures require the capability of real time tracking of changes in response to the time-dependent behavior of polymer based structures. The paper proposes artificial neural networks as a tool of solving inverse problem appearing within time-dependent material characterization, since the conventional methods are computationally demanding and cannot operate in the real time mode. Abilities of a Multilayer Perceptron (MLP) and a Radial Basis Function Neural Network (RBFN) to solve ill-posed inverse problems on an example of determination of a time-dependent relaxation modulus curve segment from constant strain rate tensile test data are investigated. The required modeling data composed of strain rate, tensile and related relaxation modulus were generated using existing closed-form solution. Several neural networks topologies were tested with respect to the structure of input data, and their performance was compared to an exponential fitting technique. Selected optimal topologies of MLP and RBFN were tested for generalization and robustness on noisy data; performance of all the modeling methods with respect to the number of data points in the input vector was analyzed as well. It was shown that MLP and RBFN are capable of solving inverse problems related to the determination of a time dependent relaxation modulus curve segment. Particular topologies demonstrate good generalization and robustness capabilities, where the topology of RBFN with data provided in parallel proved to be superior compared to other methods.
Directory of Open Access Journals (Sweden)
Rupinder Singh
2017-01-01
Full Text Available In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack. For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.
FLORISTIC DIVERSITY AND EQUITABILITY IN FOREST FRAGMENTS USING ARTIFICIAL NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
Christian Dias Cabacinha
2017-01-01
Full Text Available This study aimed to evaluate the predictive efficiency of Shannon index (H’ and Pielou Equitability index (J in forest fragments from the Brazilian Cerrado biome, from the vegetation indices and landscape metrics using artificial neural networks (ANN. Feedforward networks were used and they were trained through a back propagation error algorithm. The variables used as ANN input for simultaneous estimation of indices were: the categorical (H’ and J and the numbers related to the mean and standard deviation of vegetation indices (NDVI, SAVI, EVI, and MVI5, MVI7 and landscape metrics (AREA, GYRATE, SHAPE, CONTIG, CORE and ENN. It was generated five models of ANN from the functional relationships between numerical variables inherent to vegetation indices in two seasons, a dry season (June and a rainy season (February. The architecture of the networks was the Multilayer Perceptron (MLP, to estimate simultaneously the H’ and J: 500 using vegetation indices in the wet season (100 for each vegetation index and 500 in dry (100 for each vegetation index. The precision, accuracy and realism of biological ANN were assessed. The nets built during the rainy season and dry season that used vegetation indices MVI5 (Moisture Vegetation Index and SAVI (Soil Adjusted Vegetation Index, respectively, were more appropriate, accurate and biologically realistic to estimate both indices H’ and J. The ANN modeling demonstrated to be adequate to estimate the diversity index.
Directory of Open Access Journals (Sweden)
N. Sriraam
2011-01-01
Full Text Available A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.
Sriraam, N
2011-01-01
A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.
Wei, Yawei; Venayagamoorthy, Ganesh Kumar
2017-09-01
To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Artificial Neural Networks to Predict the Power Output of a PV Panel
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Valerio Lo Brano
2014-01-01
Full Text Available The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs for the power energy output forecasting of photovoltaic (PV modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP, a recursive neural network (RNN, and a gamma memory (GM trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.
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Benjamin W. Y. Lo
2013-01-01
Full Text Available Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH. Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients. Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs. Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.
A research about breast cancer detection using different neural networks and K-MICA algorithm.
Kalteh, A A; Zarbakhsh, Payam; Jirabadi, Meysam; Addeh, Jalil
2013-01-01
Breast cancer is the second leading cause of death for women all over the world. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paper presents a novel hybrid intelligent method for detection of breast cancer. The proposed method includes two main modules: Clustering module and the classifier module. In the clustering module, first the input data will be clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks and the radial basis function neural networks are investigated. Using the experimental study, we choose the best classifier in order to recognize the breast cancer. The proposed system is tested on Wisconsin Breast Cancer (WBC) database and the simulation results show that the recommended system has high accuracy.
Porosity Estimation By Artificial Neural Networks Inversion . Application to Algerian South Field
Eladj, Said; Aliouane, Leila; Ouadfeul, Sid-Ali
2017-04-01
One of the main geophysicist's current challenge is the discovery and the study of stratigraphic traps, this last is a difficult task and requires a very fine analysis of the seismic data. The seismic data inversion allows obtaining lithological and stratigraphic information for the reservoir characterization . However, when solving the inverse problem we encounter difficult problems such as: Non-existence and non-uniqueness of the solution add to this the instability of the processing algorithm. Therefore, uncertainties in the data and the non-linearity of the relationship between the data and the parameters must be taken seriously. In this case, the artificial intelligence techniques such as Artificial Neural Networks(ANN) is used to resolve this ambiguity, this can be done by integrating different physical properties data which requires a supervised learning methods. In this work, we invert the acoustic impedance 3D seismic cube using the colored inversion method, then, the introduction of the acoustic impedance volume resulting from the first step as an input of based model inversion method allows to calculate the Porosity volume using the Multilayer Perceptron Artificial Neural Network. Application to an Algerian South hydrocarbon field clearly demonstrate the power of the proposed processing technique to predict the porosity for seismic data, obtained results can be used for reserves estimation, permeability prediction, recovery factor and reservoir monitoring. Keywords: Artificial Neural Networks, inversion, non-uniqueness , nonlinear, 3D porosity volume, reservoir characterization .
A research about breast cancer detection using different neural networks and K-MICA algorithm
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A A Kalteh
2013-01-01
Full Text Available Breast cancer is the second leading cause of death for women all over the world. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paper presents a novel hybrid intelligent method for detection of breast cancer. The proposed method includes two main modules: Clustering module and the classifier module. In the clustering module, first the input data will be clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA and K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks and the radial basis function neural networks are investigated. Using the experimental study, we choose the best classifier in order to recognize the breast cancer. The proposed system is tested on Wisconsin Breast Cancer (WBC database and the simulation results show that the recommended system has high accuracy.
A morphological perceptron with gradient-based learning for Brazilian stock market forecasting.
Araújo, Ricardo de A
2012-04-01
Several linear and non-linear techniques have been proposed to solve the stock market forecasting problem. However, a limitation arises from all these techniques and is known as the random walk dilemma (RWD). In this scenario, forecasts generated by arbitrary models have a characteristic one step ahead delay with respect to the time series values, so that, there is a time phase distortion in stock market phenomena reconstruction. In this paper, we propose a suitable model inspired by concepts in mathematical morphology (MM) and lattice theory (LT). This model is generically called the increasing morphological perceptron (IMP). Also, we present a gradient steepest descent method to design the proposed IMP based on ideas from the back-propagation (BP) algorithm and using a systematic approach to overcome the problem of non-differentiability of morphological operations. Into the learning process we have included a procedure to overcome the RWD, which is an automatic correction step that is geared toward eliminating time phase distortions that occur in stock market phenomena. Furthermore, an experimental analysis is conducted with the IMP using four complex non-linear problems of time series forecasting from the Brazilian stock market. Additionally, two natural phenomena time series are used to assess forecasting performance of the proposed IMP with other non financial time series. At the end, the obtained results are discussed and compared to results found using models recently proposed in the literature. Copyright © 2011 Elsevier Ltd. All rights reserved.
Middleton, Ian; Damper, Robert I
2004-01-01
Segmentation of medical images is very important for clinical research and diagnosis, leading to a requirement for robust automatic methods. This paper reports on the combined use of a neural network (a multilayer perceptron, MLP) and active contour model ('snake') to segment structures in magnetic resonance (MR) images. The perceptron is trained to produce a binary classification of each pixel as either a boundary or a non-boundary point. Subsequently, the resulting binary (edge-point) image forms the external energy function for a snake, used to link the candidate boundary points into a continuous, closed contour. We report here on the segmentation of the lungs from multiple MR slices of the torso; lung-specific constraints have been avoided to keep the technique as general as possible. In initial investigations, the inputs to the MLP were limited to normalised intensity values of the pixels from an (7 x 7) window scanned across the image. The use of spatial coordinates as additional inputs to the MLP is then shown to provide an improvement in segmentation performance as quantified using the effectiveness measure (a weighted product of precision and recall). Training sets were first developed using a lengthy iterative process. Thereafter, a novel cost function based on effectiveness is proposed for training that allows us to achieve dramatic improvements in segmentation performance, as well as faster, non-iterative selection of training examples. The classifications produced using this cost function were sufficiently good that the binary image produced by the MLP could be post-processed using an active contour model to provide an accurate segmentation of the lungs from the multiple slices in almost all cases, including unseen slices and subjects.
AN APPLICATION OF SPEAKER RECOGNITION USING ARTIFICIAL NEURAL NETWORKS
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Murat CANER
2006-02-01
Full Text Available In this study an artificial neural network (ANN is implemented, which has been used frequently as an implementation model in recent years, to recognize speaker identification. Generally, recognition is consist of three stages that, processing of signal, obtaining attributes and comparing them. Speech samples are transformed into digital data according to voice card of PC. In the analysis of voice stage, recurrent periods and white noise of voice data are trimmed by hamming window method and voice attribute part of the digital data is obtained. For obtaining attribute of voice data LPC (linear predictive coding and DFT (discrete fourier transform methods are used. Of those 28 coefficents, that is used for speaker recognition, 16 were obtained by the analysis of DFT and 12 were obtained by the analysis of LPC. The parameters that represent speaker voice, is used for training and test of ANN. Multilayer perceptron model is used as an architecture of ANN and backpropagation algorithm is used for training method. Voices of "a" is taken from 7 different person and their attributes are found. ANN is trained with these features to find the speaker who is the owner of the sample voice. And then using the test data that is not used for training part, recognition achievement of ANN is tested. As a result, good results were obtained with low failure rate.
Inflow forecasting using Artificial Neural Networks for reservoir operation
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C. Chiamsathit
2016-05-01
Full Text Available In this study, multi-layer perceptron (MLP artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1 inflow known and assumed to be the historic (Type A; (2 inflow known and assumed to be the forecast (Type F; (3 inflow known and assumed to be the historic mean for month (Type M; and (4 inflow is unknown with release decision only conditioned on the starting reservoir storage (Type N. Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. It was found that Type F inflow situation produced the best performance while Type N was the worst performing. This clearly demonstrates the importance of good inflow information for effective reservoir operation.
Neural network feature selection for breast cancer diagnosis
Kocur, Catherine M.; Rogers, Steven K.; Bauer, Kenneth W., Jr.; Steppe, Jean M.; Hoffmeister, Jeffrey W.
1995-04-01
More than 50 million women over the age of 40 are currently at risk for breast cancer in the United States. Computer-aided diagnosis, as a second opinion to radiologists, will aid in decreasing the number of false readings of mammograms. Neural network benefits are exploited at both the classification and feature selection stages in the development of a computer-aided breast cancer diagnostic system. The multilayer perceptron is used to classify and contrast three features (angular second moment, eigenmasses, and wavelets) developed to distinguish benign from malignant lesion in a database of 94 difficult-to-diagnose digitized microcalcification cases. System performance of 74 percent correct classifications is achieved. Feature selection techniques are presented which further improve performance. Neural and decision boundary-based methods are implemented, compared, and validated to isolate and remove useless features. The contribution from this analysis is an increase to 88 percent correct classification in system performance. These feature selection techniques can also process risk factor data.
Artificial Neural Network versus Linear Models Forecasting Doha Stock Market
Yousif, Adil; Elfaki, Faiz
2017-12-01
The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.
Poswar, Fabiano de Oliveira; Farias, Lucyana Conceição; Fraga, Carlos Alberto de Carvalho; Bambirra, Wilson; Brito-Júnior, Manoel; Sousa-Neto, Manoel Damião; Santos, Sérgio Henrique Souza; de Paula, Alfredo Maurício Batista; D'Angelo, Marcos Flávio Silveira Vasconcelos; Guimarães, André Luiz Sena
2015-06-01
Bioinformatics has emerged as an important tool to analyze the large amount of data generated by research in different diseases. In this study, gene expression for radicular cysts (RCs) and periapical granulomas (PGs) was characterized based on a leader gene approach. A validated bioinformatics algorithm was applied to identify leader genes for RCs and PGs. Genes related to RCs and PGs were first identified in PubMed, GenBank, GeneAtlas, and GeneCards databases. The Web-available STRING software (The European Molecular Biology Laboratory [EMBL], Heidelberg, Baden-Württemberg, Germany) was used in order to build the interaction map among the identified genes by a significance score named weighted number of links. Based on the weighted number of links, genes were clustered using k-means. The genes in the highest cluster were considered leader genes. Multilayer perceptron neural network analysis was used as a complementary supplement for gene classification. For RCs, the suggested leader genes were TP53 and EP300, whereas PGs were associated with IL2RG, CCL2, CCL4, CCL5, CCR1, CCR3, and CCR5 genes. Our data revealed different gene expression for RCs and PGs, suggesting that not only the inflammatory nature but also other biological processes might differentiate RCs and PGs. Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.
Krajišnik, Danina; Stepanović-Petrović, Radica; Tomić, Maja; Micov, Ana; Ibrić, Svetlana; Milić, Jela
2014-04-01
In this study, utilization of artificial neural network (ANN) models [static-multilayer perceptron (MLP) and generalized regression neural networks and dynamic-gamma one-layer network and recurrent one-layer network] for prediction of diclofenac sodium (DS) release from drug-cationic surfactant-modified zeolites physical mixtures comprising different surfactant/drug molar ratio (0.2-2.5) was performed. The inputs for ANNs trainings were surfactant/drug molar ratios, that is, drug loadings in the drug-modified zeolite mixtures, whereas the outputs were percents of drug release in predetermined time points during drug release test (8 h). The obtained results revealed that MLP showed the highest correlation between experimental and predicted drug release. The safety of both natural and cationic surfactant-modified zeolite as a potential excipient was confirmed in an acute toxicity testing during 72 h. DS (1.5, 5, 10, mg/kg, p.o.) as well as DS-modified zeolites mixtures produced a significant dose-dependent reduction of the rat paw edema induced by proinflammatory agent carrageenan. DS antiedematous effect was intensified and prolonged significantly by modified zeolite. These results could suggest the potential improvement in the treatment of inflammation by DS-modified zeolite mixtures. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.
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Namık KılıÇ
2015-06-01
Full Text Available Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods (FEM in this research field. The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort, therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time. This study aims to apply a hybrid method using FEM simulation and artificial neural network (ANN analysis to approximate ballistic limit thickness for armor steels. To achieve this objective, a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition. In this methodology, the FEM simulations are used to create training cases for Multilayer Perceptron (MLP three layer networks. In order to validate FE simulation methodology, ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569. Afterwards, the successfully trained ANN(s is used to predict the ballistic limit thickness of 500 HB high hardness steel armor. Results show that even with limited number of data, FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.
Braga, C C
2001-01-01
A neural network structure has been used for unfolding neutron spectra measured by means of a Bonner Sphere Spectrometer set and a foil activation set using several neutron induced reactions. The present work used the SNNS (Stuttgart Neural Network Simulator) as the interface for designing, training and validation of the Multilayer Perceptron network. The back-propagation algorithm was applied. The Bonner Sphere set chosen has been calibrated at the National Physical Laboratory, United Kingdom, and uses gold activation foils as thermal neutron detectors. The neutron energy covered by the response functions goes from 0.0001 eV to 14 MeV. The foil activation set chosen has been irradiated at the IEA-R1 research reactor and measured at the Nuclear Metrology Laboratory of IPEN-CNEN/SP. Two types of neutron spectra were numerically investigated: monoenergetic and continuous The unfolded spectra were compared to a conventional method using code SAND-II as part of the neutron dosimetry system SAIPS. Good results wer...
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Pablo García
2013-06-01
Full Text Available Thanks to the built in intelligence (deployment of new intelligent devices and sensors in places where historically they were not present, the Smart Grid and Microgrid paradigms are able to take advantage from aggregated load forecasting, which opens the door for the implementation of new algorithms to seize this information for optimization and advanced planning. Therefore, accuracy in load forecasts will potentially have a big impact on key operation factors for the future Smart Grid/Microgrid-based energy network like user satisfaction and resource saving, and new methods to achieve an efficient prediction in future energy landscapes (very different from the centralized, big area networks studied so far. This paper proposes different improved models to forecast next day’s aggregated load using artificial neural networks, taking into account the variables that are most relevant for the aggregated. In particular, seven models based on the multilayer perceptron will be proposed, progressively adding input variables after analyzing the influence of climate factors on aggregated load. The results section presents the forecast from the proposed models, obtained from real data.
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Bahman O. Taha
2015-06-01
Full Text Available The reinforced concrete with fiber reinforced polymer (FRP bars (carbon, aramid, basalt and glass is used in places where a high ratio of strength to weight is required and corrosion is not acceptable. Behavior of structural members using (FRP bars is hard to be modeled using traditional methods because of the high non-linearity relationship among factors influencing the strength of structural members. Back-propagation neural network is a very effective method for modeling such complicated relationships. In this paper, back-propagation neural network is used for modeling the flexural behavior of beams reinforced with (FRP bars. 101 samples of beams reinforced with fiber bars were collected from literatures. Five important factors are taken in consideration for predicting the strength of beams. Two models of Multilayer Perceptron (MLP are created, first with single-hidden layer and the second with two-hidden layers. The two-hidden layer model showed better accuracy ratio than the single-hidden layer model. Parametric study has been done for two-hidden layer model only. Equations are derived to be used instead of the model and the importance of input factors is determined. Results showed that the neural network is successful in modeling the behavior of concrete beams reinforced with different types of (FRP bars.
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Ozren Bukovac
2016-01-01
Full Text Available Compared to the other marine engines for ship propulsion, turbocharged two-stroke low speed diesel engines have advantages due to their high efficiency and reliability. Modern low speed ”intelligent” marine diesel engines have a flexibility in its operation due to the variable fuel injection strategy and management of the exhaust valve drive. This paper carried out verified zerodimensional numerical simulations which have been used for MLP (Multilayer Perceptron neural network predictions of marine two-stroke low speed diesel engine steady state performances. The developed MLP neural network was used for marine engine optimized operation control. The paper presents an example of achieving lowest specific fuel consumption and for minimization of the cylinder process highest temperature for reducing NOx emission. Also, the developed neural network was used to achieve optimal exhaust gases heat flow for utilization. The obtained data maps give insight into the optimal working areas of simulated marine diesel engine, depending on the selected start of the fuel injection (SOI and the time of the exhaust valve opening (EVO.
Tarasov, D. A.; Buevich, A. G.; Sergeev, A. P.; Shichkin, A. V.; Baglaeva, E. M.
2017-06-01
Forecasting the soil pollution is a considerable field of study in the light of the general concern of environmental protection issues. Due to the variation of content and spatial heterogeneity of pollutants distribution at urban areas, the conventional spatial interpolation models implemented in many GIS packages mostly cannot provide appreciate interpolation accuracy. Moreover, the problem of prediction the distribution of the element with high variability in the concentration at the study site is particularly difficult. The work presents two neural networks models forecasting a spatial content of the abnormally distributed soil pollutant (Cr) at a particular location of the subarctic Novy Urengoy, Russia. A method of generalized regression neural network (GRNN) was compared to a common multilayer perceptron (MLP) model. The proposed techniques have been built, implemented and tested using ArcGIS and MATLAB. To verify the models performances, 150 scattered input data points (pollutant concentrations) have been selected from 8.5 km2 area and then split into independent training data set (105 points) and validation data set (45 points). The training data set was generated for the interpolation using ordinary kriging while the validation data set was used to test their accuracies. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. The predictive accuracy of both models was confirmed to be significantly higher than those achieved by the geostatistical approach (kriging). It is shown that MLP could achieve better accuracy than both kriging and even GRNN for interpolating surfaces.
Rodríguez-Entrena, Macario; Salazar-Ordóñez, Melania; Becerra-Alonso, David
2016-03-30
This paper studies which of the attitudinal, cognitive and socio-economic factors determine the willingness to purchase genetically modified (GM) food, enabling the forecasting of consumers' behaviour in Andalusia, southern Spain. This classification has been made by a standard multilayer perceptron neural network trained with extreme learning machine. Later, an ordered logistic regression was applied to determine whether the neural network can outperform this traditional econometric approach. The results show that the highest relative contributions lie in the variables related to perceived risks of GM food, while the perceived benefits have a lower influence. In addition, an innovative attitude towards food presents a strong link, as does the perception of food safety. The variables with the least relative contribution are subjective knowledge about GM food and the consumers' age. The neural network approach outperforms the correct classification percentage from the ordered logistic regression. The perceived risks must be considered as a critical factor. A strategy to improve the GM food acceptance is to develop a transparent and balanced information framework that makes the potential risk understandable by society, and make them aware of the risk assessments for GM food in the EU. For its success, it is essential to improve the trust in EU institutions and scientific regulatory authorities. © 2015 Society of Chemical Industry.
Adhesion on Nanoorganized Multilayers
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Yolla Kazzi
2011-01-01
Full Text Available Nanostructured multilayers composed of alternate organic (alkyldithiol and metallic (gold layers are grafted onto glass plates and prepared in order to modify the mechanical and local dissipative properties of a thin surface layer of the substrate. The adhesion phenomenon between a polyisoprene elastomer and these layers is presented and verified by two theories, namely, Johnson, Kendall, Roberts (JKR and linear elastic fracture mechanics. The increase in adhesion with contact time following a power law has been clearly noted.
A Neural Network Classifier Model for Forecasting Safety Behavior at Workplaces
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Fakhradin Ghasemi
2017-07-01
Full Text Available The construction industry is notorious for having an unacceptable rate of fatal accidents. Unsafe behavior has been recognized as the main cause of most accidents occurring at workplaces, particularly construction sites. Having a predictive model of safety behavior can be helpful in preventing construction accidents. The aim of the present study was to build a predictive model of unsafe behavior using the Artificial Neural Network approach. A brief literature review was conducted on factors affecting safe behavior at workplaces and nine factors were selected to be included in the study. Data were gathered using a validated questionnaire from several construction sites. Multilayer perceptron approach was utilized for constructing the desired neural network. Several models with various architectures were tested to find the best one. Sensitivity analysis was conducted to find the most influential factors. The model with one hidden layer containing fourteen hidden neurons demonstrated the best performance (Sum of Squared Errors=6.73. The error rate of the model was approximately 21 percent. The results of sensitivity analysis showed that safety attitude, safety knowledge, supportive environment, and management commitment had the highest effects on safety behavior, while the effects from resource allocation and perceived work pressure were identified to be lower than those of others. The complex nature of human behavior at workplaces and the presence of many influential factors make it difficult to achieve a model with perfect performance.
Sergeev, A. P.; Tarasov, D. A.; Buevich, A. G.; Shichkin, A. V.; Tyagunov, A. G.; Medvedev, A. N.
2017-06-01
Modeling of spatial distribution of pollutants in the urbanized territories is difficult, especially if there are multiple emission sources. When monitoring such territories, it is often impossible to arrange the necessary detailed sampling. Because of this, the usual methods of analysis and forecasting based on geostatistics are often less effective. Approaches based on artificial neural networks (ANNs) demonstrate the best results under these circumstances. This study compares two models based on ANNs, which are multilayer perceptron (MLP) and generalized regression neural networks (GRNNs) with the base geostatistical method - kriging. Models of the spatial dust distribution in the snow cover around the existing copper quarry and in the area of emissions of a nickel factory were created. To assess the effectiveness of the models three indices were used: the mean absolute error (MAE), the root-mean-square error (RMSE), and the relative root-mean-square error (RRMSE). Taking into account all indices the model of GRNN proved to be the most accurate which included coordinates of the sampling points and the distance to the likely emission source as input parameters for the modeling. Maps of spatial dust distribution in the snow cover were created in the study area. It has been shown that the models based on ANNs were more accurate than the kriging, particularly in the context of a limited data set.
Use of artificial neural networks and geographic objects for classifying remote sensing imagery
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Pedro Resende Silva
2014-06-01
Full Text Available The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1 to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2 to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3 to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.
Wang, Baijie; Wang, Xin; Chen, Zhangxin
2013-08-01
Reservoir characterization refers to the process of quantitatively assigning reservoir properties using all available field data. Artificial neural networks (ANN) have recently been introduced to solve reservoir characterization problems dealing with the complex underlying relationships inherent in well log data. Despite the utility of ANNs, the current limitation is that most existing applications simply focus on directly implementing existing ANN models instead of improving/customizing them to fit the specific reservoir characterization tasks at hand. In this paper, we propose a novel intelligent framework that integrates fuzzy ranking (FR) and multilayer perceptron (MLP) neural networks for reservoir characterization. FR can automatically identify a minimum subset of well log data as neural inputs, and the MLP is trained to learn the complex correlations from the selected well log data to a target reservoir property. FR guarantees the selection of the optimal subset of representative data from the overall well log data set for the characterization of a specific reservoir property; and, this implicitly improves the modeling and predication accuracy of the MLP. In addition, a growing number of industrial agencies are implementing geographic information systems (GIS) in field data management; and, we have designed the GFAR solution (GIS-based FR ANN Reservoir characterization solution) system, which integrates the proposed framework into a GIS system that provides an efficient characterization solution. Three separate petroleum wells from southwestern Alberta, Canada, were used in the presented case study of reservoir porosity characterization. Our experiments demonstrate that our method can generate reliable results.
Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks
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E. Chatzimichail
2013-01-01
Full Text Available The long-term solution to the asthma epidemic is believed to be prevention and not treatment of the established disease. Most cases of asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life counts as an important priority. Artificial neural networks (ANN have been already utilized in medicine in order to improve the performance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of persistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to the persistent asthma have been chosen. Multilayer perceptron and probabilistic neural networks topologies have been investigated in order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the asthma outcome with a success of 96.77%. The ANN, with which these high rates of reliability were obtained, will help the doctors to identify which of the young patients are at a high risk of asthma disease progression. Moreover, this may lead to better treatment opportunities and hopefully better disease outcomes in adulthood.
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E. Khalilzadeh Vahidi
2016-01-01
Full Text Available The effects of different parameters on steel plate shear wall (SPSW are investigated. The studied parameters are thickness of plate, location of the opening, thickness of diagonal stiffeners, and thickness of circular stiffener. Load-carrying capacity of the SPSW is studied under static load using nonlinear geometrical and material analysis in ABAQUS and the obtained simulation results are verified. An artificial neural network (ANN is proposed to model the effects of these parameters. According to the results the circular stiffener has more effect compared with the diagonal stiffeners. However, the thickness of the plate has the most significant effect on the SPSW behavior. The results show that the best place for the opening location is the center of SPSW. Multilayer perceptron (MLP neural network was used to predict the maximum load in SPSW with opening. The predicted maximum load values using the proposed MLP model were compared with the simulated validated data. The obtained results show that the proposed ANN model has achieved good agreement with the validated simulated data, with correlation coefficient of more than 0.9975. Therefore, the proposed model is useful, reliable, fast, and cheap tools to predict the maximum load in SPSW.
Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad
2016-11-01
In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.
Satellite image classification by narrowband Gabor filters and artificial neural networks
Nezamoddini-Kachouie, Nezamoddin; Alirezaie, Javad
2004-05-01
Satellite image segmentation is an important task to generate classification maps. Land areas are classified and clustered into groups of similar land cover or land use by segmentation of satellite images. It may be broad classification such as urban, forested, open fields and water or may be more specific such as differentiating corn, soybean, beet and wheat fields. One of the most important among them is partitioning the urban area to different regions. On the other hand Multi-Channel filtering is used widely for texture segmentation by many researchers. This paper describes a texture segmentation algorithm to segment satellite images using Gabor filter bank and neural networks. In the proposed method feature vectors are extracted by multi-channel decomposition. The spatial/spatial-frequency features of the input satellite image are extracted by optimized Gabor filter bank. Some important considerations about filter parameters, filter bank coverage in frequency domain and the reduction of feature dimensions are discussed. A competitive network is trained to extract the best features and to reduce the feature dimension. Eventually a Multi-Layer Perceptron (MLP) is employed to accomplish the segmentation task. Our MLP uses the sigmoid transfer function in all layers and during the training, random selected feature vectors are assigned to proper classes. After MLP is trained the optimized extracted features are classified into sections according to the textured land cover regions.
Optimization of bending sequence in roll forming using neural network and genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Park, Hong Seok; Anh, Tran Viet [University of Ulsan, Ulsan (Korea, Republic of)
2011-08-15
In the roll forming process, the bending sequence plays a major role in the product quality. The optimal bending sequence results in the smallest number of passes and the flawless process. This paper presents a new optimization procedure of bending sequence in a roll forming process. The multilayer perceptron is used to build the neural network (NN), which models the variation of longitudinal strain in process while the genetic algorithm (GA) is employed to optimize the bending sequence. The data used for training the network is automatically obtained by the integration between CAD and CAE. The values of peak longitudinal strains are maximized while the number of passes is reduced to the smallest and the constraint conditions being set on the maximal longitudinal strain to avoid buckling. The overbending at final pass after spring back is also considered in this paper. Two roll forming processes are optimized in order to prove applicability and efficiency of the optimization procedure. This method maintains the longitudinal strain less than the buckling limit, whereas reducing the number of passes to the smallest. Thus, the advantages of the proposed method show the high applicability in designing and optimizing the bending sequence in the roll forming process.
Improvement of radiation dose estimation due to nuclear accidents using deep neural network and GPU
Energy Technology Data Exchange (ETDEWEB)
Desterro, Filipe S.M.; Almeida, Adino A.H.; Pereira, Claudio M.N.A., E-mail: filipesantana18@gmail.com, E-mail: adino@ien.gov.br, E-mail: cmcoelho@ien.gov.br [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)
2017-07-01
Recently, the use of mobile devices has been proposed for dose assessment during nuclear accidents. The idea is to support field teams, providing an approximated estimation of the dose distribution map in the vicinity of the nuclear power plant (NPP), without needing to be connected to the NPP systems. In order to provide such stand-alone execution, the use of artificial neural networks (ANN) has been proposed in substitution of the complex and time consuming physical models executed by the atmospheric dispersion radionuclide (ADR) system. One limitation observed on such approach is the very time-consuming training of the ANNs. Moreover, if the number of input parameters increases the performance of standard ANNs, like Multilayer-Perceptron (MLP) with backpropagation training, is affected leading to unreasonable training time. To improve learning, allowing better dose estimations, more complex ANN architectures are required. ANNs with many layers (much more than a typical number of layers), referred to as Deep Neural Networks (DNN), for example, have demonstrating to achieve better results. On the other hand, the training of such ANNs is very much slow. In order to allow the use of such DNNs in a reasonable training time, a parallel programming solution, using Graphic Processing Units (GPU) and Computing Unified Device Architecture (CUDA) is proposed. This work focuses on the study of computational technologies for improvement of the ANNs to be used in the mobile application, as well as their training algorithms. (author)
Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B
2016-08-01
Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous
Increasing spatial resolution of CHIRPS rainfall datasets for Cyprus with artificial neural networks
Tymvios, Filippos; Michaelides, Silas; Retalis, Adrianos; Katsanos, Dimitrios; Lelieveld, Jos
2016-08-01
The use of high resolution rainfall datasets is an alternative way of studying climatological regions where conventional rain measurements are sparse or not available. Starting in 1981 to near-present, the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) dataset incorporates a 5km×5km resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis, severe events and seasonal drought monitoring. The aim of this work is to further increase the resolution of the rainfall dataset for Cyprus to 1km×1km, by correlating the CHIRPS dataset with elevation information, the NDVI index (Normalized Difference Vegetation Index) from satellite images at 1km×1km and precipitation measurements from the official raingauge network of the Cyprus' Department of Meteorology, utilizing Artificial Neural Networks. The Artificial Neural Networks' architecture that was implemented is the Multi-Layer Perceptron (MLP) trained with the back propagation method, which is widely used in environmental studies. Seven different network architectures were tested, all with two hidden layers. The number of neurons ranged from 3 to10 in the first hidden layer and from 5 to 25 in the second hidden layer. The dataset was separated into a randomly selected training set, a validation set and a testing set; the latter is independently used for the final assessment of the models' performance. Using the Artificial Neural Network approach, a new map of the spatial analysis of rainfall is constructed which exhibits a considerable increase in its spatial resolution. A statistical assessment of the new spatial analysis was made using the rainfall ground measurements from the raingauge network. The assessment indicates that the methodology is promising for several applications.
Geodesign the multilayered water safety
Sophronides, Panayiotis; Steenbruggen, John; Scholten, Henk J.; Giaoutzi, Maria
2016-01-01
This paper aims to frame the multi-layered water safety concept in the context of a systematic, thorough, multidisciplinary and collaborative methodology for complex problems solving, i.e. geodesign. Multi-layered safety is an integrated flood risk management (FRM) concept based not only on flood
Hyperbolic metamaterials beyond simple multilayers
DEFF Research Database (Denmark)
Zhukovsky, Sergei; Andryieuski, Andrei; Lavrinenko, Andrei
2014-01-01
Highly corrugated surfaces, nanoparticle assemblies, and super- structured multilayers offer superior functionality in controlling propagating volume plasmons with large wave vectors.......Highly corrugated surfaces, nanoparticle assemblies, and super- structured multilayers offer superior functionality in controlling propagating volume plasmons with large wave vectors....
Perrone, Antonio L.; Castiglione, Patrizia; Basti, Gianfranco; Messi, Roberto
1994-03-01
In the context of M. Minsky's and S. Papert's theorems on the impossibility of evaluating simple linear predicates by parallel architectures we want to show how these limitations can be avoided by introducing a generalized input-dependent preprocessing technique that does not suppose any a-priori knowledge of input like in classical input filtering procedures. This technique can be formalized in a very general way and can be also deduced by meta- mathematical arguments. A further development of the same technique can be applied at level of learning procedure to introduce in such a way the complete notion of `dynamic perception'. From the experimental standpoint, we show two applications of the dynamic perceptron in particle track recognition in high-energy accelerators. Firstly, we show the amazing improvement of performances that can be obtained in a perceptron architecture with classical learning by adding our dynamic preprocessing technique, already introduced last year in another paper presented at this Conference. Secondly, we show the first results of this technique extended also at the level of learning procedure always applied to the problem of particle track recognition.
Maran, J Prakash; Priya, B
2015-03-01
The present study is to evaluate and compare the prediction and simulating efficiencies of response surface methodology (RSM) and artificial neural network (ANN) based models on fatty acid methyl esters (FAME) yield achieved from muskmelon oil (MMO) under ultrasonication by two step in situ process. In first in situ process, free fatty acid content of MMO was reduced from 6.43% to 0.91% using H2SO4 as acid catalyst and organic phase in the first step was subjected to second reaction by adding KOH in methanol as basic catalyst. The influence of process variables (methanol to oil molar ratio, catalyst concentration, reaction temperature and reaction time) on conversion of FAME (second step) was investigated by central composite rotatable design (CCRD) of RSM and Multi-Layer Perceptron (MLP) neural network with the topology of 4-7-1. Both (RSM and ANN) were statistically compared by the coefficient of determination, root mean square error and absolute average deviation, based on the validation data set. The coefficient of determination (R(2)) calculated from the validation data for RSM and ANN models were 0.869 and 0.991 respectively. While both models showed good predictions in this study. But, the ANN model was more precise compared to the RSM model and it showed that, ANN is to be a powerful tool for modeling and optimizing FAME production. Copyright © 2014 Elsevier B.V. All rights reserved.
Rai, H. M.; Trivedi, A.; Chatterjee, K.; Shukla, S.
2014-01-01
This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.
Eslamizadeh, Gholamhossein; Barati, Ramin
2017-05-01
Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart sounds using wavelet transformer. The network inputs are features extracted from individual heart cycles, and two classification outputs. Classification accuracy of the proposed model is compared with five multilayer perceptron trained with Levenberg-Marquardt, Extreme-learning-machine, back-propagation, simulated-annealing, and neighbor-annealing algorithms. It is also compared with a Self-Organizing Map (SOM) ANN. The proposed model is trained and tested using real heart sounds available in the Pascal database to show the applicability of the proposed scheme. Also, a device to record real heart sounds has been developed and used for comparison purposes too. Based on the results of this study, MNA can be used to produce considerable results as a heart cycle classifier. Copyright © 2017 Elsevier B.V. All rights reserved.
Quang Truong, Dinh; Ahn, Kyoung Kwan
2014-07-01
An ion polymer metal composite (IPMC) is an electroactive polymer that bends in response to a small applied electric field as a result of mobility of cations in the polymer network and vice versa. This paper presents an innovative and accurate nonlinear black-box model (NBBM) for estimating the bending behavior of IPMC actuators. The model is constructed via a general multilayer perceptron neural network (GMLPNN) integrated with a smart learning mechanism (SLM) that is based on an extended Kalman filter with self-decoupling ability (SDEKF). Here the GMLPNN is built with an ability to autoadjust its structure based on its characteristic vector. Furthermore, by using the SLM based on the SDEKF, the GMLPNN parameters are optimized with small computational effort, and the modeling accuracy is improved. An apparatus employing an IPMC actuator is first set up to investigate the IPMC characteristics and to generate the data for training and validating the model. The advanced NBBM model for the IPMC system is then created with the proper inputs to estimate IPMC tip displacement. Next, the model is optimized using the SLM mechanism with the training data. Finally, the optimized NBBM model is verified with the validating data. A comparison between this model and the previously developed model is also carried out to prove the effectiveness of the proposed modeling technique.
Directory of Open Access Journals (Sweden)
Edia E.O.
2010-10-01
Full Text Available Despite their importance in stream management, the aquatic insect assemblages are still little known in West Africa. This is particularly true in South-Eastern Ivory Coast, where aquatic insect assemblages were hardly studied. We therefore aimed at characterising aquatic insect assemblages on four coastal rivers in South-Eastern Ivory Coast. Patterning aquatic insect assemblages was achieved using a Self-Organizing Map (SOM, an unsupervised Artificial Neural Networks (ANN method. This method was applied to pattern the samples based on the richness of five major orders of aquatic insects (Diptera, Ephemeroptera, Coleoptera, Trichoptera and Odonata. This permitted to identify three clusters that were mainly related to the local environmental status of sampling sites. Then, we used the environmental characteristics of the sites to predict, using a multilayer perceptron neural network (MLP, trained by BackPropagation algorithm (BP, a supervised ANN, the richness of the five insect orders. The BP showed high predictability (0.90 for both Diptera and Trichoptera, 0.84 for both Coleoptera and Odonata, 0.69 for Ephemeroptera. The most contributing variables in predicting the five insect order richness were pH, conductivity, total dissolved solids, water temperature, percentage of rock and the canopy. This underlines the crucial influence of both instream characteristics and riparian context.
Directory of Open Access Journals (Sweden)
Min Oh
Full Text Available The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer's disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer's disease.
Energy Technology Data Exchange (ETDEWEB)
Hood, Randolph Quentin [Univ. of California, Berkeley, CA (United States)
1994-04-01
Utilizing self-consistent Hartree-Fock calculations, several aspects of multilayers and interfaces are explored: enhancement and reduction of the local magnetic moments, magnetic coupling at the interfaces, magnetic arrangements within each film and among non-neighboring films, global symmetry of the systems, frustration, orientation of the various moments with respect to an outside applied field, and magnetic-field induced transitions. Magnetoresistance of ferromagnetic-normal-metal multilayers is found by solving the Boltzmann equation. Results explain the giant negative magnetoresistance encountered in these systems when an initial antiparallel arrangement is changed into a parallel configuration by an external magnetic field. The calculation depends on (1) geometric parameters (thicknesses of layers), (2) intrinsic metal parameters (number of conduction electrons, magnetization, and effective masses in layers), (3) bulk sample properties (conductivity relaxation times), (4) interface scattering properties (diffuse scattering versus potential scattering at the interfaces, and (5) outer surface scattering properties (specular versus diffuse surface scattering). It is found that a large negative magnetoresistance requires considerable asymmetry in interface scattering for the two spin orientations. Features of the interfaces that may produce an asymmetrical spin-dependent scattering are studied: varying interfacial geometric random roughness with no lateral coherence, correlated (quasi-periodic) roughness, and varying chemical composition of the interfaces. The interplay between these aspects of the interfaces may enhance or suppress the magnetoresistance, depending on whether it increases or decreases the asymmetry in the spin-dependent scattering of the conduction electrons.
Energy Technology Data Exchange (ETDEWEB)
Chrzan, D.C.; Dugger, M.; Follstaedt, D.M.; Friedman, Lawrence H.; Friedmann, T.A.; Knapp, J.A.; McCarty, K.F.; Medlin, D.L.; Mirkarimi, P.B.; Missert, N.; Newcomer, P.P.; Sullivan, J.P.; Tallant, D.R.
1999-05-01
We have developed a new multilayer a-tC material that is thick stress-free, adherent, low friction, and with hardness and stiffness near that of diamond. The new a-tC material is deposited by J pulsed-laser deposition (PLD) at room temperature, and fully stress-relieved by a short thermal anneal at 600°C. A thick multilayer is built up by repeated deposition and annealing steps. We measured 88 GPa hardness, 1100 GPa Young's modulus, and 0.1 friction coefficient (under high load). Significantly, these results are all well within the range reported for crystalline diamond. In fact, this material, if considered separate from crystalline diamond, is the 2nd hardest material known to man. Stress-free a-tC also has important advantages over thin film diamond; namely, it is smooth, processed at lower temperature, and can be grown on a much broader range of substrates. This breakthrough will enable a host of applications that we are actively pursuing in MEMs, sensors, LIGA, etc.
A Comparative Approach to Hand Force Estimation using Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Farid Mobasser
2012-01-01
Full Text Available In many applications that include direct human involvement such as control of prosthetic arms, athletic training, and studying muscle physiology, hand force is needed for control, modeling and monitoring purposes. The use of inexpensive and easily portable active electromyography (EMG electrodes and position sensors would be advantageous in these applications compared to the use of force sensors which are often very expensive and require bulky frames. Among non-model-based estimation methods, Multilayer Perceptron Artificial Neural Networks (MLPANN has widely been used to estimate muscle force or joint torque from different anatomical features in humans or animals. This paper investigates the use of Radial Basis Function (RBF ANN and MLPANN for force estimation and experimentally compares the performance of the two methodologies for the same human anatomy, ie, hand force estimation, under an ensemble of operational conditions. In this unified study, the EMG signal readings from upper-arm muscles involved in elbow joint movement and elbow angular position and velocity are utilized as inputs to the ANNs. In addition, the use of the elbow angular acceleration signal as an input for the ANNs is also investigated.
Fernández Caballero, Juan Carlos; Martínez, Francisco José; Hervás, César; Gutiérrez, Pedro Antonio
2010-05-01
This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class.
Directory of Open Access Journals (Sweden)
Bertin Dominique
2016-01-01
Full Text Available The Liane River is a small costal river, famous for its floods, which can affect the city of Boulogne-sur-Mer. Due to the complexity of land cover and hydrologic processes, a black-box non-linear modelling was chosen using neural networks. The multilayer perceptron model, known for its property of universal approximation is thus chosen. Four models were designed, each one for one forecasting horizon using rainfall forecasts: 24h, 12h, 6h, 3h. The desired output of the model is original: it represents the maximal value of the water level respectively 24h, 12h, 6h, 3h ahead. Working with best forecasts of rain (the observed ones during the event in the past, on the major flood of the database in test set, the model provides excellent forecasts. Nash criteria calculated for the four lead times are 0.98 (3h, 0.97 (6h, 0.91 (12h, 0.89 (24h. Designed models were thus estimated as efficient enough to be implemented in a specific tool devoted to real time operational use. The software tool is described hereafter: designed in Java, it presents a friendly interface allowing applying various scenarios of future rainfalls, and a graphical visualization of the predicted maximum water levels and their associated real time observed values.
Lallahem, S.; Hani, A.
2017-02-01
Water sustainability in the lower Seybouse River basin, eastern Algeria, must take into account the importance of water quantity and quality integration. So, there is a need for a better knowledge and understanding of the water quality determinants of groundwater abstraction to meet the municipal and agricultural uses. In this paper, the artificial neural network (ANN) models were used to model and predict the relationship between groundwater abstraction and water quality determinants in the lower Seybouse River basin. The study area chosen is the lower Seybouse River basin and real data were collected from forty five wells for reference year 2006. Results indicate that the feed-forward multilayer perceptron models with back-propagation are useful tools to define and prioritize the important water quality parameters of groundwater abstraction and use. The model evaluation shows that the correlation coefficients are more than 95% for training, verification and testing data. The model aims to link the water quantity and quality with the objective to strengthen the Integrated Water Resources Management approach. It assists water planners and managers to better assess the water quality parameters and progress towards the provision of appropriate quantities of water of suitable quality.
Directory of Open Access Journals (Sweden)
Saleh Shahinfar
2012-01-01
Full Text Available Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.
Computer vision-based method for classification of wheat grains using artificial neural network.
Sabanci, Kadir; Kayabasi, Ahmet; Toktas, Abdurrahim
2017-06-01
A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains. Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10 -6 by the simplified ANN model. This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.
Medarević, Djordje P; Kleinebudde, Peter; Djuriš, Jelena; Djurić, Zorica; Ibrić, Svetlana
2016-01-01
This study for the first time demonstrates combined application of mixture experimental design and artificial neural networks (ANNs) in the solid dispersions (SDs) development. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs were prepared by solvent casting method to improve carbamazepine dissolution rate. The influence of the composition of prepared SDs on carbamazepine dissolution rate was evaluated using d-optimal mixture experimental design and multilayer perceptron ANNs. Physicochemical characterization proved the presence of the most stable carbamazepine polymorph III within the SD matrix. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs significantly improved carbamazepine dissolution rate compared to pure drug. Models developed by ANNs and mixture experimental design well described the relationship between proportions of SD components and percentage of carbamazepine released after 10 (Q10) and 20 (Q20) min, wherein ANN model exhibit better predictability on test data set. Proportions of carbamazepine and poloxamer 188 exhibited the highest influence on carbamazepine release rate. The highest carbamazepine release rate was observed for SDs with the lowest proportions of carbamazepine and the highest proportions of poloxamer 188. ANNs and mixture experimental design can be used as powerful data modeling tools in the systematic development of SDs. Taking into account advantages and disadvantages of both techniques, their combined application should be encouraged.
Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A.
2012-01-01
Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production. PMID:22991575
Directory of Open Access Journals (Sweden)
Onur Satir
2016-09-01
Full Text Available Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN method was used to map forest fire probability in Upper Seyhan Basin (USB in Turkey. Multi-layer perceptron (MLP approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R = −0.43, tree cover (R = 0.93 and temperature (R = 0.42 were strongly correlated with forest fire probability in the USB region.
Hramov, Alexander E.; Maksimenko, Vladimir A.; Pchelintseva, Svetlana V.; Runnova, Anastasiya E.; Grubov, Vadim V.; Musatov, Vyacheslav Yu.; Zhuravlev, Maksim O.; Koronovskii, Alexey A.; Pisarchik, Alexander N.
2017-01-01
In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces. PMID:29255403
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GEMAN, O.
2014-02-01
Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.
A Comparative Approach to Hand Force Estimation using Artificial Neural Networks.
Mobasser, Farid; Hashtrudi-Zaad, Keyvan
2012-01-01
In many applications that include direct human involvement such as control of prosthetic arms, athletic training, and studying muscle physiology, hand force is needed for control, modeling and monitoring purposes. The use of inexpensive and easily portable active electromyography (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors which are often very expensive and require bulky frames. Among non-model-based estimation methods, Multilayer Perceptron Artificial Neural Networks (MLPANN) has widely been used to estimate muscle force or joint torque from different anatomical features in humans or animals. This paper investigates the use of Radial Basis Function (RBF) ANN and MLPANN for force estimation and experimentally compares the performance of the two methodologies for the same human anatomy, ie, hand force estimation, under an ensemble of operational conditions. In this unified study, the EMG signal readings from upper-arm muscles involved in elbow joint movement and elbow angular position and velocity are utilized as inputs to the ANNs. In addition, the use of the elbow angular acceleration signal as an input for the ANNs is also investigated.
Dariani, Sharareh; Keshavarz, Mansoor; Parviz, Mohsen; Raoufy, Mohammad Reza; Gharibzadeh, Shahriar
2007-01-01
The aim of this study is to design an artificial neural network (ANN) to model force-velocity relation in skeletal muscle isotonic contraction. We obtained the data set, including physiological and morphometric parameters, by myography and morphometric measurements on frog gastrocnemius muscle. Then, we designed a multilayer perceptron ANN, the inputs of which are muscle volume, muscle optimum length, tendon length, preload, and afterload. The output of the ANN is contraction velocity. The experimental data were divided randomly into two parts. The first part was used to train the ANN. In order to validate the model, the second part of experimental data, which was not used in training, was employed to the ANN and then, its output was compared with Hill model and the experimental data. The behavior of ANN in high forces was more similar to experimental data, but in low forces the Hill model had better results. Furthermore, extrapolation of ANN performance showed that our model is more or less able to simulate eccentric contraction. Our results indicate that ANNs represent a powerful tool to capture some essential features of muscle isotonic contraction.
Modeling of Soft sensor based on Artificial Neural Network for Galactic Cosmic Rays Application
Suparta, W.; Putro, W. S.
2014-10-01
For successful designing of space radiation Galactic Cosmic Rays (GCRs) model, we develop a soft sensor based on the Artificial Neural Network (ANN) model. At the first step, the soft sensor based ANN was constructed as an alternative to model space radiation environment. The structure of ANN in this model is using Multilayer Perceptron (MLP) and Levenberg Marquardt algorithms with 3 inputs and 2 outputs. In the input variable, we use 12 years data (Corr, Uncorr and Press) of GCR particles obtained from Neutron Monitor of Bartol University (Fort Smith area) and the target output is (Corr and Press) from the same source but for Inuvik area in the Polar Regions. In the validation step, we obtained the Root Mean Square Error (RMSE) value of Corr 3.8670e-004 and Press 1.3414e-004 and Variance Accounted For (VAF) of Corr 99.9839 % and Press 99.9831% during the training section. After all the results obtained, then we applied into a Matlab GUI simulation (soft sensor simulation). This simulation will display the estimation of output value from input (Corr and Press). Testing results showed an error of 0.133% and 0.014% for Corr and Press, respectively.
Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A
2012-01-01
Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.
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J. Szajnar
2010-01-01
Full Text Available In paper is presented the possibility of making of multi-layers cast steel castings in result of connection of casting and welding coating technologies. First layer was composite surface layer on the basis of Fe-Cr-C alloy, which was put directly in founding process of cast carbon steel 200–450 with use of preparation of mould cavity method. Second layer were padding welds, which were put with use of TIG – Tungsten Inert Gas surfacing by welding technology with filler on Ni matrix, Ni and Co matrix with wolfram carbides WC and on the basis on Fe-Cr-C alloy, which has the same chemical composition with alloy, which was used for making of composite surface layer. Usability for industrial applications of surface layers of castings were estimated by criterion of hardness and abrasive wear resistance of type metal-mineral.
SIW based multilayer transition and power divider in LTCC technology
Abuzaid, Hattan
2013-06-01
A multilayer transition and balanced power divider are presented for millimeter-wave system-on-package (SoP). These two components operate at Ka-band and exploit the substrate integrate waveguide (SIW) technology with its shielding characteristics and the Low-temperature co-fired ceramics (LTCC) technology for its high density integration. A coupling slot has been used to perform vertical integration, which can be easily optimized through its length. The measured input return loss within the bandwidth of interest (32 GHz-38 GHz) is less than -15 dB and -18 dB for the multilayer transition and the power divider, respectively. The lateral dimensions of a multilayer system, such as a feed network of an array, can be greatly reduced by employing these 3D slot-coupled components. © 2013 IEEE.
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Luiz Henry Monken e Silva
2005-01-01
Full Text Available Neste artigo a habilidade das redes neurais perceptron multicamada eminterpolar foi utilizada para analisar duas classes de problemas de contorno. A primeira classe é formada por equações diferenciais em que a solução pode apresentar gradientes elevados e a segunda classe é formada de equações diferenciais definidas em domínios arbitrários. As metodologias propostas por Lagaris et al. (1998 foram estendidas para casos de equações diferenciais sujeitas às condições de Cauchy e condições de contorno mistas. Os resultados fornecidos pelo método da rede neural se apresentam precisos quando comparados com os resultados analíticos ou por métodos numéricos de resolução deequações diferenciais. A precisão alcançada nos resultados e a facilidade no manuseio do método para resolver estes problemas de contorno encorajaram a continuidade da pesquisa, particularmente no tocante à convergência e estabilidade numérica.In this paper, the ability of the multilayer perceptron neural network (MLP in interpolation was used to analyze two classes of boundary value problems. The first class is formed by differential equations, with solutions which can have high gradients and the second are partial differential equations, defined on arbitrary shaped domain. Also, the methodologies proposed by Lagaris et al. (1998 were enlarged for differential equations subjected to Cauchy and mix boundary conditions type. The results of the artificial neural network method are very precise when comparison to the analytical ones or those of classical numerical methods to solve differential equations. The precision achieved in the results and the ability to handle the method, to solve those boundary value problems, were encouraging to keep the research, particularly on an important direction, concerning convergence and numerical stability.
Multilayered Magnetic Gelatin Membrane Scaffolds
Samal, Sangram K.; Goranov, Vitaly; Dash, Mamoni; Russo, Alessandro; Shelyakova, Tatiana; Graziosi, Patrizio; Lungaro, Lisa; Riminucci, Alberto; Uhlarz, Marc; Bañobre-López, Manuel; Rivas, Jose; Herrmannsdörfer, Thomas; Rajadas, Jayakumar; De Smedt, Stefaan; Braeckmans, Kevin; Kaplan, David L.; Dediu, V. Alek
2016-01-01
A versatile approach for the design and fabrication of multilayer magnetic scaffolds with tunable magnetic gradients is described. Multilayer magnetic gelatin membrane scaffolds with intrinsic magnetic gradients were designed to encapsulate magnetized bioagents under an externally applied magnetic field for use in magnetic-field-assisted tissue engineering. The temperature of the individual membranes increased up to 43.7 °C under an applied oscillating magnetic field for 70 s by magnetic hyperthermia, enabling the possibility of inducing a thermal gradient inside the final 3D multilayer magnetic scaffolds. On the basis of finite element method simulations, magnetic gelatin membranes with different concentrations of magnetic nanoparticles were assembled into 3D multilayered scaffolds. A magnetic-gradient-controlled distribution of magnetically labeled stem cells was demonstrated in vitro. This magnetic biomaterial–magnetic cell strategy can be expanded to a number of different magnetic biomaterials for various tissue engineering applications. PMID:26451743
Gholipour Peyvandi, R.; Islami Rad, S. Z.
2017-12-01
The determination of the volume fraction percentage of the different phases flowing in vessels using transmission gamma rays is a conventional method in petroleum and oil industries. In some cases, with access only to the one side of the vessels, attention was drawn toward backscattered gamma rays as a desirable choice. In this research, the volume fraction percentage was measured precisely in water-gasoil-air three-phase flows by using the backscatter gamma ray technique andthe multilayer perceptron (MLP) neural network. The volume fraction determination in three-phase flows requires two gamma radioactive sources or a dual-energy source (with different energies) while in this study, we used just a 137Cs source (with the single energy) and a NaI detector to analyze backscattered gamma rays. The experimental set-up provides the required data for training and testing the network. Using the presented method, the volume fraction was predicted with a mean relative error percentage less than 6.47%. Also, the root mean square error was calculated as 1.60. The presented set-up is applicable in some industries with limited access. Also, using this technique, the cost, radiation safety and shielding requirements are minimized toward the other proposed methods.
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Jamshid Behin
2016-12-01
Full Text Available In this work, response surface methodology (RSM and artificial neural network (ANN were used to predict the decolorization efficiency of Reactive Red 33 (RR 33 by applying the O3/UV process in a bubble column reactor. The effects of four independent variables including time (20-60 min, superficial gas velocity (0.06-0.18 cm/s, initial concentration of dye (50-150 ppm, and pH (3-11 were investigated using a 3-level 4-factor central composite experimental design. This design was utilized to train a feed-forward multilayered perceptron artificial neural network with a back-propagation algorithm. A comparison between the models’ results and experimental data gave high correlation coefficients and showed that the two models were able to predict Reactive Red 33 removal by employing the O3/UV process. Considering the results of the yield of dye removal and the response surface-generated model, the optimum conditions for dye removal were found to be a retention time of 59.87 min, a superficial gas velocity of 0.18 cm/s, an initial concentration of 96.33 ppm, and a pH of 7.99.
Optical transmittance of multilayer graphene
Zhu, Shou-En; Yuan, Shengjun; Janssen, G.C.A.M.
2014-01-01
We study the optical transmittance of multilayer graphene films up to 65 layers thick. By combing large-scale tight-binding simulation and optical measurement on CVD multilayer graphene, the optical transmission through graphene films in the visible region is found to be solely determined by the number of graphene layers. We argue that the optical transmittance measurement is more reliable in the determination of the number of layers than the commonly used Raman Spectroscopy. Moreover, optica...
Adiabatic superconducting cells for ultra-low-power artificial neural networks
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Andrey E. Schegolev
2016-10-01
Full Text Available We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.
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Juan David Velásquez Henao
2007-12-01
Full Text Available En este artículo, se modela el precio promedio mensual del café colombiano en la Bolsa de Nueva York, usando varios modelos alternativos. El modelo final seleccionado está compuesto por una componente lineal autorregresiva más una red neuronal artificial tipo perceptron multicapa con dos neuronas en la capa oculta, que permite representar la dinámica que sigue el valor esperado de la serie de precios; mientras que la dinámica de los residuales es especificada usando un proceso heterocedástico condicional autoregresivo de primer orden. Los residuales normalizados del modelo son incorrelacionados y homocedásticos, y siguen aproximadamente una distribución normal. Los resultados indican que el precio actual depende de los precios ocurridos en los últimos cuatro meses.In this paper, the monthly average price of the Colombian coffee in the New York Stock Exchange, is modelling by means of several alternative models. The preferred model is composed by a lineal autoregressive component plus a multilayer perceptron neural network with two neurons in the hidden layer, that allow us to representing the dynamic following by the expected value of the price time series; while, the dynamic of the residuals is specified by an autoregressive conditional heterocedastic model of first order. The normalized residuals of the preferred model are uncorrelated, homocedastic and are distributed following a normal distribution. The results indicate that the current price depend of the prices in the previous four months.
Amozegar, M; Khorasani, K
2016-04-01
In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies. Copyright © 2016 Elsevier Ltd. All rights reserved.
METHODS OF TEXT INFORMATION CLASSIFICATION ON THE BASIS OF ARTIFICIAL NEURAL AND SEMANTIC NETWORKS
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L. V. Serebryanaya
2016-01-01
Full Text Available The article covers the use of perseptron, Hopfild artificial neural network and semantic network for classification of text information. Network training algorithms are studied. An algorithm of inverse mistake spreading for perceptron network and convergence algorithm for Hopfild network are implemented. On the basis of the offered models and algorithms automatic text classification software is developed and its operation results are evaluated.
Pan, Sha-sha; Huang, Fu-rong; Xiao, Chi; Xian, Rui-yi; Ma, Zhi-guo
2015-10-01
To explore rapid reliable methods for detection of Epicarpium citri grandis (ECG), the experiment using Fourier Transform Attenuated Total Reflection Infrared Spectroscopy (FTIR/ATR) and Fluorescence Spectrum Imaging Technology combined with Multilayer Perceptron (MLP) Neural Network pattern recognition, for the identification of ECG, and the two methods are compared. Infrared spectra and fluorescence spectral images of 118 samples, 81 ECG and 37 other kinds of ECG, are collected. According to the differences in tspectrum, the spectra data in the 550-1 800 cm(-1) wavenumber range and 400-720 nm wavelength are regarded as the study objects of discriminant analysis. Then principal component analysis (PCA) is applied to reduce the dimension of spectroscopic data of ECG and MLP Neural Network is used in combination to classify them. During the experiment were compared the effects of different methods of data preprocessing on the model: multiplicative scatter correction (MSC), standard normal variable correction (SNV), first-order derivative(FD), second-order derivative(SD) and Savitzky-Golay (SG). The results showed that: after the infrared spectra data via the Savitzky-Golay (SG) pretreatment through the MLP Neural Network with the hidden layer function as sigmoid, we can get the best discrimination of ECG, the correct percent of training set and testing set are both 100%. Using fluorescence spectral imaging technology, corrected by the multiple scattering (MSC) results in the pretreatment is the most ideal. After data preprocessing, the three layers of the MLP Neural Network of the hidden layer function as sigmoid function can get 100% correct percent of training set and 96.7% correct percent of testing set. It was shown that the FTIR/ATR and fluorescent spectral imaging technology combined with MLP Neural Network can be used for the identification study of ECG and has the advantages of rapid, reliable effect.
Multilayer graphene rubber nanocomposites
Schartel, Bernhard; Frasca, Daniele; Schulze, Dietmar; Wachtendorf, Volker; Krafft, Bernd; Morys, Michael; Böhning, Martin; Rybak, Thomas
2016-05-01
Multilayer Graphene (MLG), a nanoparticle with a specific surface of BET = 250 m2/g and thus made of only approximately 10 graphene sheets, is proposed as a nanofiller for rubbers. When homogenously dispersed, it works at low loadings enabling the replacement of carbon black (CB), increase in efficiency, or reduction in filler concentration. Actually the appropriate preparation yielded nanocomposites in which just 3 phr are sufficient to significantly improve the rheological, curing and mechanical properties of different rubbers, as shown for Chlorine-Isobutylene-Isoprene Rubber (CIIR), Nitrile-Butadiene Rubber (NBR), Natural Rubber (NR), and Styrene-Butadiene Rubber (SBR). A mere 3 phr of MLG tripled the Young's modulus of CIIR, an effect equivalent to 20 phr of carbon black. Similar equivalents are observed for MLG/CB mixtures. MLG reduces gas permeability, increases thermal and electrical conductivities, and retards fire behavior. The later shown by the reduction in heat release rate in the cone calorimeter. The higher the nanofiller concentration is (3 phr, 5 phr, and 10 phr was investigated), the greater the improvement in the properties of the nanocomposites. Moreover, the MLG nanocomposites improve stability of mechanical properties against weathering. An increase in UV-absorption as well as a pronounced radical scavenging are proposed and were proved experimentally. To sum up, MLG is interesting as a multifunctional nanofiller and seems to be quite ready for rubber development.
Multilayer optical calculations
Byrnes, Steven J
2016-01-01
When light hits a multilayer planar stack, it is reflected, refracted, and absorbed in a way that can be derived from the Fresnel equations. The analysis is treated in many textbooks, and implemented in many software programs, but certain aspects of it are difficult to find explicitly and consistently worked out in the literature. Here, we derive the formulas underlying the transfer-matrix method of calculating the optical properties of these stacks, including oblique-angle incidence, absorption-vs-position profiles, and ellipsometry parameters. We discuss and explain some strange consequences of the formulas in the situation where the incident and/or final (semi-infinite) medium are absorptive, such as calculating $T>1$ in the absence of gain. We also discuss some implementation details like complex-plane branch cuts. Finally, we derive modified formulas for including one or more "incoherent" layers, i.e. very thick layers in which interference can be neglected. This document was written in conjunction with ...
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Zhe Dong
2014-02-01
Full Text Available Small modular reactors (SMRs could be beneficial in providing electricity power safely and also be viable for applications such as seawater desalination and heat production. Due to its inherent safety features, the modular high temperature gas-cooled reactor (MHTGR has been seen as one of the best candidates for building SMR-based nuclear power plants. Since the MHTGR dynamics display high nonlinearity and parameter uncertainty, it is necessary to develop a nonlinear adaptive power-level control law which is not only beneficial to the safe, stable, efficient and autonomous operation of the MHTGR, but also easy to implement practically. In this paper, based on the concept of shifted-ectropy and the physically-based control design approach, it is proved theoretically that the simple proportional-differential (PD output-feedback power-level control can provide asymptotic closed-loop stability. Then, based on the strong approximation capability of the multi-layer perceptron (MLP artificial neural network (ANN, a compensator is established to suppress the negative influence caused by system parameter uncertainty. It is also proved that the MLP-compensated PD power-level control law constituted by an experientially-tuned PD regulator and this MLP-based compensator can guarantee bounded closed-loop stability. Numerical simulation results not only verify the theoretical results, but also illustrate the high performance of this MLP-compensated PD power-level controller in suppressing the oscillation of process variables caused by system parameter uncertainty.
Anderson, R. B.; Morris, Richard V.; Clegg, S. M.; Humphries, S. D.; Wiens, R. C.; Bell, J. F., III; Mertzman, S. A.
2010-01-01
The ChemCam instrument [1] on the Mars Science Laboratory (MSL) rover will be used to obtain the chemical composition of surface targets within 7 m of the rover using Laser Induced Breakdown Spectroscopy (LIBS). ChemCam analyzes atomic emission spectra (240-800 nm) from a plasma created by a pulsed Nd:KGW 1067 nm laser. The LIBS spectra can be used in a semiquantitative way to rapidly classify targets (e.g., basalt, andesite, carbonate, sulfate, etc.) and in a quantitative way to estimate their major and minor element chemical compositions. Quantitative chemical analysis from LIBS spectra is complicated by a number of factors, including chemical matrix effects [2]. Recent work has shown promising results using multivariate techniques such as partial least squares (PLS) regression and artificial neural networks (ANN) to predict elemental abundances in samples [e.g. 2-6]. To develop, refine, and evaluate analysis schemes for LIBS spectra of geologic materials, we collected spectra of a diverse set of well-characterized natural geologic samples and are comparing the predictive abilities of PLS, cascade correlation ANN (CC-ANN) and multilayer perceptron ANN (MLP-ANN) analysis procedures.
Ennouri, Karim; Ben Ayed, Rayda; Triki, Mohamed Ali; Ottaviani, Ennio; Mazzarello, Maura; Hertelli, Fathi; Zouari, Nabil
2017-07-01
The aim of the present work was to develop a model that supplies accurate predictions of the yields of delta-endotoxins and proteases produced by B. thuringiensis var. kurstaki HD-1. Using available medium ingredients as variables, a mathematical method, based on Plackett-Burman design (PB), was employed to analyze and compare data generated by the Bootstrap method and processed by multiple linear regressions (MLR) and artificial neural networks (ANN) including multilayer perceptron (MLP) and radial basis function (RBF) models. The predictive ability of these models was evaluated by comparison of output data through the determination of coefficient (R 2 ) and mean square error (MSE) values. The results demonstrate that the prediction of the yields of delta-endotoxin and protease was more accurate by ANN technique (87 and 89% for delta-endotoxin and protease determination coefficients, respectively) when compared with MLR method (73.1 and 77.2% for delta-endotoxin and protease determination coefficients, respectively), suggesting that the proposed ANNs, especially MLP, is a suitable new approach for determining yields of bacterial products that allow us to make more appropriate predictions in a shorter time and with less engineering effort.
Akdagli, Ali; Toktas, Abdurrahim; Kayabasi, Ahmet; Develi, Ibrahim
2013-09-01
An application of artificial neural network (ANN) based on multilayer perceptrons (MLP) to compute the resonant frequency of E-shaped compact microstrip antennas (ECMAs) is presented in this paper. The resonant frequencies of 144 ECMAs with different dimensions and electrical parameters were firstly determined by using IE3D(tm) software based on the method of moments (MoM), then the ANN model for computing the resonant frequency was built by considering the simulation data. The parameters and respective resonant frequency values of 130 simulated ECMAs were employed for training and the remaining 14 ECMAs were used for testing the model. The computed resonant frequencies for training and testing by ANN were obtained with the average percentage errors (APE) of 0.257% and 0.523%, respectively. The validity and accuracy of the present approach was verified on the measurement results of an ECMA fabricated in this study. Furthermore, the effects of the slots loading method over the resonant frequency were investigated to explain the relationship between the slots and resonant frequency.
DeLong, Caroline M; Heberle, Amanda L; Wisniewski, Matthew G; Mercado, Eduardo
2014-05-01
Object constancy, the ability to recognize objects despite changes in orientation, has not been well studied in the auditory modality. Dolphins use echolocation for object recognition, and objects ensonified by dolphins produce echoes that can vary significantly as a function of orientation. In this experiment, human listeners had to classify echoes from objects varying in material, shape, and size that were ensonified with dolphin signals. Participants were trained to discriminate among the objects using an 18-echo stimulus from a 10° range of aspect angles, then tested with novel aspect angles across a 60° range. Participants were typically successful recognizing the objects at all angles (M = 78 %). Artificial neural networks were trained and tested with the same stimuli with the purpose of identifying acoustic cues that enable object recognition. A multilayer perceptron performed similarly to the humans and revealed that recognition was enabled by both the amplitude and frequency of echoes, as well as the temporal dynamics of these features over the course of echo trains. These results provide insight into representational processes underlying echoic recognition in dolphins and suggest that object constancy perceived through the auditory modality is likely to parallel what has been found in the visual domain in studies with both humans and animals.
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Elham Azarpazhooh
2017-01-01
Full Text Available Saffron, as the most expensive agricultural and pharmaceutical product of the world, has a special value among plants. Since the Saffron harvesting period is short, its storage for later processing requires understanding the most effective factors affecting the quality of saffron and its deterioration. Therefore the effects of reposition thickness, storage temperature and storage time of saffron flowers on its chemical parameters including crocin, safranal and picrocrocin values of saffron stigma and its microbial quality indicators including total count, coliform and mold contamination were modelled. This was done using multi-layer perceptron artificial neural network (ANN and its structure and the learning parameters were optimized using genetic algorithm technique. The optimized MLP neural networkwas capable to predict the saffron quality characteristics during storage with coefficient of determinations higher than %94 and low error values (RMSE lower than 3.5 for all responses. The ANN model showed that reposition thickness has the lowest impact on chemical and microbial parameters deterioration while increasing storage temperature and time drastically increased loss of quality although the effect of storage time is lower than that of storage temperature. Overall, keeping fresh saffron flowers at a low temperature near zero degrees centigrade is necessary for maximum retention of valuable chemical compounds and minimum microbial contamination development during saffron flower storage for further processing.
Formation of multiple networks
DEFF Research Database (Denmark)
Magnani, Matteo; Rossi, Luca
2013-01-01
While most research in Social Network Analysis has focused on single networks, the availability of complex on-line data about individuals and their mutual heterogenous connections has recently determined a renewed interest in multi-layer network analysis. To the best of our knowledge, in this paper...... we introduce the first network formation model for multiple networks. Network formation models are among the most popular tools in traditional network studies, because of both their practical and theoretical impact. However, existing models are not sufficient to describe the generation of multiple...... networks. Our model, motivated by an empirical analysis of real multi-layered network data, is a conservative extension of single-network models and emphasizes the additional level of complexity that we experience when we move from a single- to a more complete and realistic multi-network context....
Investigation of efficient features for image recognition by neural networks.
Goltsev, Alexander; Gritsenko, Vladimir
2012-04-01
In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. Copyright © 2011 Elsevier Ltd. All rights reserved.
Issues in the use of neural networks in information retrieval
Iatan, Iuliana F
2017-01-01
This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a new NN-based method for learning image similarity and describes how to use fuzzy Gaussian neural networks to predict personality. It introduces the fuzzy Clifford Gaussian network, and two concurrent neural models: (1) concurrent fuzzy nonlinear perceptron modules, and (2) concurrent fuzzy Gaussian neural network modules. Furthermore, it explains the design of a new model of fuzzy nonlinear perceptron based on alpha level sets and describes a recurrent fuzzy neural network model with a learning algorithm based on the improved particle swarm optimization method.
Sectioning of multilayers to make a multilayer Laue lens
Kang, Hyon Chol; Stephenson, G. Brian; Liu, Chian; Conley, Ray; Khachatryan, Ruben; Wieczorek, Michael; Macrander, Albert T.; Yan, Hanfei; Maser, Jörg; Hiller, Jon; Koritala, Rachel
2007-04-01
We report a process to fabricate multilayer Laue lenses (MLL's) by sectioning and thinning multilayer films. This method can produce a linear zone plate structure with a very large ratio of zone depth to width (e.g., >1000), orders of magnitude larger than can be attained with photolithography. Consequently, MLL's are advantageous for efficient nanofocusing of hard x rays. MLL structures prepared by the technique reported here have been tested at an x-ray energy of 19.5keV, and a diffraction-limited performance was observed. The present article reports the fabrication techniques that were used to make the MLL's.
Multilayer adsorption on fractal surfaces.
Vajda, Péter; Felinger, Attila
2014-01-10
Multilayer adsorption is often observed in liquid chromatography. The most frequently employed model for multilayer adsorption is the BET isotherm equation. In this study we introduce an interpretation of multilayer adsorption measured on liquid chromatographic stationary phases based on the fractal theory. The fractal BET isotherm model was successfully used to determine the apparent fractal dimension of the adsorbent surface. The nonlinear fitting of the fractal BET equation gives us the estimation of the adsorption equilibrium constants and the monolayer saturation capacity of the adsorbent as well. In our experiments, aniline and proline were used as test molecules on reversed phase and normal phase columns, respectively. Our results suggest an apparent fractal dimension 2.88-2.99 in the case of reversed phase adsorbents, in the contrast with a bare silica column with a fractal dimension of 2.54. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.
On Seismic Ground Roll Filtering Using the Wavelet Transform and Neural Network
Benaissa, Zahia; Benaissa, Abdelkader; Ouadfeul, Sid-Ali; Aliouane, Leila; Boudella, Amar
2013-04-01
Here, we present an adapted filtering technique for the non-stationary signals. It is based on the wavelet transform and its rebuilding formula. This technique is used generally to detect and extract locally in the time-scale field particular events from seismic data. We show the efficiency of this technique to filter the ground roll from reflection seismic vibroseis recording (shot gather). The results for two different filtering processes are presented, one of these results is based on the annulment of the transform coefficients in the selected zone relating to the ground roll, and the other one is based on their attenuation (roll-off). Obtained results shows the efficiency of the first process especially when the wavelet transform is calculated only on the noisy zone and when the ground roll is made up of two or more pseudo-Rayleigh waves, in this case iterations are mandatory to improve the signal to noise ratio using the second process. The current work shows also the use of the artificial neural network on the prediction of the mute parameters in the F-K domain to be used on the Ground Roll attenuation. The proposed idea is very robust and useful in case of 3D seismic data. A set of 3D seismic Inlines are used for the training of the Multilayer Perceptron (MLP) neural network machine. Application to real data shows clearly the robustness of the proposed technique. Keywords: Filtering - Ground roll - Wavelet transform - Seismic - Reflection - Signal to noise ratio - Artificial neuronal network -3D-MLP- Training.
Monthly evaporation forecasting using artificial neural networks and support vector machines
Tezel, Gulay; Buyukyildiz, Meral
2016-04-01
Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and ɛ-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, ɛ-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). According to the performance criteria, the ANN algorithms and ɛ-SVR had similar results. The ANNs and ɛ-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R 2 = 0.905.
Applying an artificial neural network to predict osteoporosis in the elderly.
Chiu, Jainn-Shiun; Li, Yu-Chuan; Yu, Fu-Chiu; Wang, Yuh-Feng
2006-01-01
Osteoporosis is an essential index of health and economics in every country. Recognizing asymptomatic elderly population with high risks of osteoporosis remains a difficult challenge. For this purpose, we developed and validated an artificial neural network (ANN) to identify the osteoporotic subjects in the elderly. The study population consisted of 1403 elderly adults (mean age 63.50 +/- 0.24 years ranged from 50 to 91 years old, 157 male and 1246 female) randomly selected into 3 sets, 703 participants in training set, 350 participants in selection set, and the remaining 350 participants in test set. The input variables included demographic characteristics, anthropometric measurements, and clinical data. The outcome variable was dichotomous, either non-osteoporotic (T-score of greater than -2.5) or osteoporotic (T-score of -2.5 or less) groups classified by the measurement from dual energy X-ray absorptiometry. ANN was constructed with data from training and selection sets and validated in test set whose outcome variable was unknown to the network. The performance of ANN was evaluated by discrimination and calibration simultaneously. After training processes, the final best ANN was a multilayer perceptron network which determined seven input variables (gender, age, weight, height, body mass index, postmenopausal status, and coffee consumption) as significant features. The discriminatory power of ANN for test set was excellent (area under receiver operating characteristics curve = 0.82 +/- 0.03). ANN also had statistically good fit represented by statistically insignificant Hosmer-Lemeshow statistic (p = 0.24). These results suggested that our final ANN concurrently had good discriminatory power and good-fit calibration. ANN can be used as a promising tool for the elderly to stratify high risk subjects into osteoporotic group.
Scanning Tunneling Spectroscopy of Proximity Superconductivity in Epitaxial Multilayer Graphene
Natterer, Fabian D.; Ha, Jeonghoon; Baek, Hongwoo; Zhang, Duming; Cullen, William; Zhitenev, Nikolai B.; Kuk, Young; Stroscio, Joseph A.
2016-01-01
We report on spatial measurements of the superconducting proximity effect in epitaxial graphene induced by a graphene-superconductor interface. Superconducting aluminum films were grown on epitaxial multilayer graphene on SiC. The aluminum films were discontinuous with networks of trenches in the film morphology reaching down to exposed graphene terraces. Scanning tunneling spectra measured on the graphene terraces show a clear decay of the superconducting energy gap with increasing separatio...
Optical transmittance of multilayer graphene
Zhu, Shou-En; Yuan, Shengjun; Janssen, G. C. A. M.
2014-10-01
We study the optical transmittance of multilayer graphene films up to 65 layers thick. By combing large-scale tight-binding simulation and optical measurement on CVD multilayer graphene, the optical transmission through graphene films in the visible region is found to be solely determined by the number of graphene layers. We argue that the optical transmittance measurement is more reliable in the determination of the number of layers than the commonly used the Raman spectroscopy. Moreover, the optical transmittance measurement can be applied also to other 2D materials with weak van der Waals interlayer interaction.
Thermally induced delamination of multilayers
DEFF Research Database (Denmark)
Sørensen, Bent F.; Sarraute, S.; Jørgensen, O.
1998-01-01
Steady-state delamination of multilayered structures, caused by stresses arising during processing due to thermal expansion mismatch, is analyzed by a fracture mechanics model based on laminate theory. It is found that inserting just a few interlayers with intermediate thermal expansion coefficie......Steady-state delamination of multilayered structures, caused by stresses arising during processing due to thermal expansion mismatch, is analyzed by a fracture mechanics model based on laminate theory. It is found that inserting just a few interlayers with intermediate thermal expansion...
Chen, Chi-Kan
2017-07-26
The identification of genetic regulatory networks (GRNs) provides insights into complex cellular processes. A class of recurrent neural networks (RNNs) captures the dynamics of GRN. Algorithms combining the RNN and machine learning schemes were proposed to reconstruct small-scale GRNs using gene expression time series. We present new GRN reconstruction methods with neural networks. The RNN is extended to a class of recurrent multilayer perceptrons (RMLPs) with latent nodes. Our methods contain two steps: the edge rank assignment step and the network construction step. The former assigns ranks to all possible edges by a recursive procedure based on the estimated weights of wires of RNN/RMLP (RE RNN /RE RMLP ), and the latter constructs a network consisting of top-ranked edges under which the optimized RNN simulates the gene expression time series. The particle swarm optimization (PSO) is applied to optimize the parameters of RNNs and RMLPs in a two-step algorithm. The proposed RE RNN -RNN and RE RMLP -RNN algorithms are tested on synthetic and experimental gene expression time series of small GRNs of about 10 genes. The experimental time series are from the studies of yeast cell cycle regulated genes and E. coli DNA repair genes. The unstable estimation of RNN using experimental time series having limited data points can lead to fairly arbitrary predicted GRNs. Our methods incorporate RNN and RMLP into a two-step structure learning procedure. Results show that the RE RMLP using the RMLP with a suitable number of latent nodes to reduce the parameter dimension often result in more accurate edge ranks than the RE RNN using the regularized RNN on short simulated time series. Combining by a weighted majority voting rule the networks derived by the RE RMLP -RNN using different numbers of latent nodes in step one to infer the GRN, the method performs consistently and outperforms published algorithms for GRN reconstruction on most benchmark time series. The framework of two
A multi-layer MRI description of Parkinson's disease
La Rocca, M.; Amoroso, N.; Lella, E.; Bellotti, R.; Tangaro, S.
2017-09-01
Magnetic resonance imaging (MRI) along with complex network is currently one of the most widely adopted techniques for detection of structural changes in neurological diseases, such as Parkinson's Disease (PD). In this paper, we present a digital image processing study, within the multi-layer network framework, combining more classifiers to evaluate the informative power of the MRI features, for the discrimination of normal controls (NC) and PD subjects. We define a network for each MRI scan; the nodes are the sub-volumes (patches) the images are divided into and the links are defined using the Pearson's pairwise correlation between patches. We obtain a multi-layer network whose important network features, obtained with different feature selection methods, are used to feed a supervised multi-level random forest classifier which exploits this base of knowledge for accurate classification. Method evaluation has been carried out using T1 MRI scans of 354 individuals, including 177 PD subjects and 177 NC from the Parkinson's Progression Markers Initiative (PPMI) database. The experimental results demonstrate that the features obtained from multiplex networks are able to accurately describe PD patterns. Besides, also if a privileged scale for studying PD disease exists, exploring the informative content of more scales leads to a significant improvement of the performances in the discrimination between disease and healthy subjects. In particular, this method gives a comprehensive overview of brain regions statistically affected by the disease, an additional value to the presented study.
Multilayer Controller for Outdoor Vehicle
DEFF Research Database (Denmark)
Reske-Nielsen, Anders; Mejnertsen, Asbjørn; Andersen, Nils Axel
2006-01-01
A full software and hardware solution has been designed, implemented and tested for control of a small agricultural automatic tractor. The objective was to realise a user-friendly, multi-layer controller architecture for an outdoor platform. The collaborative research work was done as a part...
New developments in Ni/Ti multilayers
Energy Technology Data Exchange (ETDEWEB)
Anderson, I.; Hoghoj, P. [Institut Max von Laue - Paul Langevin (ILL), 38 - Grenoble (France)
1997-04-01
It is now 20 years since super-mirrors were first used as a neutron optical element. Since then the field of multilayer neutron-optics has matured with multilayers finding their way to application in many neutron scattering instruments. However, there is still room for progress in terms of multilayer quality, performance and application. Along with work on multilayers for neutron polarisation Ni/Ti super-mirrors have been optimised. The state-of-the-art Ni/Ti super-mirror performance and the results obtained in two neutron-optics applications of Ni/Ti multilayers are presented. (author).
Energy Technology Data Exchange (ETDEWEB)
Biyanto, Totok R. [Department of Engineering Physics, Institute Technology of Sepuluh Nopember Surabaya, Surabaya, Indonesia 60111 (Indonesia)
2016-06-03
Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO{sub 2} emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.
Directory of Open Access Journals (Sweden)
Ivana Đurđević Babić
2015-03-01
Full Text Available Student satisfaction with courses in academic institutions is an important issue and is recognized as a form of support in ensuring effective and quality education, as well as enhancing student course experience. This paper investigates whether there is a connection between student satisfaction with courses and log data on student courses in a virtual learning environment. Furthermore, it explores whether a successful classification model for predicting student satisfaction with course can be developed based on course log data and compares the results obtained from implemented methods. The research was conducted at the Faculty of Education in Osijek and included analysis of log data and course satisfaction on a sample of third and fourth year students. Multilayer Perceptron (MLP with different activation functions and Radial Basis Function (RBF neural networks as well as classification tree models were developed, trained and tested in order to classify students into one of two categories of course satisfaction. Type I and type II errors, and input variable importance were used for model comparison and classification accuracy. The results indicate that a successful classification model using tested methods can be created. The MLP model provides the highest average classification accuracy and the lowest preference in misclassification of students with a low level of course satisfaction, although a t-test for the difference in proportions showed that the difference in performance between the compared models is not statistically significant. Student involvement in forum discussions is recognized as a valuable predictor of student satisfaction with courses in all observed models.
Kumari, Amrita; Das, Suchandan Kumar; Srivastava, Prem Kumar
2016-04-01
Application of computational intelligence for predicting industrial processes has been in extensive use in various industrial sectors including power sector industry. An ANN model using multi-layer perceptron philosophy has been proposed in this paper to predict the deposition behaviors of oxide scale on waterwall tubes of a coal fired boiler. The input parameters comprises of boiler water chemistry and associated operating parameters, such as, pH, alkalinity, total dissolved solids, specific conductivity, iron and dissolved oxygen concentration of the feed water and local heat flux on boiler tube. An efficient gradient based network optimization algorithm has been employed to minimize neural predictions errors. Effects of heat flux, iron content, pH and the concentrations of total dissolved solids in feed water and other operating variables on the scale deposition behavior have been studied. It has been observed that heat flux, iron content and pH of the feed water have a relatively prime influence on the rate of oxide scale deposition in water walls of an Indian boiler. Reasonably good agreement between ANN model predictions and the measured values of oxide scale deposition rate has been observed which is corroborated by the regression fit between these values.
Atila, U.; Karas, I. R.; Turan, M. K.; Rahman, A. A.
2013-09-01
One of the most dangerous disaster threatening the high rise and complex buildings of today's world including thousands of occupants inside is fire with no doubt. When we consider high population and the complexity of such buildings it is clear to see that performing a rapid and safe evacuation seems hard and human being does not have good memories in case of such disasters like world trade center 9/11. Therefore, it is very important to design knowledge based realtime interactive evacuation methods instead of classical strategies which lack of flexibility. This paper presents a 3D-GIS implementation which simulates the behaviour of an intelligent indoor pedestrian navigation model proposed for a self -evacuation of a person in case of fire. The model is based on Multilayer Perceptron (MLP) which is one of the most preferred artificial neural network architecture in classification and prediction problems. A sample fire scenario following through predefined instructions has been performed on 3D model of the Corporation Complex in Putrajaya (Malaysia) and the intelligent evacuation process has been realized within a proposed 3D-GIS based simulation.
Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J
2015-01-01
In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.
Directory of Open Access Journals (Sweden)
Zhiqiang Guo
Full Text Available In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D2PCA and a Radial Basis Function Neural Network (RBFNN to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA and independent component analysis (ICA. The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.
Gonzalez Vilas, L.; Castro Fernandez, M.; Spyrakos, E.; Torres Palenzuela, J.
2013-08-01
In typical case 2 waters an accurate remote sensing retrieval of chlorophyll a (chla) is still challenging. There is a widespread understanding that universally applicable water constituent retrieval algorithms are currently not feasible, shifting the research focus to regionally specific implementations of powerful inversion methods. This study takes advantage of regionally specific chlorophyll a (chla) algorithms, which were developed by the authors of this abstract in previous works, and the characteristics of Medium Resolution Imaging Spectrometer (MERIS) in order to study harmful algal events in the optically complex waters of the Galician Rias (NW). Harmful algal events are a frequent phenomenon in this area with direct and indirect impacts to the mussel production that constitute a very important economic activity for the local community. More than 240 106 kg of mussel per year are produced in these highly primary productive upwelling systems. A MERIS archive from nine years (2003-2012) was analysed using regionally specific chla algorithms. The latter were developed based on Multilayer perceptron (MLP) artificial neural networks and fuzzy c-mean clustering techniques (FCM). FCM specifies zones (based on water leaving reflectances) where the retrieval algorithms normally provide more reliable results. Monthly chla anomalies and other statistics were calculated for the nine years MERIS archive. These results were then related to upwelling indices and other associated measurements to determine the driver forces for specific phytoplankton blooms. The distribution and changes of chla are also discussed.
Directory of Open Access Journals (Sweden)
Luciana C. Bucene
2004-12-01
Full Text Available Objetivando classificar terras para irrigação, faz-se necessário analisar e determinar alguns parâmetros, entre eles a produtividade do solo. A classificação de produtividade (comumente chamada fertilidade aparente é delimitada em cinco classes: muito alta, alta, média, baixa e muito baixa, e em cada classe é preciso avaliar certos atributos do solo, como pH, CTC (capacidade de troca de cátions, V% (índice de saturação por bases, P (fósforo, Mg (magnésio e K (potássio. Neste trabalho, objetivou-se identificar a produtividade na qual atributos do solo, da parte inicial da microbacia hidrográfica do Rio Pardo, localizada em Pardinho, SP, foram analisados e classificados nas classes que a delimitam, através de Redes Neurais Artificiais (RNAs utilizandose Perceptron Múltiplas Camadas (Multilayers Perceptrons - MLP com o algoritmo de treinamento "backpropagation"- classificador de padrões, obtendo-se um número ótimo de camadas intermediárias e de neurônios; resultando na classificação de produtividade, a situação ótima da rede obteve 78% dos resultados iguais aos desejados, com duas camadas de neurônios, uma das quais intermediária, com 5 neurônios, e uma camada de saída.Productivity data (commonly known as apparent fertility of the initial part of the river Pardo-SP watershed was analyzed and classified with Artificial Neural Networks (ANNs, in order to classify lands for irrigation. Soil attributes as pH, CEC (cation exchange capacity, V% (base saturation index, P (phosphorus, Mg (magnesium and K (potassium were defined in five classes: very high, high, medium, low and very low. Apparent fertility classification taking into account the five classes was performed by using Multiple Layers Perceptron (MLP. Backpropagation algorithm was performed with the training set. One hidden layer with 5 neurons was the situation that best performed.
Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach
Directory of Open Access Journals (Sweden)
Wenshuai Chen
2018-01-01
Full Text Available In this paper, a novel polarimetric synthetic aperture radar (PolSAR image classification method based on multilayer autoencoders and self-paced learning (SPL is proposed. The multilayer autoencoders network is used to learn the features, which convert raw data into more abstract expressions. Then, softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. When we optimize the multilayer autoencoders network, self-paced learning is used to accelerate the learning convergence and achieve a stronger generalization capability. Under this learning paradigm, the network learns the easier samples first and gradually involves more difficult samples in the training process. The proposed method achieves the overall classification accuracies of 94.73%, 94.82% and 78.12% on the Flevoland dataset from AIRSAR, Flevoland dataset from RADARSAT-2 and Yellow River delta dataset, respectively. Such results are comparable with other state-of-the-art methods.
Huang, Qiushi; Louis, Eric; Bijkerk, Frederik; de Boer, Meint J.; von Blanckenhagen, G.
2016-01-01
A multilayer mirror (M) reflecting extreme ultraviolet (EUV) radiation from a first wave-length range in a EUV spectral region comprises a substrate (SUB) and a stack of layers (SL) on the substrate, the stack of layers comprising layers comprising a low index material and a high index material, the
A multi-scale hybrid neural network retrieval model for dust storm detection, a study in Asia
Wong, Man Sing; Xiao, Fei; Nichol, Janet; Fung, Jimmy; Kim, Jhoon; Campbell, James; Chan, P. W.
2015-05-01
Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the large uncertainties in global climate modeling, due to its significant impact on the radiation budget and atmospheric stability. Observations of dust storms in humid tropical south China (e.g. Hong Kong), are challenging due to high industrial pollution from the nearby Pearl River Delta region. This study develops a method for dust storm detection by combining ground station observations (PM10 concentration, AERONET data), geostationary satellite images (MTSAT), and numerical weather and climatic forecasting products (WRF/Chem). The method is based on a hybrid neural network (NN) retrieval model for two scales: (i) a NN model for near real-time detection of dust storms at broader regional scale; (ii) a NN model for detailed dust storm mapping for Hong Kong and Taiwan. A feed-forward multilayer perceptron (MLP) NN, trained using back propagation (BP) algorithm, was developed and validated by the k-fold cross validation approach. The accuracy of the near real-time detection MLP-BP network is 96.6%, and the accuracies for the detailed MLP-BP neural network for Hong Kong and Taiwan is 74.8%. This newly automated multi-scale hybrid method can be used to give advance near real-time mapping of dust storms for environmental authorities and the public. It is also beneficial for identifying spatial locations of adverse air quality conditions, and estimates of low visibility associated with dust events for port and airport authorities.
Binary tissue classification on wound images with neural networks and bayesian classifiers.
Veredas, Francisco; Mesa, Héctor; Morente, Laura
2010-02-01
A pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, or friction. Diagnosis, treatment, and care of pressure ulcers are costly for health services. Accurate wound evaluation is a critical task for optimizing the efficacy of treatment and care. Clinicians usually evaluate each pressure ulcer by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. In this paper, a hybrid approach based on neural networks and Bayesian classifiers is used in the design of a computational system for automatic tissue identification in wound images. A mean shift procedure and a region-growing strategy are implemented for effective region segmentation. Color and texture features are extracted from these segmented regions. A set of k multilayer perceptrons is trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes which are determined by clinical experts. This training procedure is driven by a k-fold cross-validation method. Finally, a Bayesian committee machine is formed by training a Bayesian classifier to combine the classifications of the k neural networks. Specific heuristics based on the wound topology are designed to significantly improve the results of the classification. We obtain high efficiency rates from a binary cascade approach for tissue identification. Results are compared with other similar machine-learning approaches, including multiclass Bayesian committee machine classifiers and support vector machines. The different techniques analyzed in this paper show high global classification accuracy rates. Our binary cascade approach gives high global performance rates (average sensitivity =78.7% , specificity =94.7% , and accuracy =91.5% ) and shows the highest average sensitivity score ( =86.3%) when detecting
A screening system for smear-negative pulmonary tuberculosis using artificial neural networks.
de O Souza Filho, João B; de Seixas, José Manoel; Galliez, Rafael; de Bragança Pereira, Basilio; de Q Mello, Fernanda C; Dos Santos, Alcione Miranda; Kritski, Afranio Lineu
2016-08-01
Molecular tests show low sensitivity for smear-negative pulmonary tuberculosis (PTB). A screening and risk assessment system for smear-negative PTB using artificial neural networks (ANNs) based on patient signs and symptoms is proposed. The prognostic and risk assessment models exploit a multilayer perceptron (MLP) and inspired adaptive resonance theory (iART) network. Model development considered data from 136 patients with suspected smear-negative PTB in a general hospital. MLP showed higher sensitivity (100%, 95% confidence interval (CI) 78-100%) than the other techniques, such as support vector machine (SVM) linear (86%; 95% CI 60-96%), multivariate logistic regression (MLR) (79%; 95% CI 53-93%), and classification and regression tree (CART) (71%; 95% CI 45-88%). MLR showed a slightly higher specificity (85%; 95% CI 59-96%) than MLP (80%; 95% CI 54-93%), SVM linear (75%, 95% CI 49-90%), and CART (65%; 95% CI 39-84%). In terms of the area under the receiver operating characteristic curve (AUC), the MLP model exhibited a higher value (0.918, 95% CI 0.824-1.000) than the SVM linear (0.796, 95% CI 0.651-0.970) and MLR (0.782, 95% CI 0.663-0.960) models. The significant signs and symptoms identified in risk groups are coherent with clinical practice. In settings with a high prevalence of smear-negative PTB, the system can be useful for screening and also to aid clinical practice in expediting complementary tests for higher risk patients. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Munro, Kelly; Miller, Thomas H; Martins, Claudia P B; Edge, Anthony M; Cowan, David A; Barron, Leon P
2015-05-29
The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of <1.3min at the 75th percentile of all measured tR data in wastewater samples (<10% of the total runtime). Coefficients of determination for 30 blind test compounds in wastewater matrices lay at or above R(2)=0.92. Finally, the model was evaluated for application to the semi-targeted identification of pharmaceutical residues during a weeklong wastewater sampling campaign. The model successfully identified native compounds at a rate of 83±4% and 73±5% in influent and effluent extracts, respectively. The use of an HRMS database and the optimised ANN model was also applied to shortlisting of 37 additional compounds in wastewater. Ultimately, this research will potentially enable faster identification of emerging contaminants in the environment through more efficient post-acquisition data mining. Copyright © 2015 Elsevier B.V. All rights reserved.
Multilayer X-ray optics at CHESS.
Kazimirov, Alexander; Smilgies, Detlef M; Shen, Qun; Xiao, Xianghui; Hao, Quan; Fontes, Ernest; Bilderback, Donald H; Gruner, Sol M; Platonov, Yuriy; Martynov, Vladimir V
2006-03-01
Almost half of the X-ray beamlines at the Cornell High Energy Synchrotron Source (CHESS) are based on multilayer optics. ;Traditional' multilayers with an energy resolution of DeltaE/E approximately 2% are routinely used to deliver X-ray flux enhanced by a factor of 10(2) in comparison with standard Si(111) optics. Sagittal-focusing multilayers with fixed radius provide an additional factor of 10 gain in flux density. High-resolution multilayer optics with DeltaE/E approximately 0.2% are now routinely used by MacCHESS crystallographers. New wide-bandpass multilayers with DeltaE/E = 5% and 10% have been designed and tested for potential applications in macromolecular crystallography. Small d-spacing multilayers with d < or = 20 A have been successfully used to extend the energy range of multilayer optics. Analysis of the main characteristics of the Mo/B4C and W/B4C small d-spacing multilayer optics shows enhancement in their performance at higher energies. Chemical vapour deposited SiC, with a bulk thermal conductivity of a factor of two higher than that of silicon, has been successfully introduced as a substrate material for multilayer optics. Characteristics of different types of multilayer optics at CHESS beamlines and their applications in a variety of scattering, diffraction and imaging techniques are discussed.
Neural Networks in Control Applications
DEFF Research Database (Denmark)
Sørensen, O.
The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...
Neural Networks in Control Applications
DEFF Research Database (Denmark)
Sørensen, O.
simulated process and compared. The closing chapter describes some practical experiments, where the different control concepts and training methods are tested on the same practical process operating in very noisy environments. All tests confirm that neural networks also have the potential to be trained......The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...