Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network
DEFF Research Database (Denmark)
Míguez González, M; López Peña, F.; Díaz Casás, V.; Galeazzi, Roberto; Blanke, Mogens
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 is......Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been...... shown to be capable of forecasting the ship’s roll motion in realistic scenarios....
Classification of fused face images using multilayer perceptron neural network
Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas
2010-01-01
This paper presents a concept of image pixel fusion of visual and thermal faces, which can significantly improve the overall performance of a face recognition system. Several factors affect face recognition performance including pose variations, facial expression changes, occlusions, and most importantly illumination changes. So, image pixel fusion of thermal and visual images is a solution to overcome the drawbacks present in the individual thermal and visual face images. Fused images are projected into eigenspace and finally classified using a multi-layer perceptron. In the experiments we have used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 95.07%. The main objective of employing fusion is to produce a fused image that provides the most detailed and reliable information. Fusion of multip...
DEFF Research Database (Denmark)
Proud, Simon Richard
2015-01-01
A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 ?m) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.
Apply Multi-Layer Perceptrons Neural Network for Off-Line Signature Verification and Recognition
Directory of Open Access Journals (Sweden)
Suhail Odeh
2011-11-01
Full Text Available This paper discusses the applying of Multi-layer perceptrons for signature verification and recognition using a new approach enables the user to recognize whether a signature is original or a fraud. The approach starts by scanning images into the computer, then modifying their quality through image enhancement and noise reduction, followed by feature extraction and neural network training, and finally verifies the authenticity of the signature. The paper discusses the different stages of the process including: image pre-processing, feature extraction and pattern recognition through neural networks.
Classification of fuels using multilayer perceptron neural networks
International Nuclear Information System (INIS)
Electrical impedance data obtained with an array of conducting polymer chemical sensors was used by a neural network (ANN) to classify fuel adulteration. Real samples were classified with accuracy greater than 90% in two groups: approved and adulterated.
Directory of Open Access Journals (Sweden)
Alireza Taravat
2015-02-01
Full Text Available A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 ?m with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.
Generation of hourly irradiation synthetic series using the neural network multilayer perceptron
Energy Technology Data Exchange (ETDEWEB)
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)
Highly Accurate Multi-layer Perceptron Neural Network for Air Data System
Directory of Open Access Journals (Sweden)
H. S. Krishna
2009-11-01
Full Text Available The error backpropagation multi-layer perceptron algorithm is revisited. This algorithm is used to train and validate two models of three-layer neural networks that can be used to calibrate a 5-hole pressure probe. This paper addresses Occam's Razor problem as it describes the adhoc training methodology applied to improve accuracy and sensitivity. The trained outputs from 5-4-3 feed-forward network architecture with jump connection are comparable to second decimal digit (~0.05 accuracy, hitherto unreported in literature.Defence Science Journal, 2009, 59(6, pp.670-674, DOI:http://dx.doi.org/10.14429/dsj.59.1574
LALIT KUMAR BEHERA; MAYA NAYAK; SAREETA MOHANTY
2011-01-01
This paper presents discrete wavelet transform and the S-transform based neural classifier scheme used for time series data mining of power quality events occurring due to power signal disturbances. The DWT and the S –transform are used for feature extraction and then the extracted features are classified with neural classifiers such as multilayered perceptron network (MLP) for pattern classification, data mining and subsequent knowledge discovery.
Geomagnetic Dst index forecast using a multilayer perceptrons artificial neural network
International Nuclear Information System (INIS)
Complete text of publication follows. The best known manifestations of the impact of solar wind on the magnetosphere are the geomagnetic storms. The prediction of geomagnetic field behavior allows the alert of geomagnetic storms occurrence, as those phenomena can cause many damages in the planet. The Artificial Intelligence tools have been applied in many multidisciplinary studies, covering several areas of knowledge, as a choice of approach to the solution of problems with characteristics like non-linearity, imprecision, and other features that can not be easily solved with conventional computational models. Techniques such as Artificial Neural Networks, Expert Systems and Decision Trees have been used in the Space Weather studies to perform tasks such as forecasting geomagnetic storms and the investigation of rules and parameters related on its occurrence. The main focus of this work is on forecasting the geomagnetic field behavior, represented this time by the Dst index, using for that task, mainly, the interplanetary magnetic field components and solar wind data. The tool chosen here to solve the non-linear problem was a Multi-layer Perceptrons Artificial Neural Network, trained with the backpropagation algorithm. Unlike what was done in other studies, we chose to predict calm and disturbed periods like, for example, a full month of data, for application in a real time forecasting system. It was possible to predict the geomagnetic Dst index one or two hours before with great percentage efficiency.
DEFF Research Database (Denmark)
Kucuk, Nil; Manohara, S.R.; Hanagodimath, S.M.; Gerward, L.
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 0.015–15MeV, and...
Ahmed, Kamal; Shahid, Shamsuddin; Haroon, Sobri Bin; Xiao-jun, Wang
2015-08-01
Downscaling rainfall in an arid region is much challenging compared to wet region due to erratic and infrequent behaviour of rainfall in the arid region. The complexity is further aggregated due to scarcity of data in such regions. 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 one of the most vulnerable areas of Pakistan to climate change. The National Center for Environmental Prediction (NCEP) reanalysis datasets from 20 grid points surrounding the study area were used to select the predictors using principal component analysis. Monthly rainfall data for the time periods 1961-1990 and 1991-2001 were used for the calibration and validation of the MLP model, respectively. The performance of the model was assessed using various statistics including mean, variance, quartiles, root mean square error (RMSE), mean bias error (MBE), coefficient of determination (R 2) and Nash-Sutcliffe efficiency (NSE). Comparisons of mean monthly time series of observed and downscaled rainfall showed good agreement during both calibration and validation periods, while the downscaling model was found to underpredict rainfall variance in both periods. Other statistical parameters also revealed good agreement between observed and downscaled rainfall during both calibration and validation periods in most of the stations.
Indian Academy of Sciences (India)
Kamal Ahmed; Shamsuddin Shahid; Sobri Bin Haroon; Wang Xiao-Jun
2015-08-01
Downscaling rainfall in an arid region is much challenging compared to wet region due to erratic and infrequent behaviour of rainfall in the arid region. The complexity is further aggregated due to scarcity of data in such regions. 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 one of the most vulnerable areas of Pakistan to climate change. The National Center for Environmental Prediction (NCEP) reanalysis datasets from 20 grid points surrounding the study area were used to select the predictors using principal component analysis. Monthly rainfall data for the time periods 1961â€“1990 and 1991â€“2001 were used for the calibration and validation of the MLP model, respectively. The performance of the model was assessed using various statistics including mean, variance, quartiles, root mean square error (RMSE), mean bias error (MBE), coefficient of determination (R2) and Nashâ€“Sutcliffe efficiency (NSE). Comparisons of mean monthly time series of observed and downscaled rainfall showed good agreement during both calibration and validation periods, while the downscaling model was found to underpredict rainfall variance in both periods. Other statistical parameters also revealed good agreement between observed and downscaled rainfall during both calibration and validation periods in most of the stations.
Quaternionic Multilayer Perceptron with Local Analyticity
Directory of Open Access Journals (Sweden)
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 neuronsâ€™ 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.
Energy Technology Data Exchange (ETDEWEB)
Vaziri, Nima [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of)]. E-mail: n.vaziri@gmail.com; Hojabri, Alireza [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of); Erfani, Ali [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of); Monsefi, Mehrdad [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of); Nilforooshan, Behnam [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of)
2007-02-15
Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported.
International Nuclear Information System (INIS)
Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
A. Piotrowski
2007-08-01
Full Text Available The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on longitudinal dispersion coefficients, this new approach relies more heavily on measurements of concentration collected during tracer tests over a range of flow conditions, but fewer hydraulic and morphological data are needed. The study is based upon 26 tracer experiments performed in a small river in Edinburgh, UK (Murray Burn at various flow rates in a 540 m long reach. The only data used in this study were concentration measurements collected at 4 cross-sections, distances between the cross-sections and the injection site, time, as well as flow rate and water velocity, obtained according to the data measured at the 1st and 2nd cross-sections.
The four main features of concentration versus time profiles at a particular cross-section, namely the peak concentration, the arrival time of the peak at the cross-section, and the shapes of the rising and falling limbs of the profile are modeled, and for each of them a separately designed neural network was used. There was also a variant investigated in which the conservation of the injected mass was assured by adjusting the predicted peak concentration. The neural network methods were compared with the unit peak attenuation curve concept.
In general the neural networks predicted the main features of the concentration profiles satisfactorily. The predicted peak concentrations were generally better than those obtained using the unit peak attenuation method, and the method with mass-conservation assured generally performed better than the method that did not account for mass-conservation. Predictions of peak travel time were also better using the neural networks than the unit peak attenuation method. Including more data into the neural network training set clearly improved the prediction of the shapes of the concentration profiles. Similar improvements in peak concentration were less significant and the travel time prediction appeared to be largely unaffected.
Directory of Open Access Journals (Sweden)
A. Piotrowski
2007-12-01
Full Text Available The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on longitudinal dispersion coefficients, this new approach relies more heavily on measurements of concentration collected during tracer tests over a range of flow conditions, but fewer hydraulic and morphological data are needed. The study is based upon 26 tracer experiments performed in a small river in Edinburgh, UK (Murray Burn at various flow rates in a 540 m long reach. The only data used in this study were concentration measurements collected at 4 cross-sections, distances between the cross-sections and the injection site, time, as well as flow rate and water velocity, obtained according to the data measured at the 1st and 2nd cross-sections.
The four main features of concentration versus time profiles at a particular cross-section, namely the peak concentration, the arrival time of the peak at the cross-section, and the shapes of the rising and falling limbs of the profile are modeled, and for each of them a separately designed neural network was used. There was also a variant investigated in which the conservation of the injected mass was assured by adjusting the predicted peak concentration. The neural network methods were compared with the unit peak attenuation curve concept.
In general the neural networks predicted the main features of the concentration profiles satisfactorily. The predicted peak concentrations were generally better than those obtained using the unit peak attenuation method, and the method with mass-conservation assured generally performed better than the method that did not account for mass-conservation. Predictions of peak travel time were also better using the neural networks than the unit peak attenuation method. Including more data into the neural network training set clearly improved the prediction of the shapes of the concentration profiles. Similar improvements in peak concentration were less significant and the travel time prediction appeared to be largely unaffected.
Sarakhs branch; Sarakhs, Iran.
2012-01-01
This paper presents a multi-layered perceptronneural network (MLPNN) method to solve the combinedeconomic and emission dispatch (CEED) problem. The harmfulecological effects caused by the emission of particulate andgaseous pollutants like sulfur dioxide (SO2) and oxides ofnitrogen ( NOx ) can be reduced by adequate distribution ofload between the plants of a power system. However, this leadsto a noticeable increase in the operating cost of the plants. Thispaper presents the (MLPNN) method app...
Wind speed estimation using multilayer perceptron
International Nuclear Information System (INIS)
Highlights: • We present a method for determining the average wind speed using neural networks. • We use data from that site in the short term and data from other nearby stations. • The inputs used in the ANN were wind speed and direction data from a station. • The method allows knowing the wind speed without topographical data. - Abstract: Wind speed knowledge is prerequisite in the siting of wind turbines. In consequence the wind energy use requires meticulous and specified knowledge of the wind characteristics at a location. This paper presents a method for determining the annual average wind speed at a complex terrain site by using neural networks, when only short term data are available for that site. This information is useful for preliminary calculations of the wind resource at a remote area having only a short time period of wind measurements measurement in a site. Artificial neural networks are useful for implementing non-linear process variables over time, and therefore are a useful tool for estimating the wind speed. The neural network used is multilayer perceptron with three layers and the supervised learning algorithm used is backpropagation. The inputs used in the neural network were wind speed and direction data from a single station, and the training patterns used correspond to sixty days data. The results obtained by simulating the annual average wind speed at the selected site based on data from nearby stations with correlation coefficients above 0.5 were satisfactory, compared with actual values. Reliable estimations were obtained, with errors below 6%
Auto-kernel using multilayer perceptron
Directory of Open Access Journals (Sweden)
Wei-Chen Cheng
2012-06-01
Full Text Available This work presents a constructive method to train the multilayer perceptron layer after layer successively and to accomplish the kernel used in the support vector machine. Data in different classes will be trained to map to distant points in each layer. This will ease the mapping of the next layer. A perfect mapping kernel can be accomplished successively. Those distant mapped points can be discriminated easily by a single perceptron.
Multi-Layer Perceptrons and Symbolic Data
Rossi, Fabrice
2008-01-01
In some real world situations, linear models are not sufficient to represent accurately complex relations between input variables and output variables of a studied system. Multilayer Perceptrons are one of the most successful non-linear regression tool but they are unfortunately restricted to inputs and outputs that belong to a normed vector space. In this chapter, we propose a general recoding method that allows to use symbolic data both as inputs and outputs to Multilayer Perceptrons. The recoding is quite simple to implement and yet provides a flexible framework that allows to deal with almost all practical cases. The proposed method is illustrated on a real world data set.
Multi-Layer Perceptrons and Symbolic Data
Rossi, Fabrice; Conan-Guez, Brieuc
2008-01-01
In some real world situations, linear models are not sufficient to represent accurately complex relations between input variables and output variables of a studied system. Multilayer Perceptrons are one of the most successful non-linear regression tool but they are unfortunately restricted to inputs and outputs that belong to a normed vector space. In this chapter, we propose a general recoding method that allows to use symbolic data both as inputs and outputs to Multilayer ...
A Parallel Framework for Multilayer Perceptron for Human Face Recognition
Mita Nasipuri; Mahantapas Kundu; Dipak Kumar Basu; Debotosh Bhattacharjee; Mrinal Kanti Bhowmik
2010-01-01
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perce...
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. PMID:25966483
KLASIFIKASI WEBSITE MENGGUNAKAN ALGORITMA MULTILAYER PERCEPTRON
Directory of Open Access Journals (Sweden)
Nyoman Purnama
2014-08-01
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%.
DEFF Research Database (Denmark)
Kucuk, Nil; Manohara, S.R.
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 0.015–15MeV, and for penetration depths up to 10 mfp (mean-free-path). The MLPNNs have been trained by a Levenberg–Marquardt learning algorithm. The developed model is in 99% agreement with the ANSI/ANS-6.4.3 standard data set. Furthermore, the model is fast and does not require tremendous computational efforts. The estimated BA 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.
Multilayer perceptron-based DFE with lattice structure.
Zerguine, A; Shafi, A; Bettayeb, M
2001-01-01
The severely distorting channels limit the use of linear equalizers and the use of the nonlinear equalizers then becomes justifiable. Neural-network-based equalizers, especially the multilayer perceptron (MLP)-based equalizers, are computationally efficient alternative to currently used nonlinear filter realizations, e.g., the Volterra type. The drawback of the MLP-based equalizers is, however, their slow rate of convergence, which limit their use in practical systems. In this work, the effect of whitening the input data in a multilayer perceptron-based decision feedback equalizer (DFE) is evaluated. It is shown from computer simulations that whitening the received data employing adaptive lattice channel equalization algorithms improves the convergence rate and bit error rate performances of multilayer perceptron-based DFE. The adaptive lattice algorithm is a modification to the one developed by Ling and Proakis (1985). The consistency in performance is observed in both time-invariant and time-varying channels. Finally, it is found in this work that, for time-invariant channels, the MLP DFE outperforms the least mean squares (LMS)-based DFE. However, for time-varying channels comparable performance is obtained for the two configurations. PMID:18249886
Scientific Electronic Library Online (English)
Héctor, Tabares; John, Branch; Jaime, Valencia.
2006-09-01
Full Text Available En este trabajo se aplica un método constructivo aproximado para encontrar arquitecturas de redes neuronales artificiales (RNA) de tipo perceptrón multicapa (PMC). El método se complementa con la técnica de la búsqueda forzada de mejores mínimos locales. El entrenamiento de la red se lleva a cabo a [...] través del algoritmo gradiente descendente básico (GDB); se aplican técnicas como la repetición del entrenamiento y la detención temprana (validación cruzada), para mejorar los resultados. El criterio de evaluación se basa en las habilidades de aprendizaje y de generalización de las arquitecturas generadas específicas de un dominio. Se presentan resultados experimentales con los cuales se demuestra la efectividad del método propuesto y comparan con las arquitecturas halladas por otros métodos. Abstract in english This paper deals with an approximate constructive method to find architectures of artificial neuronal network (ANN) of the type MultiLayer Percetron (MLP) which solves a particular problem. This method is supplemented with the technique of the Forced search of better local minima. The training of th [...] e net uses an algorithm basic descending gradient (BDG). Techniques such as repetition of the training and the early stopping (cross validation) are used to improve the results. The evaluation approach is based not only on the learning abilities but also on the generalization of the specific generated architectures of a domain. Experimental results are presented in order to prove the effectiveness of the proposed method. These are compared with architectures found by other methods.
International Nuclear Information System (INIS)
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 0.015–15 MeV, and for penetration depths up to 10 mfp (mean-free-path). The MLPNNs have been trained by a Levenberg–Marquardt learning algorithm. The developed model is in 99% agreement with the ANSI/ANS-6.4.3 standard data set. Furthermore, the model is fast and does not require tremendous computational efforts. The estimated BA 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. - Highlights: ? Gamma-ray energy absorption buildup factors estimation in TLD materials. ? The ANN approach can be alternative to G-P fitting method for BA calculations. ? The applied model is not time-consuming and easily predicted
Çaylak, Ça?r?; Kaftan, ?lknur
2014-12-01
This study proposes the use of multi-layer perceptron neural networks (MLPNN) to invert dispersion curves obtained via multi-channel analysis of surface waves (MASW) for shear S-wave velocity profile. The dispersion curve used in inversion includes the fundamental-mode dispersion data. In order to investigate the applicability and performance of the proposed MLPNN algorithm, test studies were performed using both synthetic and field examples. Gaussian random noise with a standard deviation of 4 and 8% was added to the noise-free test data to make the synthetic test more realistic. The model parameters, such as S-wave velocities and thicknesses of the synthetic layered-earth model, were obtained for different S/N ratios and noise-free data. The field survey was performed over the natural gas pipeline, located in the Germencik district of Ayd?n city, western Turkey. The results show that depth, velocity, and location of the embedded natural gas pipe are successfully estimated with reasonably good approximation.
Mandal, Uttam; Gowda, Veeran; Ghosh, Animesh; Bose, Anirbandeep; Bhaumik, Uttam; Chatterjee, Bappaditya; Pal, Tapan Kumar
2008-02-01
The aim of the present study was to apply the simultaneous optimization method incorporating Artificial Neural Network (ANN) using Multi-layer Perceptron (MLP) model to the development of a metformin HCl 500 mg sustained release matrix tablets with an optimized in vitro release profile. The amounts of HPMC K15M and PVP K30 at three levels (-1, 0, +1) for each were selected as casual factors. In vitro dissolution time profiles at four different sampling times (1 h, 2 h, 4 h and 8 h) were chosen as output variables. 13 kinds of metformin matrix tablets were prepared according to a 2(3) factorial design (central composite) with five extra center points, and their dissolution tests were performed. Commercially available STATISTICA Neural Network software (Stat Soft, Inc., Tulsa, OK, U.S.A.) was used throughout the study. The training process of MLP was completed until a satisfactory value of root square mean (RSM) for the test data was obtained using feed forward back propagation method. The root mean square value for the trained network was 0.000097, which indicated that the optimal MLP model was reached. The optimal tablet formulation based on some predetermined release criteria predicted by MLP was 336 mg of HPMC K15M and 130 mg of PVP K30. Calculated difference (f(1) 2.19) and similarity (f(2) 89.79) factors indicated that there was no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network with MLP, to assist in development of sustained release dosage forms. PMID:18239298
Online learning dynamics of multilayer perceptrons with unidentifiable parameters
International Nuclear Information System (INIS)
In the over-realizable learning scenario of multilayer perceptrons, in which the student network has a larger number of hidden units than the true or optimal network, some of the weight parameters are unidentifiable. In this case, the teacher network consists of a union of optimal subspaces included in the parameter space. The optimal subspaces, which lead to singularities, are known to affect the estimation performance of neural networks. Using statistical mechanics, we investigate the online learning dynamics of two-layer neural networks in the over-realizable scenario with unidentifiable parameters. We show that the convergence speed strongly depends on the initial parameter conditions. We also show that there is a quasi-plateau around the optimal subspace, which differs from the well-known plateaus caused by permutation symmetry. In addition, we discuss the property of the final learning state, relating this to the singular structures
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Rodrigo Martins da Silva
2011-12-01
Full Text Available There are several neural network implementations using either software, hardware-based or a hardware/software co-design. This work proposes a hardware architecture to implement an artificial neural network (ANN, whose topology is the multilayer perceptron (MLP. In this paper, we explore the parallelism of neural networks and allow on-thefly changes of the number of inputs, number of layers and number of neurons per layer of the net. This reconfigurability characteristic permits that any application of ANNs may be implemented using the proposed hardware. In order to reduce the processing time that is spent in arithmetic computation, a real number is represented using a fraction of integers. In this way, the arithmetics is limited to integer operations, performed by fast combinational circuits. A simple state machine is required to control sums and products of fractions. Sigmoid is used as the activation function in the proposed implementation. It is approximated by polynomials, whose underlying computation requires only sums and products. A theorem is introduced and proven so as to cover the arithmetic strategy of the computation of the activation function. Thus, the arithmetic circuitry used to implement the neuron weighted sum is reused for computing the sigmoid. this resource sharing decreased drastically the total area of the system. After modeling and simulation for functionality validation, the proposed architecture synthesized using reconfigurable hardware. The results are promising.Em termos computacionais, uma rede neural artificial (RNA pode ser implementada em software ou em hardware, ou ainda de maneira híbrida, combinando ambos os recursos. O presente trabalho propõe uma arquitetura de hardware para a computação de uma rede neural do tipo perceptron com múltiplas camadas (MLP. Soluções em hardware tendem a ser mais eficientes do que soluções em software. O projeto em questão, além de explorar fortemente o paralelismo das redes neurais, permite alterações do número de entradas, número de camadas e de neurônios por camada, de modo que diversas aplicações de RNAs possam ser executadas no hardware proposto. Visando a uma redução de tempo do processamento aritmético, um número real é aproximado por uma fração de inteiros. Dessa forma, as operações aritméticas limitam-se a operações inteiras, executadas por circuitos combinacionais. Uma simples máquina de estados é demandada para controlar somas e produtos de frações. A função de ativação usada neste projeto é a sigmóide. Essa função é aproximada mediante o uso de polinômios, cujas operações são regidas por somas e produtos. Um teorema é introduzido e provado, permitindo a fundamentação da estratégia de cálculo da função de ativação. Dessa forma, reaproveita-se o circuito aritmético da soma ponderada para também computar a sigmóide. Essa re-utilização dos recursos levou a uma redução drástica de área total de circuito. Após modelagem e simulação para validação do bom funcionamento, a arquitetura proposta foi sintetizada utilizando recursos reconfiguráveis, do tipo FPGA. Os resultados são promissores.
Scientific Electronic Library Online (English)
Rodrigo Martins da, Silva; Luiza de Macedo, Mourelle; Nadia, Nedjah.
2011-12-01
Full Text Available Em termos computacionais, uma rede neural artificial (RNA) pode ser implementada em software ou em hardware, ou ainda de maneira híbrida, combinando ambos os recursos. O presente trabalho propõe uma arquitetura de hardware para a computação de uma rede neural do tipo perceptron com múltiplas camadas [...] (MLP). Soluções em hardware tendem a ser mais eficientes do que soluções em software. O projeto em questão, além de explorar fortemente o paralelismo das redes neurais, permite alterações do número de entradas, número de camadas e de neurônios por camada, de modo que diversas aplicações de RNAs possam ser executadas no hardware proposto. Visando a uma redução de tempo do processamento aritmético, um número real é aproximado por uma fração de inteiros. Dessa forma, as operações aritméticas limitam-se a operações inteiras, executadas por circuitos combinacionais. Uma simples máquina de estados é demandada para controlar somas e produtos de frações. A função de ativação usada neste projeto é a sigmóide. Essa função é aproximada mediante o uso de polinômios, cujas operações são regidas por somas e produtos. Um teorema é introduzido e provado, permitindo a fundamentação da estratégia de cálculo da função de ativação. Dessa forma, reaproveita-se o circuito aritmético da soma ponderada para também computar a sigmóide. Essa re-utilização dos recursos levou a uma redução drástica de área total de circuito. Após modelagem e simulação para validação do bom funcionamento, a arquitetura proposta foi sintetizada utilizando recursos reconfiguráveis, do tipo FPGA. Os resultados são promissores. Abstract in english There are several neural network implementations using either software, hardware-based or a hardware/software co-design. This work proposes a hardware architecture to implement an artificial neural network (ANN), whose topology is the multilayer perceptron (MLP). In this paper, we explore the parall [...] elism of neural networks and allow on-thefly changes of the number of inputs, number of layers and number of neurons per layer of the net. This reconfigurability characteristic permits that any application of ANNs may be implemented using the proposed hardware. In order to reduce the processing time that is spent in arithmetic computation, a real number is represented using a fraction of integers. In this way, the arithmetics is limited to integer operations, performed by fast combinational circuits. A simple state machine is required to control sums and products of fractions. Sigmoid is used as the activation function in the proposed implementation. It is approximated by polynomials, whose underlying computation requires only sums and products. A theorem is introduced and proven so as to cover the arithmetic strategy of the computation of the activation function. Thus, the arithmetic circuitry used to implement the neuron weighted sum is reused for computing the sigmoid. this resource sharing decreased drastically the total area of the system. After modeling and simulation for functionality validation, the proposed architecture synthesized using reconfigurable hardware. The results are promising.
A Parallel Framework for Multilayer Perceptron for Human Face Recognition
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Mita Nasipuri
2010-01-01
Full Text Available Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP have been demonstrated. The first architecture is All-Class-in-One-Network (ACON where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
Multilayer perceptron in damage detection of bridge structures
Pandey, P. C.; Barai, S. V.
1995-02-01
Recent developments in artificial neural networks (ANN) have opened up new possibilities in the domain of structural engineering. For inverse problems like structural identification of large civil engineerlng structures such as bridges and buildings where the in situ measured data are expected to be imprecise and often incomplete, the ANN holds greater promise. The detection of structural damage and identification of damaged element in a large complex structure is a challenging task indeed. This paper presents an application of multilayer perceptron in the damage detection of steel bridge structures. The ssues relating to the design of network and learning paradigm are addressed and network architectures have been developed with reference to trussed bridge structures. The training patterns are generated for multiple damaged zones in a structure and performance of the networks with one and two hidden layers are examined. It has been observed that the performance of the network with two hidden layers was better than that of a single-layer architecture in general. The engineering importance of the whole exercise is demonstrated from the fact that measured input at only a few locations in the structure is needed in the identification process using the ANN.
Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics
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N. Pedroni
2008-03-01
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.
Sartori, Michael A.; Passino, Kevin M.; Antsaklis, Panos J.
1992-01-01
In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.
Efficient training of multilayer perceptrons using principal component analysis
International Nuclear Information System (INIS)
A training algorithm for multilayer perceptrons is discussed and studied in detail, which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix computed from the example inputs and their target outputs. Typical properties of the training procedure are investigated by means of a statistical physics analysis in models of learning regression and classification tasks. We demonstrate that the procedure requires by far fewer examples for good generalization than traditional online training. For networks with a large number of hidden units we derive the training prescription which achieves, within our model, the optimal generalization behavior
Pham, Binh Thai; Tien Bui, Dieu; Pourghasemi, Hamid Reza; Indra, Prakash; Dholakia, M. B.
2015-12-01
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 NaÃ¯ve 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.
Dynamics of learning in multilayer perceptrons near singularities.
Cousseau, Florent; Ozeki, Tomoko; Amari, Shun-Ichi
2008-08-01
The dynamical behavior of learning is known to be very slow for the multilayer perceptron, being often trapped in the "plateau." It has been recently understood that this is due to the singularity in the parameter space of perceptrons, in which trajectories of learning are drawn. The space is Riemannian from the point of view of information geometry and contains singular regions where the Riemannian metric or the Fisher information matrix degenerates. This paper analyzes the dynamics of learning in a neighborhood of the singular regions when the true teacher machine lies at the singularity. We give explicit asymptotic analytical solutions (trajectories) both for the standard gradient (SGD) and natural gradient (NGD) methods. It is clearly shown, in the case of the SGD method, that the plateau phenomenon appears in a neighborhood of the critical regions, where the dynamical behavior is extremely slow. The analysis of the NGD method is much more difficult, because the inverse of the Fisher information matrix diverges. We conquer the difficulty by introducing the "blow-down" technique used in algebraic geometry. The NGD method works efficiently, and the state converges directly to the true parameters very quickly while it staggers in the case of the SGD method. The analytical results are compared with computer simulations, showing good agreement. The effects of singularities on learning are thus qualitatively clarified for both standard and NGD methods. PMID:18701364
Theoretical Properties of Projection Based Multilayer Perceptrons with Functional Inputs
Rossi, F; Rossi, Fabrice; Conan-Guez, Brieuc
2006-01-01
Many real world data are sampled functions. As shown by Functional Data Analysis (FDA) methods, spectra, time series, images, gesture recognition data, etc. can be processed more efficiently if their functional nature is taken into account during the data analysis process. This is done by extending standard data analysis methods so that they can apply to functional inputs. A general way to achieve this goal is to compute projections of the functional data onto a finite dimensional sub-space of the functional space. The coordinates of the data on a basis of this sub-space provide standard vector representations of the functions. The obtained vectors can be processed by any standard method. In our previous work, this general approach has been used to define projection based Multilayer Perceptrons (MLPs) with functional inputs. We study in this paper important theoretical properties of the proposed model. We show in particular that MLPs with functional inputs are universal approximators: they can approximate to ...
Second-Order Learning Methods for a Multilayer Perceptron
International Nuclear Information System (INIS)
First- and second-order learning methods for feed-forward multilayer neural networks are studied. Newton-type and quasi-Newton algorithms are considered and compared with commonly used back-propagation algorithm. It is shown that, although second-order algorithms require enhanced computer facilities, they provide better convergence and simplicity in usage. 13 refs., 2 figs., 2 tabs
Scientific Electronic Library Online (English)
José C, Cúrvelo Santana; Sidnei A, de Araújo; Joana P, M. Biazus; Roberto R, de Souza.
2015-04-01
Full Text Available En este trabajo se propone utilizar una Red Neuronal Artificial (RNA) Perceptrón Multicapa (PMC) para simular la variación de la concentración de proteína de acuerdo con el tiempo y también para determinar la hora final del procedimiento, además de los parámetros óptimos del proceso de biodegradació [...] n de las proteínas de un efluente de matadero. Para eso, han sido utilizadas las papaínas, presentes en el látex de la papaya (Carica papaya) con el objetivo de disminuir la concentración de proteínas de un efluente de matadero a pH (5 y 7) con una temperatura de (25 y 30° C) controlada. Los resultados mostraron que las papaínas redujeron de 82% a 91% la concentración de proteína en 30 y 40 h de proceso. Las simulaciones con la RNA apuntaron que las condiciones perfectas fueron obtenidas a pH 5, con 30 °C y en 35 h, en el cual se ha alcanzado una reducción de 91% de la concentración de proteínas. Abstract in english In this paper, the use of a multilayer perceptron (MLP) artificial neural network (ANN) is proposed to simulate the variation of protein concentration according to the time and also to determine the end and optimal conditions of the biodegradation process of wastewater from meat industry. To reduce [...] the protein concentration, papains from Carica papaya latex have been used at controlled condition of pH (5 and 7) and temperature (25 and 30 °C). Results showed that a reduction of 82 to 91% of protein concentration by the action of papains for 30 to 40 h of process time. Simulations showed that the best condition of the process occurred at pH 5, 30 °C and 35 h, in which a maximum biodegradation of 91% was obtained.
Ground Radar Target Classification Using Singular Value Decomposition and Multilayer Perceptron
I. Mokris; J. Kurty; Z. Matousek
2001-01-01
The paper deals with classification of ground radar targets. Areceived radar signal backscattered from a ground radar target wasdigitized and in the form of radar signal matrix utilized for a featureextraction based on Singular Value Decomposition. Furthermore, singularvalues of a backscattered radar signal matrix, as a target feature,were utilized for Radar Target Classification by multilayer perceptron.In the learning phase of a multilayer perceptron we used the learningtarget set and in th...
FORECASTING ON FOREX MARKET WITH RBF AND PERCEPTRON NEURAL NETWORKS
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ALEXANDRA KOTTILOVÁ
2012-01-01
Full Text Available This work deals with an alternative approach in financial modelling -artificial neural networks approach. The aim of this paper is to show that this type oftime series modelling is an excellent alternative to classical econometric modelling. Atfirst, neural networks using methods of supervised machine learning are discussed.After explaining theoretical basis of ANN, these models are then applied to specificexchange rate (AUD/USD. Finally, the comparison between statistical models andRBF and perceptron neural networks is made to illustrate the sense of using ANNmodels
Query-based learning applied to partially trained multilayer perceptrons.
Hwang, J N; Choi, J J; Oh, S; Marks, R J
1991-01-01
An approach is presented for query-based neural network learning. A layered perceptron partially trained for binary classification is considered. The single-output neuron is trained to be either a zero or a one. A test decision is made by thresholding the output at, for example, one-half. The set of inputs that produce an output of one-half forms the classification boundary. The authors adopted an inversion algorithm for the neural network that allows generation of this boundary. For each boundary point, the classification gradient can be generated. The gradient provides a useful measure of the steepness of the multidimensional decision surfaces. Conjugate input pairs are generated using the boundary point and gradient information and presented to an oracle for proper classification. These data are used to refine further the classification boundary, thereby increasing the classification accuracy. The result can be a significant reduction in the training set cardinality in comparison with, for example, randomly generated data points. An application example to power system security assessment is given. PMID:18276359
Classification of Log-Polar-Visual Eigenfaces using Multilayer Perceptron
Bhowmik, Mrinal Kanti; Nasipuri, Mita; Kundu, Mahantapas; Basu, Dipak Kumar
2010-01-01
In this paper we present a simple novel approach to tackle the challenges of scaling and rotation of face images in face recognition. The proposed approach registers the training and testing visual face images by log-polar transformation, which is capable to handle complicacies introduced by scaling and rotation. Log-polar images are projected into eigenspace and finally classified using an improved multi-layer perceptron. In the experiments we have used ORL face database and Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database for visual face images. Experimental results show that the proposed approach significantly improves the recognition performances from visual to log-polar-visual face images. In case of ORL face database, recognition rate for visual face images is 89.5% and that is increased to 97.5% for log-polar-visual face images whereas for OTCBVS face database recognition rate for visual images is 87.84% and 96.36% for log-polar-visual face images.
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.
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)
International Nuclear Information System (INIS)
The problem of pion-electron identification based on their energy losses in the TRD is considered in the frame of the CBM experiment. For particles identification an artificial neural network (ANN) was used, a multilayer perceptron realized in JETNET and ROOT packages. It is demonstrated that, in order to get correct and comparable results, it is important to define the network structure correctly. The recommendations for such a selection are given. In order to achieve an acceptable level of pions suppression, the energy losses need to be transformed to more 'effective' variables. The dependency of ANN output threshold for a fixed portion of electron loss on the particle momentum is presented
Face Recognition through Multilayer Perceptron (MLP and Learning Vector Quantization (LVQ
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Dr. Ikvinderpal Singh
2012-12-01
Full Text Available Face recognition is challenging problems and there is still a lot of work that needs to be done in this area. Over the past ten years, face recognition has received substantial attention from researchers in biometrics, pattern recognition, computer vision, and cognitive psychology communities. This common interest in facial recognition technology among researchers working in diverse fields is motivated both by the remarkable ability to recognize people and by the increased attention being devoted to security applications. Applications of face recognition can be found in security, tracking, multimedia, and entertainment domains.This paper presents a face recognition system using artificial neural network. Here, we have designed a neural network with some own set network parameters. The results presented here have been obtained using two basic methods: multilayer perceptron (MLP, and learning vector quantization (LVQ. In both cases, two kinds of data have been fed to the classifiers: reduced resolution images (gray level or segmented, and feature vectors. The experimental results also show that, for the approaches considered here, analyzing gray level images produced better results than analyzing geometrical features, either because of the errors introduced during their extraction or because the original images have a richer information content. Furthermore, training times were much shorter for LVQ than for MLP. On the other hand, MLP achieved lower error rates when dealing with geometrical features.
Using multilayer perceptron and a satellite image for the estimation of soil salinity
International Nuclear Information System (INIS)
Applying the model of the Perceptron multilayer with momentum of an artificial neural network particularly and a multispectral image of high resolution spatial and radiometric, for the first time estimated the salinity of the soil cultivated with sugar cane. The study area is the UBPC 'Lazaro Romero' of the sugar company 'Hector Molina' of the locality San Nicolas de Bari, Havana province, located at 22° 44' North latitude and 81 ° 56' longitude West. The experiments were made in the framework of the El-479 project funded by the Inter universities Council of Flanders, Belgium. 36 samples geo referenced of soils were taken at 3 depths in each of the 4 sugar cane selected blocks, which determined the electrical conductivity of the saturation extract; half of that amount of data was used for the training of the network and the other half for control in a computer program of the artificial neural network created to that effect, together with the reflectance of vegetation indexes for the image, which were maps of electrical conductivity of each block and bands. They were compared with those obtained by simple linear regression between the normalized difference vegetation index and electrical conductivity, Ndv with the approach of the neuronal network, the correlation coefficient was 0.78 to 0.83, while the linear regression was between 0.65 to 0.75
Analog Multilayer Perceptron Circuit with On-chip Learning: Portable Electronic Nose
Pan, Chih-Heng; Tang, Kea-Tiong
2011-09-01
This article presents an analog multilayer perceptron (MLP) neural network circuit with on-chip back propagation learning. This low power and small area analog MLP circuit is proposed to implement as a classifier in an electronic nose (E-nose). Comparing with the E-nose using microprocessor or FPGA as a classifier, the E-nose applying analog circuit as a classifier can be faster and much smaller, demonstrate greater power efficiency and be capable of developing a portable E-nose [1]. The system contains four inputs, four hidden neurons, and only one output neuron; this simple structure allows the circuit to have a smaller area and less power consumption. The circuit is fabricated using TSMC 0.18 ?m 1P6M CMOS process with 1.8 V supply voltage. The area of this chip is 1.353×1.353 mm2 and the power consumption is 0.54 mW. Post-layout simulations show that the proposed analog MLP circuit can be successively trained to identify three kinds of fruit odors.
Dines, John; Vepa, Jithendra
2007-01-01
We propose an alternative means of training a multilayer perceptron for the task of speech activity detection based on a criterion to minimise the error in the estimation of mean and variance statistics for speech cepstrum based features using the Kullback-Leibler divergence. We present our baseline and proposed speech activity detection approaches for multi-channel meeting room recordings and demonstrate the effectiveness of the new criterion by comparing the two approaches when used to carr...
Multilayer perceptron and regression modelling to forecast hourly nitrogen dioxide concentrations
Capilla, Carmen
2014-01-01
This paper presents the application of feed-forward multilayer perceptron networks and multiple regression models, to forecast hourly nitrogen dioxide levels 24 hours in advance. Input data are traffic and meteorological variables, and nitrogen dioxide hourly levels. The introduction of four periodic components (sine and cosine terms for the daily and weekly cycles), and nitrogen oxide hourly levels was analyzed in order to improve the prediction power. The data were measure...
Directory of Open Access Journals (Sweden)
Lenniet Coello
2015-01-01
Full Text Available The most widely used neural network model is Multilayer Perceptron (MLP, in which training of the connection weights is normally completed by a Back Propagation learning algorithm. G ood initial values of weights bear a fast convergence and a better generalization capability even with simple gradient - based error minimization techniques. This work presen ts a method to calculate the initial weights in order to train the Multilayer Perceptron Model. The method named PSO+RST+FUZZY is based on the similarity quality measure proposed within the framework of the extended Rough Set Theory that employs fuzzy sets to characterize the domain of similarity thresholds. Sensitivity of BP to initial weights with PSO+RST+FUZZY was studied experimentally, showing better performance than other methods used to calculate feature weights .
Hybrid Evolutionary Algorithm for Multilayer Perceptron Networks with Competetive Performance.
Czech Academy of Sciences Publication Activity Database
Neruda, Roman
Los Alamitos : IEEE, 2007, s. 1620-1627. ISBN 978-1-4244-1339-3. [CEC 2007. Congress on Evolutionary Computation. Singapore (SG), 25.09.2007-28.09.2007] R&D Projects: GA AV ?R 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : hybrid algorithms * evolutionary learning * neural networks Subject RIV: IN - Informatics, Computer Science
Classification of Parking Spots Using Multilayer Perceptron Networks
Directory of Open Access Journals (Sweden)
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
FPGA Implementation of Multilayer Perceptron for Modeling of Photovoltaic panel
International Nuclear Information System (INIS)
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
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.
Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification
Martin, Arnaud
2008-01-01
The sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can obtain, is the interpretation of different human experts, sometimes in conflict. In this paper, we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms. The classification is conducted by a multilayer perceptron, taking into account the uncertainty of the reality in the learning stage. The results of this seabed characterization are presented on real sonar images.
International Nuclear Information System (INIS)
Highlights: • Multilayer perceptrons are used to simulate the I–V curve of thin-film PV modules. • APE from the spectral irradiance was added as an input variable to the network. • A self-organised map is used to select the curves used for training the network. • Curve error and maximum power error decrease when using this technique. • This method could provide accurate estimation of the output of a PV plant. - Abstract: In this paper, we propose the use of a methodology to characterise the electrical parameters of several thin-film photovoltaic module technologies. This methodology allows us to use not only solar irradiance and module temperature as classical models do, but also spectral distribution of solar radiation. The methodology is based on the use of neural network models. From all measured I–V curves of a module, a previous selection of them has been used in order to train the neural network model. This selection is performed using a Kohonen self-organising map fed with spectral data. This spectral information has been added as an input to the neural network itself. The results show that the incorporation of spectral measurements to simulate thin-film modules improves significantly both the fitting of the predicted I–V curve to the measured one and the peak power point estimation
A New Approach to Predicting Bankruptcy: Combining DEA and Multi-Layer Perceptron
Directory of Open Access Journals (Sweden)
Ayan Mukhopadhyay
2012-07-01
Full Text Available The question of financial health and sustenance of a firm is so intriguing that it has spanned numerous studies. For investors,stakeholders and lenders, assessing the risk associated with an enterprise is vital. Several tools have been formulated to deal with predicting the solvency of a firm. This paper attempts to combine Data Envelopment Analysis and Multi-Layer Perceptron (MLP to suggest a new method for prediction of bankruptcy that not only focusses on historical financial data of firms that filed for bankruptcy like other past studies but also takes into account the data of those firms that were likely to do so. This method thus identifies firms that have a high chance of facing bankruptcy along with those that have filed for bankruptcy. The performance of this procedure is compared with MLP. The suggested method outperforms MLP in prediction of bankruptcy.
Stojnic, Mihailo
2013-01-01
Perceptrons have been known for a long time as a promising tool within the neural networks theory. The analytical treatment for a special class of perceptrons started in seminal work of Gardner \\cite{Gar88}. Techniques initially employed to characterize perceptrons relied on a statistical mechanics approach. Many of such predictions obtained in \\cite{Gar88} (and in a follow-up \\cite{GarDer88}) were later on established rigorously as mathematical facts (see, e.g. \\cite{Sch...
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.
Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method
International Nuclear Information System (INIS)
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
Autonomous Perceptron Neural Network Inspired from Quantum computing
Zidan, M.; Sagheer, A.; Metwally, N.
2015-01-01
Recently with the rapid development of technology, there are a lot of applications require to achieve low-cost learning in order to accomplish inexpensive computation. However the known computational power of classical artificial neural networks (CANN), they are not capable to provide low-cost learning due to many reasons such as linearity, complexity of architecture, etc. In contrast, quantum neural networks (QNN) may be representing a good computational alternate to CANN, ...
FORECASTING ON FOREX MARKET WITH RBF AND PERCEPTRON NEURAL NETWORKS
ALEXANDRA KOTTILOVÁ; LUKÁŠ FALÁT
2012-01-01
This work deals with an alternative approach in financial modelling -artificial neural networks approach. The aim of this paper is to show that this type oftime series modelling is an excellent alternative to classical econometric modelling. Atfirst, neural networks using methods of supervised machine learning are discussed.After explaining theoretical basis of ANN, these models are then applied to specificexchange rate (AUD/USD). Finally, the comparison between statistical models andRBF and ...
Sleep snoring detection using multi-layer neural networks.
Nguyen, Tan Loc; Won, Yonggwan
2015-08-17
Snoring detection is important for diagnosing obstructive sleep apnea syndrome (OSAS) and other respiratory sleep disorders. In general, audio signal processing such as snoring sound analysis uses the frequency characteristics of the signal. Recently, a correlational filter Multilayer Perceptron neural network (f-MLP) has been proposed, which has the first hidden layer of correlational filter operations in frequency domain. It demonstrated a superior classification performance for the pattern sets; of these, frequency information is the dominant feature for classification. The first hidden layer is implemented with the correlational filter operation; its output is the power spectrum of the filter output, while the other layers are the same as the ordinary multilayer Perceptron (o-MLP). By using the back-propagation learning algorithm for the correlational filter layer, f-MLP was able to self-adapt the filter coefficients to produce its output with more discrimination power for classification in the higher layer. In this research, this f-MLP was applied for sleep snoring signal detection. As a result, the f-MLP achieved an average detection rate of 96% for the test patterns, compared to the conventional multilayer neural network that demonstrates an 82% average detection rate. PMID:26405943
Schottky, B
1997-01-01
We extend our study of phase transitions in the generalization behaviour of multilayer perceptrons with non-overlapping receptive fields to the problem of the influence of noise, concerning e.g. the input units and/or the couplings between the input units and the hidden units of the second layer (='input noise'), or the final output unit (='output noise'). Without output noise, the output itself is given by a general, permutation-invariant Boolean function of the outputs of the hidden units. As a result we find that the phase transitions, which we found in the deterministic case, mostly persist in the presence of noise. The influence of the noise on the position of the phase transition, as well as on the behaviour in other regimes of the loading parameter $\\alpha$, can often be described by a simple rescaling of $\\alpha$ depending on strength and type of the noise. We then consider the problem of the optimal noise level for Gibbsian and Bayesian learning, looking on replica symmetry breaking as well. Finally ...
Directory of Open Access Journals (Sweden)
Kuzmin Alexey Konstantinovich
2011-02-01
Full Text Available The algorithm of development of full set of tests for debugging of neural network expert systems based on threelayer perceptron is considered. The algo-rithm is based on rules extraction from neural network and using of the method of technical diagnostics PODEM. The use of algorithm for testing of expert sys-tem Glaukoma Complaint for prognosis of compliance of ophthalmologic patients is described.
Leaf Recognition Algorithm Using MLP Neural Network Based Image Processing
Ekshinge Sandip Sambhaji*1,; Mr. D.B Andore2
2014-01-01
In this paper, we employ Multilayer Perceptron with image and data processing techniques and neuralIn this paper, we employ Multilayer Perceptron with image and data processing techniques and neuralIn this paper, we employ Multilayer Perceptron with image and data processing techniques and neuralnetwork to implement a general purpose automated leaf recognition. Sampling leaves and photoing them are low cost and convenient. One can easily transfer the leaf image to a computer and a computer ca...
Heremans, Stien; Suykens, Johan A. K.; Van Orshoven, Jos
2016-02-01
To be physically interpretable, sub-pixel land cover fractions or abundances should fulfill two constraints, the Abundance Non-negativity Constraint (ANC) and the Abundance Sum-to-one Constraint (ASC). This paper focuses on the effect of imposing these constraints onto the MultiLayer Perceptron (MLP) for a multi-class sub-pixel land cover classification of a time series of low resolution MODIS-images covering the northern part of Belgium. Two constraining modes were compared, (i) an in-training approach that uses 'softmax' as the transfer function in the MLP's output layer and (ii) a post-training approach that linearly rescales the outputs of the unconstrained MLP. Our results demonstrate that the pixel-level prediction accuracy is markedly increased by the explicit enforcement, both in-training and post-training, of the ANC and the ASC. For aggregations of pixels (municipalities), the constrained perceptrons perform at least as well as their unconstrained counterparts. Although the difference in performance between the in-training and post-training approach is small, we recommend the former for integrating the fractional abundance constraints into MLPs meant for sub-pixel land cover estimation, regardless of the targeted level of spatial aggregation.
Cebrian, Manuel
2007-01-01
The random initialization of weights of a multilayer perceptron makes it possible to model its training process as a Las Vegas algorithm, i.e. a randomized algorithm which stops when some required training error is obtained, and whose execution time is a random variable. This modelling is used to perform a case study on a well-known pattern recognition benchmark: the UCI Thyroid Disease Database. Empirical evidence is presented of the training time probability distribution exhibiting a heavy tail behavior, meaning a big probability mass of long executions. This fact is exploited to reduce the training time cost by applying two simple restart strategies. The first assumes full knowledge of the distribution yielding a 40% cut down in expected time with respect to the training without restarts. The second, assumes null knowledge, yielding a reduction ranging from 9% to 23%.
Multilayer neural networks a generalized net perspective
Krawczak, Maciej
2013-01-01
The primary purpose of this book is to show that a multilayer neural network can be considered as a multistage system, and then that the learning of this class of neural networks can be treated as a special sort of the optimal control problem. In this way, the optimal control problem methodology, like dynamic programming, with modifications, can yield a new class of learning algorithms for multilayer neural networks. Another purpose of this book is to show that the generalized net theory can be successfully used as a new description of multilayer neural networks. Several generalized net descriptions of neural networks functioning processes are considered, namely: the simulation process of networks, a system of neural networks and the learning algorithms developed in this book. The generalized net approach to modelling of real systems may be used successfully for the description of a variety of technological and intellectual problems, it can be used not only for representing the parallel functioning of homogen...
A multi-layer feed-forward perceptron for microwave signals processing
Rouveure, R.; Faure, P.; Monod, M.O.
2003-01-01
This paper investigates the processing of radar signals using artificial neural networks. Today, the use of FMCW radar is considered to control the agricultural implements working depth, in order to overcome the limitations of sensors based on optical or ultrasound devices towards agricultural environment (dust, rain, etc.). The objective is to determine the radar-target distance R with a direct identification of the discrete-time radar signal Sb[n]. The neural network structure in a multi-la...
Tfwala, Samkele S.; Yu-Min Wang; Yu-Chieh Lin
2013-01-01
Hydrological data are often missing due to natural disasters, improper operation, limited equipment life, and other factors, which limit hydrological analysis. Therefore, missing data recovery is an essential process in hydrology. This paper investigates the accuracy of artificial neural networks (ANN) in estimating missing flow records. The purpose is to develop and apply neural networks models to estimate missing flow records in a station when data from adjacent stations is available. Multi...
Electron/pion identification in the CBM TRD using a multilayer perceptron
International Nuclear Information System (INIS)
The problem of electron/pion identification in the CBM experiment based on the measurements of energy losses and transition radiation in the TRD detector is discussed. A possibility to solve such a problem by applying an artificial neural network (ANN) is considered. As input information for the network we used both the samples of energy losses of pions or electrons in the TRD absorbers and the 'clever' variable obtained on the basis of the original data. We show that usage of this new variable permits one to reach a reliable level of particle recognition no longer than after 10-20 training epochs; there are practically no fluctuations against the trend, and the needed level of pions suppression is obtained under the condition of a minimal loss of electrons
Benrekia, Fayçal; Attari, Mokhtar; Bouhedda, Mounir
2013-01-01
This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases. PMID:23529119
Directory of Open Access Journals (Sweden)
Mokhtar Attari
2013-03-01
Full Text Available This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases.
Evolutionary Feature Selection for Spiking Neural Network Pattern Classifiers
Valko, Michal; Cavalheiro, Nuno; Castelani, Marco
2005-01-01
This paper presents an application of the biologically realistic JASTAP neural network model to classification tasks. The JASTAP neural network model is presented as an alternative to the basic multi-layer perceptron model. An evolutionary procedure previously applied to the simultaneous solution of feature selection and neural network training on standard multi-layer perceptrons is extended with JASTAP model. Preliminary results on IRIS standard data set give evidence that this extension all...
The Normalized Radial Basis Function Neural Network and its Relation to the Perceptron
Grabec, I.
2007-01-01
The normalized radial basis function neural network emerges in the statistical modeling of natural laws that relate components of multivariate data. The modeling is based on the kernel estimator of the joint probability density function pertaining to given data. From this function a governing law is extracted by the conditional average estimator. The corresponding nonparametric regression represents a normalized radial basis function neural network and can be related with th...
Advances in Artificial Neural Networks – Methodological Development and Application
Yanbo Huang
2009-01-01
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 back...
Data assimilation: Particle filter and artificial neural networks
International Nuclear Information System (INIS)
The goal of this work is to present the performance of the Neural Network Multilayer Perceptrons trained to emulate a Particle Filter in the context of data assimilation. Techniques for data assimilation are applied for the Lorenz system, which presents a strong nonlinearity and chaotic nature. The cross validation method was used for training the network. Good results were obtained applying the multilayer perceptrons neural network.
Scientific Electronic Library Online (English)
Yuleidys, Mejías César; Ramón, Carrasco Velar; Isbel, Ochoa Izquierdo; Edel, Moreno Lemus.
2013-12-01
Full Text Available El perceptrón multicapa (PMC) figura dentro de los tipos de redes neuronales artificiales (RNA) con resultados útiles en los estudios de relación estructura-actividad. Dado que los volúmenes de datos en proyectos de Bioinformática son eventualmente grandes, se propuso evaluar algoritmos para acortar [...] el tiempo de entrenamiento de la red sin afectar su eficiencia. Se desarrolló un algoritmo para el entrenamiento local y distribuido del PMC con la posibilidad de variar las funciones de transferencias para lo cual se utilizaron el Weka y la Plataforma de Tareas Distribuidas Tarenal para distribuir el entrenamiento del perceptrón multicapa. Se demostró que en dependencia de la muestra de entrenamiento, la variación de las funciones de transferencia pueden reportar resultados mucho más eficientes que los obtenidos con la clásica función Sigmoidal, con incremento de la g-media entre el 4.5 y el 17 %. Se encontró además que en los entrenamientos distribuidos es posible alcanzar eventualmente mejores resultados que los logrados en ambiente local. Abstract in english The multilayer perceptron (PMC) ranks among the types of artificial neural networks (ANN), which has provided better results in studies of structure-activity relationship. As the data volumes in Bioinformatics' projects are eventually big, it was proposed to evaluate algorithms to shorten the traini [...] ng time of the network without affecting its efficiency. There were evaluated different tools that work with ANN and were selected Weka algorithm for extracting the network and the Platform for Distributed Task Tarenal to distribute the training of multilayer perceptron. Finally, it was developed a training algorithm for local and distributed the MLP with the possibility of varying transfer functions. It was shown that depending on the training sample, the change of transfer functions can yield results much more efficient than those obtained with the classic sigmoid function with increased g-media between 4.5 and 17 %. Moreover, it was found that with distributed training can be achieved eventually, better results than those achieved in the local environment.
Jeong, Sungmoon; Jung, Chanwoong; Kim, Cheol-Su; Shim, Jae Hoon; Lee, Minho
2011-08-01
This paper presents a new computer interface system based on laser spot detection and moving pattern analysis of the detected laser spots in real-time processing. We propose a systematic method that uses either the frame difference of successive input images or an autoassociative multilayer perceptron (AAMLP) to detect laser spots. The AAMLP is applied only to areas of the input images where the frame difference of the successive images is not effective for detecting laser spots. In order to enhance the detection performance, the AAMLP is trained by a new training algorithm that increases the sensitivity of the input-to-output mapping of the AAMLP allowing a small variation in the input feature of the laser spot image to be successfully indicated. The proposed interface system is also able to keep track of the laser spot and recognize gesture commands. The moving pattern of the laser spot is recognized by using a multilayer perception. It is experimentally shown that the proposed computer interface system is fast enough for real-time operation with reliable accuracy.
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.
Evolutionary Learning Algorithm for Multi-layer Morphological Neural Networks
He Chunmei
2013-01-01
Morphological Neural Network (MNN) is a novel and important neural network and it has many applications such as image processing and pattern recognition. It makes sense to research the learning algorithm of MNN and its application. A method based on genetic algorithm is presented to train and implement multi-layer morphological neural network in this study. The algorithm calculates the weights and biases of morphological neural network and the genetic algorithm automatically acquire the learn...
Preference of echo features for classification of seafloor sediments using neural networks
Digital Repository Service at National Institute of Oceanography (India)
De, C.; Chakraborty, B.
Selection of a set of dominant echo features to classify seafloor sediments using a multilayer perceptron neural network is investigated at two acoustic frequencies (33 and 210 kHz). Several sets of inputs with different combinations of two, three...
An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network
Amiri, S; Movahedi, M M; K Kazemi; Parsaei, H
2013-01-01
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 classification errors...
Schwindling Jerome
1995-01-01
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 par...
Fault Diagnosis of Multilevel Cascaded Inverter Using Multi Layer Perceptron Network
E. Parimalasundar; N.Suthanthira Vanitha
2015-01-01
In this study, a fault diagnostic system in a multi-level inverter using a MLP network is developed. Using a mathematical model, it is difficult to diagnose a Multilevel-Inverter Drive (MLID) system, because MLID system complexity has a non-linear factor and it consist of many switching devices. Therefore neural network classification is applied to fault diagnosis of MLID system. Multilayer perceptron networks (MLP) are used to identify the type and location of occurring faults from inverter ...
Ferreira, B D L; SebastiÃ£o, 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...
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.
Scientific Electronic Library Online (English)
Haydeé Elena, Musso; Orlando José, Ávila Blas.
2013-01-01
Full Text Available En este trabajo se realizó un estudio estadístico de variables físico químicas asociadas al fenómeno de contaminación ambiental, en particular concentración media mensual de SO2 , medidas en la ciudad Salta Capital, Argentina, simultáneamente a concentraciones de NO2 y O3 . Las series bajo estudio p [...] resentaban comportamientos dinámicos no lineales, datos atípicos y cambios estructurales, lo que hizo imposible modelarlas con tipologías econométricas tradiciones (AR, MA, ARMA, ARIMA, entre otras). Una solución eficiente que se encontró, hace uso de la teoría de los perceptrones multicapa. Mediante el modelo estructural de series de tiempo, esta solución se presenta como un proceso matemático iterativo que permite obtener un modelado final el cual tiene una muy alta confiabilidad (95%), para realizar pronoósticos a futuro sobre el comportamiento de la variable estudiada. Abstract in english In this paper a statistical study of phisical-chemistry variables connected with enviroment pollution, specifically SO2 monthly average concentration, measured in Salta Capital city, Argentina, together with NO2 and O3 concentrations, was made. Time series under study shown non linear dinamic behavi [...] our, outliers and structural changes. Due to these it was impossible to use typical econometric typologies (AR, MA, ARMA, ARIMA, among others). An effective solution which uses multistep perceptrons theory was found. By using structural time series modelling, this solution is presented by an iterative mathematical process that allows us to obtain a final model with a high confidence level (95%) in order to do the forecasting step on the studied variable.
Directory of Open Access Journals (Sweden)
Haydeé Elena Musso
2013-01-01
Full Text Available En este trabajo se realizó un estudio estadístico de variables físico químicas asociadas al fenómeno de contaminación ambiental, en particular concentración media mensual de SO2 , medidas en la ciudad Salta Capital, Argentina, simultáneamente a concentraciones de NO2 y O3 . Las series bajo estudio presentaban comportamientos dinámicos no lineales, datos atípicos y cambios estructurales, lo que hizo imposible modelarlas con tipologías econométricas tradiciones (AR, MA, ARMA, ARIMA, entre otras. Una solución eficiente que se encontró, hace uso de la teoría de los perceptrones multicapa. Mediante el modelo estructural de series de tiempo, esta solución se presenta como un proceso matemático iterativo que permite obtener un modelado final el cual tiene una muy alta confiabilidad (95%, para realizar pronoósticos a futuro sobre el comportamiento de la variable estudiada.In this paper a statistical study of phisical-chemistry variables connected with enviroment pollution, specifically SO2 monthly average concentration, measured in Salta Capital city, Argentina, together with NO2 and O3 concentrations, was made. Time series under study shown non linear dinamic behaviour, outliers and structural changes. Due to these it was impossible to use typical econometric typologies (AR, MA, ARMA, ARIMA, among others. An effective solution which uses multistep perceptrons theory was found. By using structural time series modelling, this solution is presented by an iterative mathematical process that allows us to obtain a final model with a high confidence level (95% in order to do the forecasting step on the studied variable.
Multilayer Neural Net Trajectory Tracking Control for Underwater Vehicle
KuljaÄa, Ognjen; Gadewadikar, Jyotirmay; Horvat, Krunoslav
2009-01-01
An adaptive multilayer neural network controller for high precision maneuvering of underwater vehicles is presented. Maneuvering of underwater vehicles requires special attention to a number of factors, including thruster and vehicleâ€™s nonlinearities, couplings which exist between various degrees of freedom as well as effects of the sea currents. The neuro control system for underwater vehicle maneuvering described in this paper is based on a conventional controller supported with the so-c...
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.
Schwindling, Jerome
2010-04-01
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.
Multilayer associative neural networks (MANN's): storage capacity versus perfect recall.
Kang, H
1994-01-01
The objective of this paper is to to resolve important issues in artificial neural nets-exact recall and capacity in multilayer associative memories. These problems have imposed restrictions on coding strategies. We propose the following triple-layered hybrid neural network: the first synapse is a one-shot associative memory using the modified Kohonen's adaptive learning algorithm with arbitrary input patterns; the second one is Kosko's bidirectional associative memory consisting of orthogonal input/output basis vectors such as Walsh series satisfying the strict continuity condition; and finally, the third one is a simple one-shot associative memory with arbitrary output images. A mathematical framework based on the relationship between energy local minima (capacity of the neural net) and noise-free recall is established. The robust capacity conditions of this multilayer associative neural network that lead to forming the local minima of the energy function at the exact training pairs are derived. The chosen strategy not only maximizes the total number of stored images but also completely relaxes any code-dependent conditions of the learning pairs. PMID:18267854
Scientific Electronic Library Online (English)
Carlos A., de Luna-Ortega; Miguel, Mora-González; Julio C., Martínez-Romo; Francisco J., Luna-Rosas; Jesús, Muñoz-Maciel.
Full Text Available En el presente artículo se da a conocer una alternativa algorítimica a los sistemas actuales de reconocimiento automático del habla (ASR), mediante una propuesta en la forma de realizar la caracterización de las palabras basada en una aproximación que usa la extracción de coeficientes de la codifica [...] ción de predicción lineal (LPC) y la correlación cruzada. La implementación consiste en extraer las características fonéticas mediante los coeficientes LPC, después se forman vectores de patrones de la pronunciación conformados por el promedio de los coeficientes LPC de las muestras de las palabras obteniendo un vector característico de cada pronunciación mediante la autocorrelación de las secuencias de coeficientes LPC; estos vectores se utilizan para entrenar un clasificador de tipo perceptrón multicapa (MLP). Se realizaron pruebas de desempeño previo entrenamiento con los diferentes patrones de las palabras a reconocer. Se utilizó la fonética de los dígitos del cero al nueve como vocabulario objetivo, debido a su amplia aplicación, y para estimar el desempeño de este método se utilizaron dos corpus de pronunciaciones: el corpus UPA, que contempla en su base de datos la pronuncación de la región occidente de México, y el corpus Tlatoa, que hace lo propio para la región centro de México. Las señales en ambos corpus fueron adquiridas en el lenguaje español, y a una frecuencia de muestreo de 8kHz. Los porcentajes de reconocimiento obtenidos fueron del 96.7 y 93.3% para las modalidades de mono-locutor para el corpus UPA y múltiple-locutor para el corpus Tlatoa, respectivamente. Asimismo, se realizó una comparación contra dos métodos clásicos del reconocimiento de voz y del habla, Dynamic Time Warping (DTW) y Hidden Markov Models (HMM). Abstract in english It this paper we present an algorithmic alternative to the current Automatic Speech Recognition (ASR) systems by proposing a way to characterize words based on approximations that use an extracted coefficient from Linear Predictive Coding (LPC). The method consists in extracting phonetic characteris [...] tics through the use of LPC coefficients, after which pattern vectors are formed from the LPC coefficient averages taken from the word sampling, thus creating a unique vector for each pronunciation through the auto correlation of the LPC coefficient sequences. These vectors are used to train a Multilayer Perceptron (MLP) classifier. After training performance trials were executed. The sounds from the digits zero through nine where used as a target vocabulary, given its general use, and to estimate the performance of this method two corpus where used: the UPA corpus, which in its vocabulary uses a pronunciation familiar to the western part of Mexico, and the Tlatoa corpus, who's vocabulary presents a pronunciation typical of the central region of Mexico. The signals from both corpus where sampled in the Spanish language, and at a sampling frequency of 8kHz. The recognition rate for the mono-speaker from the UPA corpus and the multiple-speaker from the Tlatoa corpus were 96.7% and 93.3% respectively. Additionally, there where comparisons done against two classic methods used for speech recognition, Dynamic Time Warping (DTW) and Hidden Markov Models (HMM).
Building a Chaotic Proved Neural Network
Bahi, Jacques M; Salomon, Michel
2011-01-01
Chaotic neural networks have received a great deal of attention these last years. In this paper we establish a precise correspondence between the so-called chaotic iterations and a particular class of artificial neural networks: global recurrent multi-layer perceptrons. We show formally that it is possible to make these iterations behave chaotically, as defined by Devaney, and thus we obtain the first neural networks proven chaotic. Several neural networks with different architectures are trained to exhibit a chaotical behavior.
Phase Transitions of Neural Networks
Kinzel, W
1997-01-01
The cooperative behaviour of interacting neurons and synapses is studied using models and methods from statistical physics. The competition between training error and entropy may lead to discontinuous properties of the neural network. This is demonstrated for a few examples: Perceptron, associative memory, learning from examples, generalization, multilayer networks, structure recognition, Bayesian estimate, on-line training, noise estimation and time series generation.
Aphasia Classification Using Neural Networks
DEFF Research Database (Denmark)
Axer, H.; Jantzen, Jan; Berks, G.; Keyserlingk, Diedrich Graf von
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 of the...
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...
Multi-Layered Neural Networks Infer Fundamental Stellar Parameters
Verma, Kuldeep; Bhattacharya, Jishnu; Antia, H M; Krishnamurthy, Ganapathy
2016-01-01
The advent of space-based observatories such as CoRoT and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typically require five input parameters, the mass, initial Helium abundance, initial metallicity, mixing-length (assumed to be constant over time) and the age to which the star must be evolved. These parameters are also very useful in characterizing the associated planets and in studying galactic archaeology. How to obtain the parameters from observations rapidly and accurately, specifically in the context of surveys of thousands of stars, is an outstanding question, one that has eluded straightforward resolution. For a given star, we typically measure the effective temperature and surface metallicity spectroscopically and low-degree oscillation frequencies through space observatories. Here we demonstrate that statistical learning, using multi-layered neural networks, is successful in determining the evolutionary parameters based on spect...
Handwritten Digit Recognition with Binary Optical Perceptron
Saxena, Indu; Moerland, Perry; Fiesler, Emile; Pourzand, A. R.
1997-01-01
Binary weights are favored in electronic and optical hardware implementations of neural networks as they lead to improved system speeds. Optical neural networks based on fast ferroelectric liquid crystal binary level devices can benefit from the many orders of magnitudes improved liquid crystal response times. An optimized learning algorithm for all-positive perceptrons is simulated on a limited data set of hand-written digits and the resultant network implemented optically. First, gray-scale...
Training trajectories by continuous recurrent multilayer networks.
Leistritz, L; Galicki, M; Witte, H; Kochs, E
2002-01-01
This paper addresses the problem of training trajectories by means of continuous recurrent neural networks whose feedforward parts are multilayer perceptrons. Such networks can approximate a general nonlinear dynamic system with arbitrary accuracy. The learning process is transformed into an optimal control framework where the weights are the controls to be determined. A training algorithm based upon a variational formulation of Pontryagin's maximum principle is proposed for such networks. Computer examples demonstrating the efficiency of the given approach are also presented. PMID:18244431
Artificial Neural Networks in Catalyst Development. Chapter 10.
Czech Academy of Sciences Publication Activity Database
Hole?a, Martin; Baerns, M.
New Jersey : John Wiley and Sons, 2003 - (Cawse, J.), s. 163-202 ISBN 0-471-20343-2 Source of funding: V - iné verejné zdroje Keywords : artificial neural networks * multilayer perceptrons * nonlinear dependency * approximation * network training * knowledge extraction Subject RIV: IN - Informatics, Computer Science
Artificial neural networks applied to forecasting time series
MontaÃ±o Moreno, Juan JosÃ©; Palmer Pol, Alfonso; MuÃ±oz Gracia, MarÃa del Pilar
2011-01-01
This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparativ...
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.
Multilayer neural-net robot controller with guaranteed tracking performance.
Lewis, F L; Yegildirek, A; Liu, K
1996-01-01
A multilayer neural-net (NN) controller for a general serial-link rigid robot arm is developed. The structure of the NN controller is derived using a filtered error/passivity approach. No off-line learning phase is needed for the proposed NN controller and the weights are easily initialized. The nonlinear nature of the NN, plus NN functional reconstruction inaccuracies and robot disturbances, mean that the standard delta rule using backpropagation tuning does not suffice for closed-loop dynamic control. Novel online weight tuning algorithms, including correction terms to the delta rule plus an added robust signal, guarantee bounded tracking errors as well as bounded NN weights. Specific bounds are determined, and the tracking error bound can be made arbitrarily small by increasing a certain feedback gain. The correction terms involve a second-order forward-propagated wave in the backpropagation network. New NN properties including the notions of a passive NN, a dissipative NN, and a robust NN are introduced. PMID:18255592
Novel LDPC Decoder via MLP Neural Networks
Karami, Alireza; Attari, Mahmoud Ahmadian
2014-01-01
In this paper, a new method for decoding Low Density Parity Check (LDPC) codes, based on Multi-Layer Perceptron (MLP) neural networks is proposed. Due to the fact that in neural networks all procedures are processed in parallel, this method can be considered as a viable alternative to Message Passing Algorithm (MPA), with high computational complexity. Our proposed algorithm runs with soft criterion and concurrently does not use probabilistic quantities to decide what the estimated codeword i...
Applying Backpropagation Neural Networks to Bankruptcy Prediction
Yi-Chung Hu; Fang-Mei Tseng
2005-01-01
Bankruptcy prediction is an important classification problem for a business, and has become a major concern of managers. In this paper, two well-known backpropagation neural network models serving as data mining tools for classification problems are employed to perform bankruptcy forecasting: one is the backpropagation multi-layer perceptron, and the other is the radial basis function network. In particular, the radial basis function network can be treated as a fuzzy neural network. Through e...
A Comparative Study of RBF and MLP Neural Model for Seven Element Dynamic Phased Array Smart Antenna
Rahul Shrivastava; Abhishek Rawat; Yogendra Kumar Jain
2013-01-01
In this paper we present the neural Modelling techniques for dynamic phased array smart antenna. Neural networks are mathematical and computation models that are used to optimize the smart antenna system, which are very much suitable for real time applications. Here we are optimizing the seven element dynamic phased array smart antenna using Radial basis function neural network (RBFNN) and Multilayer Perceptron neural network (MLPNN). The beam ship prediction of seven element DPA is done up t...
Efficient learning algorithm for quantum perceptron unitary weights
Seow, Kok-Leong; Behrman, Elizabeth; Steck, James
2015-01-01
For the past two decades, researchers have attempted to create a Quantum Neural Network (QNN) by combining the merits of quantum computing and neural computing. In order to exploit the advantages of the two prolific fields, the QNN must meet the non-trivial task of integrating the unitary dynamics of quantum computing and the dissipative dynamics of neural computing. At the core of quantum computing and neural computing lies the qubit and perceptron, respectively. We see that past implementat...
International Nuclear Information System (INIS)
A nonlinear multivariable empirical model is developed for a U-tube steam generator using the recurrent multilayer perceptron network as the underlying model structure. The recurrent multilayer perceptron is a dynamic neural network, very effective in the input-output modeling of complex process systems. A dynamic gradient descent learning algorithm is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over static learning algorithms. In developing the U-tube steam generator empirical model, the effects of actuator, process,and sensor noise on the training and testing sets are investigated. Learning and prediction both appear very effective, despite the presence of training and testing set noise, respectively. The recurrent multilayer perceptron appears to learn the deterministic part of a stochastic training set, and it predicts approximately a moving average response. Extensive model validation studies indicate that the empirical model can substantially generalize (extrapolate), though online learning becomes necessary for tracking transients significantly different than the ones included in the training set and slowly varying U-tube steam generator dynamics. In view of the satisfactory modeling accuracy and the associated short development time, neural networks based empirical models in some cases appear to provide a serious alternative to first principles models. Caution, however, must be exercised because extensive on-line validation of these models is still warranted
Neural Network Revisited: Perception on Modified Poincare Map of Financial Time Series Data
Situngkir, H; Situngkir, Hokky; Surya, Yohanes
2004-01-01
Artificial Neural Network Model for prediction of time-series data is revisited on analysis of the Indonesian stock-exchange data. We introduce the use of Multi-Layer Perceptron to percept the modified Poincare-map of the given financial time-series data. The modified Poincare-map is believed to become the pattern of the data that transforms the data in time-t versus the data in time-t+1 graphically. We built the Multi-Layer Perceptron to percept and demonstrate predicting the data on specific stock-exchange in Indonesia.
Gamma-ray energy determination using neural network algorithms for an imaging silicon calorimeter
International Nuclear Information System (INIS)
A neural network technique, based on multi-layer perceptrons, is used to fully exploit the performances of a sampling silicon calorimeter in energy identification of gamma rays. The results obtained on simulated data are significantly better than those coming from a classic method analysis. (orig.)
ESTIMATION OF INPUT IMPEDANCE OF MICROSTRIP PATCH ANTENNA USING FUZZY NEURAL NETWORK
Directory of Open Access Journals (Sweden)
VANDANA VIKAS THAKARE
2010-10-01
Full Text Available The paper presents the use of fuzzy neural network (FNN as a fast and better technique for the determination of input impedance of coaxial feed rectangular microstrip antenna. The fuzzy parameter ensures better performance as compared to three layer multilayered perceptron feed forward back propagation artificial neural network (MLPFFBP ANN and radial basis function artificial neural network (RBF ANN in the determination of input impedance of the coaxial feed microstrip antenna.
Methodological Issues in Building, Training, and Testing Artificial Neural Networks
Ozesmi, Stacy L.; Ozesmi, Uygar; Tan, Can Ozan
2005-01-01
We review the use of artificial neural networks, particularly the feedforward multilayer perceptron with back-propagation for training (MLP), in ecological modelling. Overtraining on data or giving vague references to how it was avoided is the major problem. Various methods can be used to determine when to stop training in artificial neural networks: 1) early stopping based on cross-validation, 2) stopping after a analyst defined error is reached or after the error levels of...
Classification of Magneto-Optic Images using Neural Networks
Nath, Shridhar; Wincheski, Buzz; Fulton, Jim; Namkung, Min
1994-01-01
A real time imaging system with a neural network classifier has been incorporated on a Macintosh computer in conjunction with an MOI system. This system images rivets on aircraft aluminium structures using eddy currents and magnetic imaging. Moment invariant functions from the image of a rivet is used to train a multilayer perceptron neural network to classify the rivets as good or bad (rivets with cracks).
Using Artificial Neural Networks for ECG Signals Denoising
Zoltán Germán-Salló; Katalin György
2010-01-01
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+1)th 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...
Intelligent neural network classifier for automatic testing
Bai, Baoxing; Yu, Heping
1996-10-01
This paper is concerned with an application of a multilayer feedforward neural network for the vision detection of industrial pictures, and introduces a high characteristics image processing and recognizing system which can be used for real-time testing blemishes, streaks and cracks, etc. on the inner walls of high-accuracy pipes. To take full advantage of the functions of the artificial neural network, such as the information distributed memory, large scale self-adapting parallel processing, high fault-tolerance ability, this system uses a multilayer perceptron as a regular detector to extract features of the images to be inspected and classify them.
Scientific Electronic Library Online (English)
Alejandro J., Orozco-Naranjo; Pablo A., Muñoz-Gutiérrez.
2013-12-30
Full Text Available Este artículo presenta los resultados obtenidos al desarrollar una metodología para la detección de 5 tipos de latidos cardiacos (Normal (N), Bloqueo de Rama Derecha (RBBB), Bloqueo de Rama Izquierda (LBBB), Contracción Auricular Prematura (APC) y Contracción Ventricular Prematura (PVC)) utilizando [...] la transformada por paquetes Wavelet de manera no adaptativa en la extracción de características de las señales cardiacas, empleando la función Shanon para cálculo de la entropía y adicionando una fase de identificación de nodos por cada tipo de señal cardiaca en el árbol Wavelet. La utilización de la transformada por paquetes Wavelet permite acceder a información obtenida de la descomposición tanto de baja como de alta frecuencia proporcionando un análisis más integral que el logrado con la transformada Wavelet discreta. Se evaluaron Wavelets madre de las familias Daubechies, Symlet 5 y Biortogonal inversa; que fueron resultado de una investigación previa en que se identificaron las Wavelet madre que mayor entropía presentaban con las señales cardiacas. Con la modalidad no adaptativa se reduce el costo computacional al utilizar los paquetes Wavelet, coste que representa la mayor desventaja de esta transformada, dando validez a la investigación realizada. Para la clasificación de los patrones cardiacos se emplearon las máquinas de soporte vectorial y el perceptrón multicapa. Con las máquinas de soporte vectorial empleando kernel de función de base radial, se logró un error de clasificación del 2,57 %. Abstract in english 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 n [...] on-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 %.
Forecasting Runoff with Artificial Neural Networks.
Czech Academy of Sciences Publication Activity Database
Neruda, M.; Neruda, Roman; Kudová, Petra
Paris : UNESCO, 2005 - (Maraga, F.), s. 65-69 [ERB 2004. Euromediterranean Network of Experimental and Representative Basins /10./. Turin (IT), 13.10.2004-17.10.2004] R&D Projects: GA ?R(CZ) GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : artificial neural networks * rainfall-runoff modelling * multilayer perceptron * Radial Basis Function s (RBF) Subject RIV: BA - General Mathematics
Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks
Maca, Petr; Pech, Pavel
2016-01-01
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.
A fast identification of insufficiency of nutrients using spectral features would be a useful instrument in farming and in other nutrient demanding agricultural systems such as those proposed for long period space missions. A Multilayer Perceptron (MLP) neural network and backpropagation algorithm w...
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.
A Comparison between Neural Networks and Wavelet Networks in Nonlinear System Identification
S. Ehsan Razavi; Hamed Khodadadi; Hossein Ahmadi-Noubari
2012-01-01
In this study, identification of a nonlinear function will be presented by neural network and wavelet network methods. Behavior of a nonlinear system can be identified by intelligent methods. Two groups of the most common and at the same time the most effective of neural networks methods are multilayer perceptron and radial basis function that will be used for nonlinear system identification. The selected structure is series - parallel method that after network training by a series of trainin...
Vibration Based Damage Assessment of a Civil Engineering Structures using a Neural Networks
Kirkegaard, Poul Henning; Rytter, A.
1994-01-01
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 to be an effective tool for pattern recognition, the basic idea is to train a neural network with simulated values of modal parameters in order to recognize the behaviour of the damaged as well as the un...
Directory of Open Access Journals (Sweden)
A. Piotrowski
2006-01-01
Full Text Available In this paper, Multi-Layer Perceptron and Radial-Basis Function Neural Networks, along with the Nearest Neighbour approach and linear regression are utilized for flash-flood forecasting in the mountainous Nysa Klodzka river catchment. It turned out that the Radial-Basis Function Neural Network is the best model for 3- and 6-h lead time prediction and the only reliable one for 9-h lead time forecasting for the largest flood used as a test case.
Polanco, Xavier; François, Claire; Keim, Jean-Pierre
1998-01-01
This paper describes the implementation of multivariate data analysis: NEURODOC applies the axial k-means method for automatic, non-hierarchical cluster analysis and a Principal Component Analysis (PCA) for representing the clusters on a map. We next introduce Artificial Neural Networks (ANNs) to extend NEURODOC into a neural platform for the cluster analysis and cartography of bibliographic data. The ANNs tested are: the Adaptive Resonance Theory (ART 1), a Multilayer Perceptron (MLP), and a...
Artificial Neural Network Employed To Design Annular Ring Microstrip Antenna
Directory of Open Access Journals (Sweden)
Anil Kumar
2012-04-01
Full Text Available Neural network computational modules have recently gained as an unconventional and useful tool for RF and microwave modeling and design. Neural network is trained to learn the behavior of Annular Ring Microstrip Antenna’s equivalent circuit parameters. A trained neural network is used for designing fast and less error answers to the task that has to be learned. In this paper, structure of Annular Ring Microstrip Antenna (ARMSA is studied and sets of datum are collected for the training of the Multilayer Perceptron (MLP Neural Network.
Scientific Electronic Library Online (English)
F., Dall Cortivo; E. S., Chalhoub; H. F., Campos Velho.
2012-12-01
Full Text Available Estimativa do perfil de concentração de clorofila, em águas naturais, a partir da radiação emergente na superfície de um corpo d'agua, com o uso de rede neural artificial do tipo Perceptron de Múltiplas Camadas. A concentração de clorofila está relacionada com os coeficientes de absorção e espalhame [...] nto via modelos bio-ópticos. O treinamento da rede é formulado como um problema de otimização, no qual a atualização das variáveis livres da rede (pesos, viés e parâmetros de cada função de ativação) é feita através do método quasi-Newton. Abstract in english In this work the average profile of chlorophyll concentration is estimated from the emitted radiation at the surface of natural waters. This is performed through the use an Artificial Neural Network of Multilayer Perceptron type to act as the inverse operator. Bio-optical models are used to correlat [...] e the chlorophyll concentration with the absorption and scattering coefficients. The network training is formulated as an optimization problem, in which the update of the free variables of network (weights, viéses and each slope of the activation functions) is performed through the quasi-Newton method.
AN EFFICIENT NEURAL NETWORK FOR RECOGNIZING GESTURAL HINDI DIGITS
Nidal Fawzi Shilbayeh; Mohammad Mahmmoud Alwakeel; Maisa Mohy Naser
2013-01-01
Handwritten Hindi digit recognition plays an important role in eastern Arab countries especially in the courtesy amounts of Arab bank checks, recognizing numbers in car plates, or in postal code for mail sorting. In our study, we proposed an efficient Hindi Digit Recognition System drawn by the mouse and developed using Multilayer Perceptron Neural Network (MLP) with backpropagation. The system has been designed, implemented and tested successfully. Analysis has been carried out to determine ...
Artificial Neural Network to predict mean monthly total ozone in Arosa, Switzerland
Chattopadhyay, S; Chattopadhyay, Surajit; Bandyopadhyay, Goutami
2006-01-01
Present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Networks models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with learning rate 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found skillful. But, Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.
Multilayer neural networks for reduced-rank approximation.
Diamantaras, K I; Kung, S Y
1994-01-01
This paper is developed in two parts. First, the authors formulate the solution to the general reduced-rank linear approximation problem relaxing the invertibility assumption of the input autocorrelation matrix used by previous authors. The authors' treatment unifies linear regression, Wiener filtering, full rank approximation, auto-association networks, SVD and principal component analysis (PCA) as special cases. The authors' analysis also shows that two-layer linear neural networks with reduced number of hidden units, trained with the least-squares error criterion, produce weights that correspond to the generalized singular value decomposition of the input-teacher cross-correlation matrix and the input data matrix. As a corollary the linear two-layer backpropagation model with reduced hidden layer extracts an arbitrary linear combination of the generalized singular vector components. Second, the authors investigate artificial neural network models for the solution of the related generalized eigenvalue problem. By introducing and utilizing the extended concept of deflation (originally proposed for the standard eigenvalue problem) the authors are able to find that a sequential version of linear BP can extract the exact generalized eigenvector components. The advantage of this approach is that it's easier to update the model structure by adding one more unit or pruning one or more units when the application requires it. An alternative approach for extracting the exact components is to use a set of lateral connections among the hidden units trained in such a way as to enforce orthogonality among the upper- and lower-layer weights. The authors call this the lateral orthogonalization network (LON) and show via theoretical analysis-and verify via simulation-that the network extracts the desired components. The advantage of the LON-based model is that it can be applied in a parallel fashion so that the components are extracted concurrently. Finally, the authors show the application of their results to the solution of the identification problem of systems whose excitation has a non-invertible autocorrelation matrix. Previous identification methods usually rely on the invertibility assumption of the input autocorrelation, therefore they can not be applied to this case. PMID:18267843
A Comparative Study of RBF and MLP Neural Model for Seven Element Dynamic Phased Array Smart Antenna
Directory of Open Access Journals (Sweden)
Rahul Shrivastava
2013-05-01
Full Text Available In this paper we present the neural Modelling techniques for dynamic phased array smart antenna. Neural networks are mathematical and computation models that are used to optimize the smart antenna system, which are very much suitable for real time applications. Here we are optimizing the seven element dynamic phased array smart antenna using Radial basis function neural network (RBFNN and Multilayer Perceptron neural network (MLPNN. The beam ship prediction of seven element DPA is done up to 60 deg scan angle and results of RBF and MLP are compared to find out the better neural network approach for smart antenna optimization.
Eduardo O. Cerqueira; JoÃ£o C. de Andrade; Ronei J. Poppi; Cesar Mello
2001-01-01
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 analytic...
Sankalia, Mayur G.; Mashru, Rajshree C.; Sankalia, Jolly M.; Sutariya, Vijay B.
2005-01-01
This work examines the influence of various process parameters (like sodium alginate concentration, calcium chloride concentration, and hardening time) on papain entrapped in ionotropically cross-linked alginate beads for stability improvement and site-specific delivery to the small intestine using neural network modeling. A 33 full-factorial design and feed-forward neural network with multilayer perceptron was used to investigate the effect of process variables on percentage of entrapment, t...
Dutot, A; Steiner, F; Rude, J
2008-01-01
A neural network combined to a neural classifier is used in a real time forecasting of hourly maximum ozone in the centre of France, in an urban atmosphere. This neural model is based on the MultiLayer Perceptron (MLP) structure. The inputs of the statistical network are model output statistics of the weather predictions from the French National Weather Service. With this neural classifier, the Success Index of forecasting is 78% whereas it is from 65% to 72% with the classical MLPs. During the validation phase, in the Summer of 2003, six ozone peaks above the threshold were detected. They actually were seven.
Directory of Open Access Journals (Sweden)
Onursal Ã‡etin
2015-06-01
Full Text Available Objective: Implementation of multilayer neural network (MLNN with sigmoid activation function for the diagnosis of hepatitis disease. Methods: Artificial neural networks (ANNs are efficient tools currently in common use for medical diagnosis. In hardware based architectures activation functions play an important role in ANN behavior. Sigmoid function is the most frequently used activation function because of its smooth response. Thus, sigmoid function and its close approximations were implemented as activation function. The dataset is taken from the UCI machine learning database. Results: For the diagnosis of hepatitis disease, MLNN structure was implemented and Levenberg Morquardt (LM algorithm was used for learning. Our method of classifying hepatitis disease produced an accuracy of 91.9% to 93.8% via 10 fold cross validation. Conclusion: When compared to previous work that diagnosed hepatitis disease using artificial neural networks and the identical data set, our results are promising in order to reduce the size and cost of neural network based hardware. Thus, hardware based diagnosis systems can be developed effectively by using approximations of sigmoid function.
International Nuclear Information System (INIS)
In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of two layers: discrete wavelet layer and multi-layer perceptron. The discrete wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of Discrete Wavelet Transform (DWT) and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the used system is evaluated by using noisy Turkish word signals. Test results showing the effectiveness of the proposed automatic system are presented in this paper. The rate of correct recognition is about 92.5% for the sample speech signals. (author)
Scientific Electronic Library Online (English)
Eduardo O. de, Cerqueira; João C. de, Andrade; Ronei J., Poppi; Cesar, Mello.
2001-12-01
Full Text Available [...] Abstract in english 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. Th [...] e 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.
Design and FPGA-implementation of multilayer neural networks with on-chip learning
International Nuclear Information System (INIS)
Artificial Neural Networks (ANN) is used in many applications in the industry because of their parallel structure, high speed, and their ability to give easy solution to complicated problems. For example identifying the orange and apple in the sorting machine with neural network is easier than using image processing techniques to do the same thing. There are different software for designing, training, and testing the ANN, but in order to use the ANN in the industry, it should be implemented on hardware outside the computer. Neural networks are artificial systems inspired on the brain's cognitive behavior, which can learn tasks with some degree of complexity, such as signal processing, diagnosis, robotics, image processing, and pattern recognition. Many applications demand a high computing power and the traditional software implementation are not sufficient.This thesis presents design and FPGA implementation of Multilayer Neural Networks with On-chip learning in re-configurable hardware. Hardware implementation of neural network algorithm is very interesting due their high performance and they can easily be made parallel. The architecture proposed herein takes advantage of distinct data paths for the forward and backward propagation stages and a pipelined adaptation of the on- line backpropagation algorithm to significantly improve the performance of the learning phase. The architecture is easily scalable and able to cope with arbitrary network sizes with the same hardware. The implementation is targeted diagnosis of the Research Reactor accidents to avoid the risk of occurrence of a nuclear accident. The proposed designed circuits are implemented using Xilinx FPGA Chip XC40150xv and occupied 73% of Chip CLBs. It achieved 10.8 ?s to take decision in the forward propagation compared with current software implemented of RPS which take 24 ms. The results show that the proposed architecture leads to significant speed up comparing to high end software solutions. On-chip learning allows on line reconstruction of ANN. Re-configure ability and parallel structure of FPGA makes it possible to accomplish this task.
MÃ³nica Bocco; Enrique Willington; MÃ³nica Arias
2010-01-01
The incident solar radiation on soil is an important variable used in agricultural applications; it is also relevant in hydrology, meteorology and soil physics, among others. To estimate this variable, empirical models have been developed using several parameters and, recently, prognostic and prediction models based on artificial intelligence techniques such as neural networks. The aim of this work was to develop linear models and neural networks, multilayer perceptron, to estimate daily glob...
Thomas, Philippe; Thomas, André
2008-01-01
Simulation is often used to evaluate the relevance of a Directing Program of Production (PDP) or to evaluate its impact on detailed sc\\'enarii of scheduling. Within this framework, we propose to reduce the complexity of a model of simulation by exploiting a multilayer perceptron. A main phase of the modeling of one system using a multilayer perceptron remains the determination of the structure of the network. We propose to compare and use various pruning algorithms in order ...
Ali Abroudi; Mohammad Shokouhifar; Fardad Farokhi
2013-01-01
This paper presents a hybrid approach with two phases for improving the performance of training artificial neural networks (ANNs) by selection of the most important instances for training, and then reduction the dimensionality of features. The ANNs which are applied in this paper for validation, are included Multi-Layer Perceptron (MLP) and Neuro-Fuzzy Network (NFN). In the first phase, the Modified Fast Condensed Nearest Neighbor (MFCNN) algorithm is used to construct the subset with instanc...
Neural network modeling and correcting for delay-line data sets
International Nuclear Information System (INIS)
Because of the effects of the capacitance and inductance parasitized on the readout PCB in GEM detector, the output time of the delay-line PCB puts up a non-linear relationship with the position of its input signal. Based on Back Propagation algorithm, the multi-layer perceptrons neural network approximated the non-linear function and gave out accurate analyses, which is a better method for data correcting in Delay-Line readout. (authors)
Hossein Naderi; Mojtaba Moradpour; Mehdi Zangeneh; Farzad Khani
2012-01-01
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...
Neural networks for modelling and control of a non-linear dynamic system
Murray-Smith, R; Neumerkel, D.; Sbarbaro-Hofer, D.
1992-01-01
The authors describe the use of neural nets to model and control a nonlinear second-order electromechanical model of a drive system with varying time constants and saturation effects. A model predictive control structure is used. This is compared with a proportional-integral (PI) controller with regard to performance and robustness against disturbances. Two feedforward network types, the multilayer perceptron and radial-basis-function nets, are used to model the system. The problems involved ...
How deals with discrete data for the reduction of simulation models using neural network
Thomas, Philippe; Thomas, André
2009-01-01
Simulation is useful for the evaluation of a Master Production/distribution Schedule (MPS). Also, the goal of this paper is the study of the design of a simulation model by reducing its complexity. According to theory of constraints, we want to build reduced models composed exclusively by bottlenecks and a neural network. Particularly a multilayer perceptron, is used. The structure of the network is determined by using a pruning procedure. This work focuses on the impact of ...
Viscosity Calculation at Moderate Pressure for Nonpolar Gases via Neural Network
Bouzidi, A.; S. Hanini; F. Souahi; B. Mohammedi; M. Touiza
2007-01-01
A new method, based on Artificial Neural Networks (ANN) of Multi-Layer Perceptron (MLP) type, has been developed to estimate the viscosity at moderate pressure for pure nonpolar gases over a wide range of temperatures. An ANN was trained, using four physicochemical properties: Molecular weight (M), boiling point (Tb), critical Temperature (Tc) and critical Pressure (Pc) combined with absolute Temperature (T) as its inputs, to correlate and predict viscosity. A group of 52 nonpolar gases were ...
Dong, Zhekang; Duan, Shukai; Hu, Xiaofang; Wang, Lidan; Li, Hai
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
Vibration Based Damage Assessment of a Civil Engineering Structures using a Neural Networks
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Rytter, A.
an effective tool for pattern recognition, the basic idea is to train a neural network with simulated values of modal parameters in order to recognize the behaviour of the damaged as well as the undamaged structure. Subjecting this trained neural network to measured modal parameters should imply......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 to be...
Automatic Analysis of Radio Meteor Events Using Neural Networks
Roman, Victor ?tefan; Buiu, C?t?lin
2015-07-01
Meteor Scanning Algorithms (MESCAL) is a software application for automatic meteor detection from radio recordings, which uses self-organizing maps and feedforward multi-layered perceptrons. This paper aims to present the theoretical concepts behind this application and the main features of MESCAL, showcasing how radio recordings are handled, prepared for analysis, and used to train the aforementioned neural networks. The neural networks trained using MESCAL allow for valuable detection results, such as high correct detection rates and low false-positive rates, and at the same time offer new possibilities for improving the results.
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...
A design philosophy for multi-layer neural networks with applications to robot control
Vadiee, Nader; Jamshidi, MO
1989-01-01
A system is proposed which receives input information from many sensors that may have diverse scaling, dimension, and data representations. The proposed system tolerates sensory information with faults. The proposed self-adaptive processing technique has great promise in integrating the techniques of artificial intelligence and neural networks in an attempt to build a more intelligent computing environment. The proposed architecture can provide a detailed decision tree based on the input information, information stored in a long-term memory, and the adapted rule-based knowledge. A mathematical model for analysis will be obtained to validate the cited hypotheses. An extensive software program will be developed to simulate a typical example of pattern recognition problem. It is shown that the proposed model displays attention, expectation, spatio-temporal, and predictory behavior which are specific to the human brain. The anticipated results of this research project are: (1) creation of a new dynamic neural network structure, and (2) applications to and comparison with conventional multi-layer neural network structures. The anticipated benefits from this research are vast. The model can be used in a neuro-computer architecture as a building block which can perform complicated, nonlinear, time-varying mapping from a multitude of input excitory classes to an output or decision environment. It can be used for coordinating different sensory inputs and past experience of a dynamic system and actuating signals. The commercial applications of this project can be the creation of a special-purpose neuro-computer hardware which can be used in spatio-temporal pattern recognitions in such areas as air defense systems, e.g., target tracking, and recognition. Potential robotics-related applications are trajectory planning, inverse dynamics computations, hierarchical control, task-oriented control, and collision avoidance.
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.
A Novel Technique to Image Annotation using Neural Network
Directory of Open Access Journals (Sweden)
Pankaj Savita
2013-03-01
Full Text Available : Automatic annotation of digital pictures is a key technology for managing and retrieving images from large image collection. Traditional image semantics extraction and representation schemes were commonly divided into two categories, namely visual features and text annotations. However, visual feature scheme are difficult to extract and are often semantically inconsistent. On the other hand, the image semantics can be well represented by text annotations. It is also easier to retrieve images according to their annotations. Traditional image annotation techniques are time-consuming and requiring lots of human effort. In this paper we propose Neural Network based a novel approach to the problem of image annotation. These approaches are applied to the Image data set. Our main work is focused on the image annotation by using multilayer perceptron, which exhibits a clear-cut idea on application of multilayer perceptron with special features. MLP Algorithm helps us to discover the concealed relations between image data and annotation data, and annotate image according to such relations. By using this algorithm we can save more memory space, and in case of web applications, transferring of images and download should be fast. This paper reviews 50 image annotation systems using supervised machine learning Techniques to annotate images for image retrieval. Results obtained show that the multi layer perceptron Neural Network classifier outperforms conventional DST Technique.
Limitations of One-Hidden-Layer Perceptron Networks.
Czech Academy of Sciences Publication Activity Database
K?rková, V?ra
Aachen & Charleston : Technical University & CreateSpace Independent Publishing Platform, 2015 - (Yaghob, J.), s. 167-171 ISBN 978-1515120650. ISSN 1613-0073. - (CEUR Workshop Proceedings. V-1422). [ITAT 2015. Conference on Theory and Practice of Information Technologies /15./. Slovenský Raj (SK), 17.09.2015-21.09.2015] R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : perceptron networks * model complexity * representations of finite mappings by neural networks Subject RIV: IN - Informatics, Computer Science
Using Artificial Neural Networks for ECG Signals Denoising
Directory of Open Access Journals (Sweden)
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.
Tomas Ayala-Silva; Beyl, Caula A.; Heath, Robert R.
2006-01-01
A fast identification of insufficiency of nutrients using spectral features would be a useful instrument in farming and in other nutrient demanding agricultural systems such as those proposed for long period space missions. A Multilayer Perceptron (MLP) neural network and backpropagation algorithm was used to differentiate between normal leaves of wheat (Triticum aestivum L.) and those deficient in nitrogen, phosphorus, (K) and (Ca) using hyperspectral data. The network consisted of three lay...
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. PMID:25594981
Directory of Open Access Journals (Sweden)
Manjula Devi Ramasamy
2014-01-01
Full Text Available Multilayer Feed Forward Neural Network (MFNN has been successfully administered architectures for solving a wide range of supervised pattern recognition tasks. The most problematic task of MFNN is training phase which consumes very long training time on very huge training datasets. An enhanced linear adaptive skipping training algorithm for MFNN called Half of Threshold (HOT is proposed in this research paper. The core idea of this study is to reduce the training time through random presentation of training input samples without affecting the network’s accuracy. The random presentation is done by partitioning the training dataset into two distinct classes, classified and misclassified class, based on the comparison result of the calculated error measure with half of threshold value. Only the input samples in the misclassified class are presented to the next epoch for training, whereas the correctly classified class is skipped linearly which dynamically reducing the number of input samples exhibited at every single epoch without affecting the network’s accuracy. Thus decreasing the size of the training dataset linearly can reduce the total training time, thereby speeding up the training process. This HOT algorithm can be implemented with any training algorithm used for supervised pattern classification and its implementation is very simple and easy. Simulation study results proved that HOT training algorithm achieves faster training than the other standard training algorithm.
Comparative study of different wavelet based neural network models for rainfall-runoff modeling
Shoaib, Muhammad; Shamseldin, Asaad Y.; Melville, Bruce W.
2014-07-01
The use of wavelet transformation in rainfall-runoff modeling has become popular because of its ability to simultaneously deal with both the spectral and the temporal information contained within time series data. The selection of an appropriate wavelet function plays a crucial role for successful implementation of the wavelet based rainfall-runoff artificial neural network models as it can lead to further enhancement in the model performance. The present study is therefore conducted to evaluate the effects of 23 mother wavelet functions on the performance of the hybrid wavelet based artificial neural network rainfall-runoff models. The hybrid Multilayer Perceptron Neural Network (MLPNN) and the Radial Basis Function Neural Network (RBFNN) models are developed in this study using both the continuous wavelet and the discrete wavelet transformation types. The performances of the 92 developed wavelet based neural network models with all the 23 mother wavelet functions are compared with the neural network models developed without wavelet transformations. It is found that among all the models tested, the discrete wavelet transform multilayer perceptron neural network (DWTMLPNN) and the discrete wavelet transform radial basis function (DWTRBFNN) models at decomposition level nine with the db8 wavelet function has the best performance. The result also shows that the pre-processing of input rainfall data by the wavelet transformation can significantly increases performance of the MLPNN and the RBFNN rainfall-runoff models.
Approximation of Functions by Perceptron Networks with Bounded Number of Hidden Units.
Czech Academy of Sciences Publication Activity Database
K?rková, V?ra
1995-01-01
Ro?. 8, ?. 5 (1995), s. 745-750. ISSN 0893-6080 R&D Projects: GA ?R GA201/93/0427; GA AV ?R IA23057 Keywords : approximation of function s * one-hidden-layer neural network * heaviside perceptrons * radial - basis - function units * bounded number of hidden units Impact factor: 1.262, year: 1995
Extraction of Logical Rules from Data by Means of Piecewise-Linear Neural Networks.
Czech Academy of Sciences Publication Activity Database
Hole?a, Martin
Berlin : Springer, 2002 - (Lange, S.; Satoh, K.; Smith, C.), s. 193-205 ISBN 3-540-00188-3. ISSN 0302-9743. - (Lecture Notes in Computer Science.. 2534). [International Conference on Algorithm ic Learning Theory /13./, International Conference on Discovery Science /5./. Lübeck (DE), 24.11.2002-26.11.2002] R&D Projects: GA ?R GA201/00/1489; GA AV ?R IAB2030007 Institutional research plan: AV0Z1030915 Keywords : data mining * knowledge discovery * artificial neural networks * multilayer perceptrons * rule extraction * piecewise-linear neural networks Subject RIV: BA - General Mathematics
Using neural networks for prediction of nuclear parameters
International Nuclear Information System (INIS)
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)
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)
Empirical modeling of nuclear power plants using neural networks
International Nuclear Information System (INIS)
A summary of a procedure for nonlinear identification of process dynamics encountered in nuclear power plant components is presented in this paper using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the nonlinear structure for system identification. In the overall identification process, the feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of time-dependent system nonlinearities. The standard backpropagation learning algorithm is modified and is used to train the proposed hybrid network in a supervised manner. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The nonlinear response of a representative steam generator is predicted using a neural network and is compared to the response obtained from a sophisticated physical model during both high- and low-power operation. The transient responses compare well, though further research is warranted for training and testing of recurrent neural networks during more severe operational transients and accident scenarios
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.
Dynamic neural controllers for induction motor.
Brdy?, M A; Kulawski, G J
1999-01-01
The paper reports application of recently developed adaptive control techniques based on neural networks to the induction motor control. This case study represents one of the more difficult control problems due to the complex, nonlinear, and time-varying dynamics of the motor and unavailability of full-state measurements. A partial solution is first presented based on a single input-single output (SISO) algorithm employing static multilayer perceptron (MLP) networks. A novel technique is subsequently described which is based on a recurrent neural network employed as a dynamical model of the plant. Recent stability results for this algorithm are reported. The technique is applied to multiinput-multioutput (MIMO) control of the motor. A simulation study of both methods is presented. It is argued that appropriately structured recurrent neural networks can provide conveniently parameterized dynamic models for many nonlinear systems for use in adaptive control. PMID:18252531
Aphasia Classification Using Neural Networks
DEFF Research Database (Denmark)
Axer, H.; Jantzen, Jan
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 of the Aachen Aphasia Test (AAT). First a coarse classification was achieved by using an assessment of spontaneous speech of the patient. This classifier produced correct results in 87% of the test cases. For a second test, data analysis tools were used to select four features out of the 30 available test features to yield a more accurate diagnosis. This second classifier produced correct results in 92% of the test cases. This test requires four AAT scores as input for the multilayer perceptron. In practice, the second test requires hours of work on behalf of the clinician, whereas the first test can be done in about half an hour in a free interview. The results of the classifiers were analyzed regarding their accuracy dependent on the diagnosis.
Advances in Artificial Neural Networks – Methodological Development and Application
Directory of Open Access Journals (Sweden)
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 engineering.
Intelligent Handwritten Digit Recognition using Artificial Neural Network
Directory of Open Access Journals (Sweden)
Saeed AL-Mansoori
2015-05-01
Full Text Available The aim of this paper is to implement a Multilayer Perceptron (MLP Neural Network to recognize and predict handwritten digits from 0 to 9. A dataset of 5000 samples were obtained from MNIST. The dataset was trained using gradient descent back-propagation algorithm and further tested using the feed-forward algorithm. The system performance is observed by varying the number of hidden units and the number of iterations. The performance was thereafter compared to obtain the network with the optimal parameters. The proposed system predicts the handwritten digits with an overall accuracy of 99.32%.
Non-linear survival analysis using neural networks
Ripley, RM; Harris, AL; Tarassenko, L.
2004-01-01
We describe models for survival analysis which are based on a multi-layer perceptron, a type of neural network. These relax the assumptions of the traditional regression models, while including them as particular cases. They allow non-linear predictors to be fitted implicitly and the effect of the covariates to vary over time. The flexibility is included in the model only when it is beneficial, as judged by cross-validation. Such models can be used to guide a search for extra regressors, by c...
Terrain Mapping and Classification in Outdoor Environments Using Neural Networks
Directory of Open Access Journals (Sweden)
Alberto Yukinobu Hata
2009-12-01
Full Text Available This paper describes a three-dimensional terrain mapping and classification technique to allow the operation of mobile robots in outdoor environments using laser range finders. We propose the use of a multi-layer perceptron neural network to classify the terrain into navigable, partially navigable, and non-navigable. The maps generated by our approach can be used for path planning, navigation, and local obstacle avoidance. Experimental tests using an outdoor robot and a laser sensor demonstrate the accuracy of the presented methods.
Prediction of total resistance coefficients using neural networks
Ortigosa Barragán, Inma; Revilla López, Guillermo; García Espinosa, Julio
2009-01-01
The Holtrop & Mennen method is widely used at the initial design stage of ships for estimating the resistance of the ship (Holtrop and Mennen, 1982). The Holtrop & Mennen method provide a prediction of the total resistance’s components. In this work we present a neural network model which performs the same task as the Holtrop & Mennem’s method, for two of the total resistance’s components. A multilayer perceptron has been therefore trained to learn the relationship between the input (length-d...
AN FUZZY NEURAL APPROACH FOR MEDICAL IMAGE RETRIEVAL
Directory of Open Access Journals (Sweden)
C. Sriramakrishnan
2012-01-01
Full Text Available Image retrieval based on a query image is necessary for effective and efficient use the information that is stored in medical image databases. Medical Image Retrieval is difficult as not only the localization and directionality of human visual system is to be considered but also the pathological condition. Image identification and segmentation for feature extraction pose a challenge to image retrieval process. Challenges posed include large number of images to be processed for the image retrieval and identifying the region of interest automatically to optimize the search. In this study, we propose a novel image segmentation algorithm Fuzzy Edge Detection and Segmentation (FEDS. The proposed FEDS algorithm is tested on medical images and for classification of images, a bell fuzzy multilayer perceptron is proposed. The proposed neural network Bell Fuzzy Multi Layer Perceptron (BF-MLP Neural network is constructed by introducing a fuzzy logic in hidden layer with the sugeno model and bell function. The proposed neural network consists of two layers with the first layer being a tanh activation function and the second layer containing the bell fuzzy activation function. The proposed FEDS method was implemented using Matlab and Modelsim. A total of 44 images were considered with three class labels. The edge obtained for which segmentation is done using the proposed segmentation algorithm. The proposed BF-MLP neural network algorithm was implemented using Visual Studio and the classification accuracy compared with MLP Neural Network with sigmoid activation function. In this study, a fuzzy segmentation algorithm and a fuzzy classification algorithm is proposed to improve the medical image retrieval accuracy. The proposed segmentation algorithm, Fuzzy Edge Detection and Segmentation (FEDS, was implemented using Matlab and features were extracted using Fast Hartley Transform (FHT. The extracted features were used to train the proposed neural network, Bell Fuzzy Multi Layer Perceptron Neural Network (BF-MLP. 44 images with 3 class labels were used to test the algorithm and classification accuracy of 93.2% was obtained.
A combinatorial approach to understanding perceptron capabilities.
Gibson, G J
1993-01-01
This work investigates the classification capabilities of perceptrons which incorporate a single hidden layer of nodes from a theoretical viewpoint. In particular, the question of determining whether a given set can be realized as the decision region of such a network is considered. The main theoretic result demonstrates that the realizability of a set can be determined by restricting attention to any neighborhood of its boundary. This result is then used to identify general classes of realizable sets, and an example is given which shows that even though the realizability of a set might be readily discerned, the construction of an appropriate perceptron architecture may be complicated. PMID:18276529
Energy Technology Data Exchange (ETDEWEB)
Furini, M.A.; Araujo, P.B. de; Pereira, A.L.S. [Universidade Estadual Paulista (FEIS/UNESP), Ilha Solteira, SP (Brazil). Fac. de Engenharia. Dept. Engenharia Eletrica], Emails: mafurini@aluno.feis.unesp.br, percival@dee.feis.unesp.br, andspa@gmail.com
2009-07-01
This paper aims at analyzing the main operation and design of operationally robust controllers in order to damp the electromechanics oscillations type inter area. For this we used an intelligent control technique based on artificial neural networks, where a multilayer perceptron it was implemented. We used a symmetrical test system of four generators, ten bars and nine transmission lines to verify the performance of the power system stabilizers and power oscillation damping (POD) for the FACTS devices, unified power flow controller (UPFC), designed for neural networks. The results show the superiority in the operation and control of oscillations in power systems using UPFC equipped with the POD.
International Nuclear Information System (INIS)
This paper shows that the artificial neural networks techniques could be used in PWR power plant, specially to automatically perform the control rods calibration periodic test and to predict the evolutions of the axial power distribution. In the first case we use an ordinary multilayer perceptron (MLP) and in the second case we use a time delay neural network (TDNN). In these two cases, the objectives are fulfilled (tests on a power plant model). On these basis we propose some perspectives of development; for example: the realization of a real time mock-up of the first application for tests in operational conditions. (author)
Best approximation by Heaviside perceptron networks.
Kainen, P; K?rková, V; Vogt, A
2000-09-01
In Lp-spaces with p an integer from [1, infinity) there exists a best approximation mapping to the set of functions computable by Heaviside perceptron networks with n hidden units; however for p an integer from (1, infinity) such best approximation is not unique and cannot be continuous. PMID:11152201
Neural Networks for Non-linear Control
DEFF Research Database (Denmark)
Sørensen, O.
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....
A Novel Channel Equalizer Using Large Margin Algebraic Perceptron Network
Directory of Open Access Journals (Sweden)
Siba P. Panigrahi
2010-08-01
Full Text Available This paper proposes a novel control scheme for channel equalization for wireless communication system. The proposed scheme considers channel equalization as a classification problem. For efficient solution of the problem, this paper makes use of a neural network working on Algebraic Perceptron (AP algorithm as a classifier. Also, this paper introduces a method of performance improvement by increasing margin of AP equalizers. Novelty of the proposed scheme is evidenced by its simulation results.
Radial basis function neural network for power system load-flow
International Nuclear Information System (INIS)
This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)
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.
STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
Mayra Luiza Marques da Silva Binoti
2015-03-01
Full Text Available The objective of this study was to train, implement and evaluate the efficiency of artificial neural networks (ANN to perform production prognosis of even-aged stands of eucalyptus clones. The data used were from plantations located in southern Bahia, totaling about 2,000 acres of forest. Numeric variables, such as age, basal area, volume and categorical variables, such as soil class texture, spacing, land relief, project and clone were used. The data were randomly divided into two groups: training (80% and generalization (20%. Three types of networks were trained: perceptron, multilayer perceptron networks and radial basis function. The RNA that showed the best performance in training and generalization were selected to perform the prognosis with data from the first forest inventory. We conclude that the RNA had satisfactory results, showing the potential and applicability of the technique in solving measurement and forest management problems.
Neural networks: a biased overview
International Nuclear Information System (INIS)
An overview of recent activity in the field of neural networks is presented. The long-range aim of this research is to understand how the brain works. First some of the problems are stated and terminology defined; then an attempt is made to explain why physicists are drawn to the field, and their main potential contribution. In particular, in recent years some interesting models have been introduced by physicists. A small subset of these models is described, with particular emphasis on those that are analytically soluble. Finally a brief review of the history and recent developments of single- and multilayer perceptrons is given, bringing the situation up to date regarding the central immediate problem of the field: search for a learning algorithm that has an associated convergence theorem
Neural networks for predicting breeding values and genetic gains
Scientific Electronic Library Online (English)
Gabi Nunes, Silva; Rafael Simões, Tomaz; Isabela de Castro, Sant' Anna; Moysés, Nascimento; Leonardo Lopes, Bhering; Cosme Damião, Cruz.
2014-12-01
Full Text Available Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for train [...] ing the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.
Determination of osteoporosis risk using by neural networks method
Directory of Open Access Journals (Sweden)
Veysi Akpolat
2009-06-01
Full Text Available Artificial neural networks (ANNs have become modeling tools that have found extensive acceptance and they have frequently used in applications in many disciplines for solving complex problems. Different ANN structures are valuable models, which are used in the medical field for the development of decision support systems. In this paper, the learning and classification processes are used for determining the level of bone-density (safe / risk of osteoporosis in woman. In this study, three different structured neural networks were used for classifying of osteoporosis and the most efficient structure was determined. The training network structures were Multilayer perceptron neural network (MLP, Linear Vector Quantization (LVQ and Self Organizing Map (SOM. Performance indicators and statistical measures were used for evaluating the structures and the results demonstrated that the MLP was the most efficient structure for classifying of osteoporosis.
Landscape statistics of the binary perceptron
Fontanari, J. F.; Köberle, R.
1990-01-01
The landscape of the binary perceptron is studied by Simulated Annealing, exhaustive search and performing random walks on the landscape. We find that the number of local minima increases exponentially with the number of bonds, becoming deeper in the vicinity of a global minimum, but more and more shallow as we move away from it. The random walker detects a simple dependence on the size of the mapping, the architecture introducing a nontrivial dependence on the number of steps.
Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia
El-Shafie, A.; Noureldin, A.; Taha, M.; Hussain, A.; Mukhlisin, M.
2012-04-01
Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBFNN) and input delay neural network (IDNN), respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997-2008) on a weekly basis and 22 yr (1987-2008) on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.
Neural networks in front-end processing and control
International Nuclear Information System (INIS)
Research into neural networks has gained a large following in recent years. In spite of the long term timescale of this Artificial Intelligence research, the tools which the community is developing can already find useful applications to real practical problems in experimental research. One of the main advantages of the parallel algorithms being developed in AI is the structural simplicity of the required hardware implementation, and the simple nature of the calculations involved. This makes these techniques ideal for problems in which both speed and data volume reduction are important, the case for most front-end processing tasks. In this paper we illustrate the use of a particular neural network known as the Multi-Layer Perceptron as a method for solving several different tasks, all drawn from the field of Tokamak research. We also briefly discuss the use of the Multi-Layer Perceptron as a non-linear controller in a feedback loop. We outline the type of problem which can be usefully addressed by these techniques, even before the large-scale parallel processing hardware currently under development becomes cheaply available. We also present some of the difficulties encountered in applying these networks. (author) 13 figs., 9 refs
How to Improve the Generalization Ability of Multi-layer Neural Networks.
Czech Academy of Sciences Publication Activity Database
Šebesta, Václav
Vol. 6. Orlando : IIIS, 2002 - (Callaos, N.; Pisarchik, A.; Ueda, M.), s. 108-113 ISBN 980-07-8150-1. [ISAS SCI 2002. World Multiconference on Systemics, Cybernetics and Informatics /6./. Orlando (US), 14.07.2002-18.07.2002] R&D Projects: GA AV ?R IAA2030801; GA ?R GA102/02/0124 Institutional research plan: AV0Z1030915 Keywords : neural networks topology * neural networks learning * generalization ability * prediction * classification * data mining Subject RIV: BA - General Mathematics
A neural method for determining electromagnetic shower positions in laterally segmented calorimeters
International Nuclear Information System (INIS)
A method based on a neural network technique is proposed to calculate the coordinates of an incident photon striking a laterally segmented calorimeter and depositing shower energies in different segments. The technique uses a multilayer perceptron trained by back-propagation implemented through standard gradient descent followed by conjugate gradient algorithms and has been demonstrated with GEANT simulations of a BAF2 detector array. The position resolution results obtained by using this method are found to be substantially better than the first moment method with logarithmic weighting. (orig.)
International Nuclear Information System (INIS)
The artificial neural network technique was used to identify drugs and plastic explosives, from a tomography composed by a set of six neutrongraphic projections obtained in real time. Bidimensional tomographic images of samples of drugs, explosives and other materials, when digitally processed, yield the characteristic spectra of each type of material. The information contained in those spectra was then used for ANN training, the best images being obtained when the multilayer perceptron model, the back-propagation training algorithm and the Cross-validation interruption criterion were used. ANN showed to be useful in forecasting presence of drugs and explosives hitting a rate of success above 97 %. (author)
Energy Technology Data Exchange (ETDEWEB)
Ohlsson, M.B.O.; Roegnvaldsson, T.S.; Peterson, C.O.; Pi, H.; Soederberg, B.P.W. [Lund Univ. (Sweden). Dept. of Theoretical Physics
1994-12-31
A feed-forward artificial neural network (ANN) procedure has been devised for predicting utility loads; the resulting predictions are presented for two test problems given by ``The Great Energy Predictor Shootout-The First Building Data Analysis and Prediction Competition`` (Kreider and Haberl 1994). Key ingredients in this approach are the multilayer perceptron and a method ({delta}-test) for determining relevant inputs. These methods are briefly reviewed, together with comments on alternative schemes such as fitting to polynomials and the use of recurrent networks.
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
Unfortunately, the Cosmic Microwave Background (CMB) radiation is contaminated by emission originating in the Milky Way (synchrotron, free-free and dust emission). Since the cosmological information is statistically in nature, it is essential to remove this foreground emission and leave the CMB...... 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...
Using Neural and Fuzzy Software for the Classification of ECG Signals
Saad Alshaban
2010-01-01
Two approaches to classify the ECG biomedical signals are presented in this work. One is theArtificial Neural Network (ANN) with multilayer perceptron and the other is the Fuzzy Logic with FuzzyKnowledge Base Controller (FKBC). Backpropagation Learning Algorithm (BPA) has been used at preset totrain the ANN. MATLAB version 6.5 program was used. The ECG signals were classified to eleven groups,one of them is for the normal cases and the others represent ten different diseases. These ECG record...
Fast non-linear extraction of plasma equilibrium parameters using a neural network mapping
International Nuclear Information System (INIS)
The shaping of non-circular plasmas requires a non-linear mapping between the measured diagnostic signals and selected equilibrium parameters. The particular configuration of Neural Network known as the multi-layer perceptron provides a powerful and general technique for formulating an arbitrary continuous non-linear multi-dimensional mapping. This technique has been successfully applied to the extraction of equilibrium parameters from measurements of single-null diverted plasmas in the DIII-D tokamak; the results are compared with a purely linear mapping. The method is promising, and hardware implementation is straightforward. (author) 15 refs., 7 figs
Artificial Neural Network Solutions of Slab-Geometry Neutron Diffusion Problems
International Nuclear Information System (INIS)
Artificial neural network (ANN) methods have been researched extensively within the nuclear community for applications in systems control, diagnostics, and signal processing. We consider here the use of multilayer perceptron ANNs as an alternative to finite-difference and finite-element methods for obtaining solutions to neutron diffusion problems. This work is based on a method proposed by van Milligen et. al. to obtain solutions of the differential equations arising in plasma physics applications. This ANN method has the potential advantage of yielding an accurate, differentiable approximation to the solution of diffusion problems at all points in the spatial domain
Pattern recognition in high energy physics with artificial neural networks - JETNET 2.0
International Nuclear Information System (INIS)
A F77 package of adaptive artificial neural network algorithms, JETNET 2.0, is presented. Its primary target is the high energy physics community, but it is general enough to be used in any pattern-recognition application area. The basic ingredients are the multilayer perceptron back-propagation algorithm and the topological self-organizing map. The package consists of a set of subroutines, which can either be used with standard options or be easily modified to host alternative architectures and procedures. (orig.)
Noise reduction technique for images using radial basis function neural networks
International Nuclear Information System (INIS)
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. (author)
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.
A Novel Approach to Speech Recognition by Using Generalized Regression Neural Networks
Directory of Open Access Journals (Sweden)
Lakshmi Kanaka Venkateswarlu Revada
2011-03-01
Full Text Available Speech recognition has been a subject of active research in the last few decades. In this paper, the applicability of a special model of Generalized Regression Neural Networks as a classifier is studied. A Generalized Regression Neural Network (GRNN is often used for function approximation. It has a radial basis layer and a special linear layer. This network uses a competitive function for computing final result. The proposed network has been tested on one digit numbers dataset and produced significantly lower recognition error rate in comparison with common pattern classifiers. All of classifiers use Linear Predictive Cepstral Coefficients and Mel - Frequency Cepstral Coefficients. Results for proposed network shows that LPCC features yield better performance when compared to MFCC. It is found that the performance of Generalized Regression Neural Networks is superior to the other classifiers namely Linear and Multilayer Perceptron Neural Networks.
A Study on Modeling of MIMO Channel by Using Different Neural Network Structures
Directory of Open Access Journals (Sweden)
N. Prabhakar
2012-11-01
Full Text Available Recognition of Radio Channel (channelParameters is one of Main Challenges in SignalTransformation, and has important role in cognitive radioapproach. Goal of this paper is “Channel modeling” to estimatecoefficients of transmission functions affected on data beingtransformed in the channel. We use Multilayer perceptron(MLPNeural Network with Back-propagation learning algorithm,block-structured Neural Network with Least Squares(LSmethod(cost function and a multilayer neural network withmultiple back-propagation(MBP learning algorithm for errorestimation. These networks will be trained with received signalsto be compatible with channel, then give us an estimation of thesecoefficients. Simulation will show that this MBP method is betterthan the other two method in error estimation. It has goodperformance and also consume less execution time. Then, we willuse this network for estimating coefficients of non-lineartransmission functions of actual radio channel.
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.
Lee, I-Chi; Wu, Yu-Chieh
2014-08-27
The key factors affecting the success of neural engineering using neural stem/progenitor cells (NSPCs) are the neuron quantity, the guidance of neurite outgrowth, and the induction of neurons to form functional synapses at synaptic junctions. Herein, a biomimetic material comprising a supported lipid bilayer (SLB) with adsorbed sequential polyelectrolyte multilayer (PEM) films was fabricated to induce NSPCs to form functional neurons without the need for serum and growth factors in a short-term culture. SLBs are suitable artificial substrates for neural engineering due to their structural similarity to synaptic membranes. In addition, PEM film adsorption provides protection for the SLB as well as the ability to vary the surface properties to evaluate the effects of physical and mechanical signals on NSPC differentiation. Our results revealed that NSPCs were inducible on SLB-PEM films consisting of up to eight alternating layers. In addition, the process outgrowth length, the percentage of differentiated neurons, and the synaptic function were regulated by the number of layers and the surface charge of the outermost layer. The average process outgrowth length was greater than 500 ?m on SLB-PLL/PLGA (n = 7.5) after only 3 days of culture. Moreover, the quantity and quality of the differentiated neurons were obviously enhanced on the SLB-PEM system compared with those on the PEM-only substrates. These results suggest that the PEM films can induce NSPC adhesion and differentiation and that an SLB base may enhance neuron differentiation and trigger the formation of functional synapses. PMID:25111699
Intelligent control of HVAC systems. Part II: perceptron performance analysis
Directory of Open Access Journals (Sweden)
Ioan URSU
2013-09-01
Full Text Available This is the second part of a paper on intelligent type control of Heating, Ventilating, and Air-Conditioning (HVAC systems. The whole study proposes a unified approach in the design of intelligent control for such systems, to ensure high energy efficiency and air quality improving. In the first part of the study it is considered as benchmark system a single thermal space HVAC system, for which it is assigned a mathematical model of the controlled system and a mathematical model(algorithm of intelligent control synthesis. The conception of the intelligent control is of switching type, between a simple neural network, a perceptron, which aims to decrease (optimize a cost index,and a fuzzy logic component, having supervisory antisaturating role for neuro-control. Based on numerical simulations, this Part II focuses on the analysis of system operation in the presence only ofthe neural control component. Working of the entire neuro-fuzzy system will be reported in a third part of the study.
Time Series Data Mining in Rainfall Forecasting Using Artificial Neural Network
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Prince Gupta, S.K.Pandey
2014-01-01
Full Text Available Rainfall is very important parameter in hydrological model. Many techniques and models have been developed for rainfall time series prediction. In this study an artificial neural network (ANN based model was developed for rainfall time series forecasting. Proposed model used Multilayer perceptron (MLP network with back propagation algorithm for training. Discharge and rainfall data are took as input parameter for ANN model to predict rainfall time series. Data preprocessing and model’s sensitivity analysis were executed. Collected data is divided in three sets for optimal neural network training. The first set is the training set, used for calculate the gradient and updating the network weights and biases. The second set is the validation set. The error on the validation set is follow during the training process. The third set is test set. It is used to compare different models. Different topologies of Neural Networks were created with change in hidden layer, number of processing element and activation function. (MAE, Mean Squared error (MSE and correlation coefficient (CC are used to evaluate the model performance. On the basis of these evaluation parameter results, it is found that multilayer perceptron (MLP network predict more accurate than other traditional models.
Stochastic resonance in an intracellular genetic perceptron
Bates, Russell; Blyuss, Oleg; Zaikin, Alexey
2014-03-01
Intracellular genetic networks are more intelligent than was first assumed due to their ability to learn. One of the manifestations of this intelligence is the ability to learn associations of two stimuli within gene-regulating circuitry: Hebbian-type learning within the cellular life. However, gene expression is an intrinsically noisy process; hence, we investigate the effect of intrinsic and extrinsic noise on this kind of intracellular intelligence. We report a stochastic resonance in an intracellular associative genetic perceptron, a noise-induced phenomenon, which manifests itself in noise-induced increase of response in efficiency after the learning event under the conditions of optimal stochasticity.
Representations of Boolean Functions by Perceptron Networks.
Czech Academy of Sciences Publication Activity Database
K?rková, V?ra
Prague : Institute of Computer Science AS CR, 2014 - (K?rková, V.; Bajer, L.; Peška, L.; Vojtáš, R.; Hole?a, M.; Nehéz, M.), s. 68-70 ISBN 978-80-87136-19-5. [ITAT 2014. European Conference on Information Technologies - Applications and Theory /14./. Demänovská dolina (SK), 25.09.2014-29.09.2014] R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : perceptron networks * model complexity * Boolean functions Subject RIV: IN - Informatics, Computer Science
Practical Application of Neural Networks in State Space Control
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon
1999-01-01
In the present thesis we address some problems in discrete-time state space control of nonlinear dynamical systems and attempt to solve them using generic nonlinear models based on artificial neural networks. The main aim of the work is to examine how well such control algorithms perform when applied to a realistic process. The thesis therefore strives to provide a thorough treatment of two classes of neural network-based controllers, and to make a rigorous comparison between them and a classical linear controller. Thus, the thesis starts out with a short review of some relevant system 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 the networks, although some modifications are needed for the method to apply to the multilayer perceptron network. In connection with the multilayer perceptron networks it is also pointed out how instantaneous, sample-by-sample linearized state space models can be extracted from a trained network, thus opening up for application of linear theory at each sample instant. The case study addressed in this work is an attemporator for a high-temperature steam circuit situated in a Danish powerplant, I/S Vestkraft unit 3. The attemporator is fitted with a nonlinear and nonconstant valve, so nonlinear and adaptive control is desired to control the steam temperature tightly. A second-order nonlinear model of the attemporator based on system identification with a multilayer perceptron network is found from data collected from the actual process. It is shown to be a highly satisfying prediction and simulation model of the process. With this model in place, we turn to the control concepts. A pole placement controller based on the sample-by-sample linearizations extracted from a multilayer perceptron state observer is first derived, and it is shown how to make the control concept adaptive by continuing the training online. 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 to be stable under certain assumptions, and we address the question of how many basis functions are necessary. It is shown that basis functions with compact supports, whose supports are not entered by a system trajectory, do not need parameter updates. Therefore, a system with bounded trajectories can be controlled by a finite-dimensional model. We also introduce a modification to the algorithm which-if an upper bound on the nonlinearity growth is known-enables us to remove a sector-boundedness assumption on the nonlinearity. Finally the control concepts are applied to the nonlinear simulation model discussed above, and it is seen that the neural network -based control concepts outperform a classical linear controller.
Training a multilayer neural network for the Euro-dollar (EUR/ USD exchange rate
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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.
International Nuclear Information System (INIS)
In this paper, the OTTANNO version of four -quadrant CMOS analog multiplier circuit for artificial neural networks multi layer perception operation will be proposed. The proposed multiplier can be divided into two or three parts, which will be in the input, synapse and neuron. The percentage of silicon area saving is 95% with respect to that multiplier presented in (Chible,1997). A comparison between OTANNO and OTANPS is also presented. (author)
International Nuclear Information System (INIS)
Multilayered microfluidic devices with a micronozzle array structure have been developed to prepare unique hydrogel microfibers with highly complex cross-sectional morphologies. Hydrogel precursor solutions with different compositions are introduced through vertical micronozzles, united and focused, and continuously gelled to form hydrogel fibers with multiple regions of different physicochemical composition. We prepared alginate hydrogel microfibers with diameters of 60 ? 130 ?m and 4/8 parallel regions in the periphery. Neuron-like PC12 cells encapsulated in the parallel region, which was made of a soft hydrogel matrix, proliferated and formed linear intercellular networks along the fiber length because of the physical restrictions imposed by the relatively rigid regions. After cultivation for 14 days, one-millimeter-long intercellular networks that structurally mimic complex nerve bundles found in vivo were formed. The proposed fibers should be useful for producing various in vivo linear tissues and should be applicable to regenerative medicine and physiological studies of cells. (papers)
Optimal Capacity of the Blume-Emery-Griffiths perceptron
Bolle, D.; Castillo, I. Perez; Shim, G. M.
2002-01-01
A Blume-Emery-Griffiths perceptron model is introduced and its optimal capacity is calculated within the replica-symmetric Gardner approach, as a function of the pattern activity and the imbedding stability parameter. The stability of the replica-symmetric approximation is studied via the analogue of the Almeida-Thouless line. A comparison is made with other three-state perceptrons.
Practical Application of Neural Networks in State Space Control
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon
In the present thesis we address some problems in discrete-time state space control of nonlinear dynamical systems and attempt to solve them using generic nonlinear models based on artificial neural networks. The main aim of the work is to examine how well such control algorithms perform when...... applied to a realistic process. The thesis therefore strives to provide a thorough treatment of two classes of neural network-based controllers, and to make a rigorous comparison between them and a classical linear controller. Thus, the thesis starts out with a short review of some relevant system...... control is desired to control the steam temperature tightly. A second-order nonlinear model of the attemporator based on system identification with a multilayer perceptron network is found from data collected from the actual process. It is shown to be a highly satisfying prediction and simulation model of...
Multi-Party Security System using Artificial Neural Networks
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Urvashi Rahul Saxena
2012-09-01
Full Text Available Multi-Party Security System is an improvised version of various security systems available using Artificial Neural Networks (ANN’s as an Intelligent Agent for Intrusion Detection. This Paper focuses how inputs can be preserved to serve as a measure for securing communication protocol between two parties using privacy protocols at the hidden layer of Multi-layer Perceptron model. Various neural network structures are observed for evaluating the optimal network considering the number of hidden layers. Results depict that the generated system is capable of classifying records with about 90% of accuracy when two hidden layers are engulfed and the accuracy reduces to 87% with one hidden layer under observation.
Implementation of multi-layer feed forward neural network on PIC16F877 microcontroller
International Nuclear Information System (INIS)
Artificial Neural Network (ANN) is an electronic model based on the neural structure of the brain. Similar to human brain, ANN consists of interconnected simple processing units or neurons that process input to generate output signals. ANN operation is divided into 2 categories; training mode and service mode. This project aims to implement ANN on PIC micro-controller that enable on-chip or stand alone training and service mode. The input can varies from sensors or switches, while the output can be used to control valves, motors, light source and a lot more. As partial development of the project, this paper reports the current status and results of the implemented ANN. The hardware fraction of this project incorporates Microchip PIC16F877A microcontrollers along with uM-FPU math co-processor. uM-FPU is a 32-bit floating point co-processor utilized to execute complex calculation requires by the sigmoid activation function for neuron. ANN algorithm is converted to software program written in assembly language. The implemented ANN structure is three layer with one hidden layer, and five neurons with two hidden neurons. To prove the operability and functionality, the network is trained to solve three common logic gate operations; AND, OR, and XOR. This paper concludes that the ANN had been successfully implemented on PIC16F877a and uM-FPU math co-processor hardware that works accordingly on both training and service mode. (Author)
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N. Lauzon
2006-01-01
Full Text Available This work addresses the issue of better considering the heterogeneity of precipitation fields within lumped rainfall-runoff models where only areal mean precipitation is usually used as an input. A method using a Kohonen neural network is proposed for the clustering of precipitation fields. The evaluation and improvement of the performance of a lumped rainfall-runoff model for one-day ahead predictions is then established based on this clustering. Multilayer perceptron neural networks are employed as lumped rainfall-runoff models. The Bas-en-Basset watershed in France, which is equipped with 23 rain gauges with data for a 21-year period, is employed as the application case. The results demonstrate the relevance of the proposed clustering method, which produces groups of precipitation fields that are in agreement with the global climatological features affecting the region, as well as with the topographic constraints of the watershed (i.e., orography. The strengths and weaknesses of the rainfall-runoff models are highlighted by the analysis of their performance vis-Ã -vis the clustering of precipitation fields. The results also show the capability of multilayer perceptron neural networks to account for the heterogeneity of precipitation, even when built as lumped rainfall-runoff models.
Directory of Open Access Journals (Sweden)
N. Lauzon
2006-02-01
Full Text Available This work addresses the issue of better considering the heterogeneity of precipitation fields within lumped rainfall-runoff models where only areal mean precipitation is usually used as an input. A method using a Kohonen neural network is proposed for the classification of precipitation fields. The evaluation and improvement of the performance of a lumped rainfall-runoff model for one-day ahead predictions is then established based on this classification. Multilayer perceptron neural networks are employed as lumped rainfall-runoff models. The Bas-en-Basset watershed in France, which is equipped with 23 rain gauges with data for a 21-year period, is employed as the application case. The results demonstrate the relevance of the proposed classification method, which produces groups of precipitation fields that are in agreement with the global climatological features affecting the region, as well as with the topographic constraints of the watershed (i.e., orography. The strengths and weaknesses of the rainfall-runoff models are highlighted by the analysis of their performance vis-Ã -vis the classification of precipitation fields. The results also show the capability of multilayer perceptron neural networks to account for the heterogeneity of precipitation, even when built as lumped rainfall-runoff models.
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Therasa Chandrasekar
2015-10-01
Full Text Available This paper provides an exposition about application of neural networks in the context of research to find out the contribution of individual job satisfiers towards work commitment. The purpose of the current study is to build a predictive model to estimate the normalized importance of individual job satisfiers towards work commitment of employees working in TVS Group, an Indian automobile company. The study is based on the tool developed by Spector (1985 and Sue Hayday (2003.The input variable of the study consists of nine independent individual job satisfiers which includes Pay, Promotion, Supervision, Benefits, Rewards, Operating procedures, Co-workers, Work-itself and Communication of Spector (1985 and dependent variable as work commitment of Sue Hayday (2003.The primary data has been collected using a closed-ended questionnaire based on simple random sampling approach. This study employed the multilayer Perceptron neural network model to envisage the level of job satisfiers towards work commitment. The result from the multilayer Perceptron neural network model displayed with four hidden layer with correct classification rate of 70% and 30% for training and testing data set. The normalized importance shows high value for coworkers, superior satisfaction and communication and which acts as most significant attributes of job satisfiers that predicts the overall work commitment of employees.
Effect of direction on wind speed estimation in complex terrain using neural networks
Energy Technology Data Exchange (ETDEWEB)
Lopez, P.; Velo, R.; Maseda, F. [Department of Agroforestry Engineering, Higher Polytechnic School. University of Santiago de Compostela, Campus Universitario, s/n, 27002, Lugo (Spain)
2008-10-15
A method of estimating the annual average wind speed at a selected site using neural networks is presented. The method proposed uses only a few measurements taken at the selected site in a short time period and data collected at nearby fixed stations. The neural network used in this study is a multilayer perceptron with one hidden layer of 15 neurons, trained by the Bayesian regularization algorithm. The number of inputs that must be used in the neural network was analyzed in detail, and results suggest that only wind speed and direction data for a single station are required. In sites of complex terrain, direction is a very important input that can cause a decrease of 23% in root mean square (RMS). The results obtained by simulating the annual average wind speed at the selected site based on data from nearby stations are satisfactory, with errors below 2%. (author)
An Approach to Neural Network Based Pattern Classifier for Printed Bengali Characters
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sabyasachi samanta
2011-04-01
Full Text Available In this paper, we have designed a Neural Network based pattern classifier for recognizing Bengali printed characters. Here view-based approach is used for extracting features from individual characters and a neural network based classifier is built to analyze the performance of the view-based approach in various experimental setups. Different Bengali character samples have been taken and whole image of individual character is considered for view based analysis. The characteristic points are extracted from the characters using left-right view based approach. These points are then used to form a feature vector which represents the given character. Multi-Layer Perceptrons Neural network has been used and it was trained by back propagation algorithm to create this recognition engine. Internal shape of each character has been considered to generate the feature vector for individual images.
Application of Neural Networks for unfolding neutron spectra measured by means of Bonner Spheres
International Nuclear Information System (INIS)
A Neural Network structure has been used for unfolding neutron spectra measured by means of a Bonner Sphere Spectrometer set. The present work used the 'Stuttgart Neural Network Simulator' as the interface for designing, training and validation of a 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 10 MeV. Two types of neutron spectra were numerically investigated: monoenergetic and continuous. Good results were obtained, indicating that the Neural Network can be considered an interesting alternative among the neutron spectrum unfolding methodologies
Learning from correlated patterns by simple perceptrons
International Nuclear Information System (INIS)
Learning behavior of simple perceptrons is analyzed for a teacher-student scenario in which output labels are provided by a teacher network for a set of possibly correlated input patterns, and such that the teacher and student networks are of the same type. Our main concern is the effect of statistical correlations among the input patterns on learning performance. For this purpose, we extend to the teacher-student scenario a methodology for analyzing randomly labeled patterns recently developed in Shinzato and Kabashima 2008 J. Phys. A: Math. Theor. 41 324013. This methodology is used for analyzing situations in which orthogonality of the input patterns is enhanced in order to optimize the learning performance
Classification of drug-induced behaviors using a multi-layer feed-forward neural network.
Gonzalez, L P; Arnaldo, C M
1993-07-01
Measurement of laboratory animal motor behavior is an important part of many studies of experimental manipulations of the central nervous system. Current automated data collection and analysis systems are limited in the number of behaviors that can be monitored and quantified simultaneously. This paper describes a signal analysis technique that when used to analyze the data from a modified Stoelting electronic activity monitor is capable of classifying multiple behavior categories automatically. In this technique, the output signal from the motility monitor is fixed-length segmented and feature extraction is performed, calculating the Fourier transform and power spectrum of each data segment. An error back-propagation neural network, implemented on a microcomputer, is used to perform behavior classification of the segment power spectra. The technique provides a high degree of accuracy in automatic behavior classification and should prove useful in the quantitative assessment of behavior. PMID:8243074
PREDICTION OF BOD AND COD OF A REFINERY WASTEWATER USING MULTILAYER ARTIFICIAL NEURAL NETWORKS
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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 Kjeldahlâ€™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.
International Nuclear Information System (INIS)
In multilayered Ti0.4Al0.6N/Mo coatings, a strengthening effect can be obtained by using alternate layers of materials with high and low elastic constants. This behaviour requires a multilayer periodicity below a certain value in order to reduce dislocation motion across layer interface. Below this critical period, in most cases the hardness decreases as the period decreases. The multiple interfaces have an important role on this behaviour, working as stress relaxation areas and preventing crack propagation, influencing the mechanical properties of the system. Understanding the origin of these effects requires knowledge of the interface structure, where the interfacial roughness is of prime importance. We used Rutherford backscattering to study roughness in a quantitative way, and developed an artificial neural network algorithm dedicated to the analysis of the data. The results compare very well with previous TEM and AFM data
Offline analysis of HEP events by ''dynamic perceptron'' neural network
International Nuclear Information System (INIS)
In this paper we start from a critical analysis of the fundamental problems of the parallel calculus in linear structures and of their extension to the partial solutions obtained with non-linear architectures. Then, we present shortly a new dynamic architecture able to solve the limitations of the previous architectures through an automatic re-definition of the topology. This architecture is applied to real-time recognition of particle tracks in high-energy accelerators. (orig.)
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Mustafa Y?ld?z
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.
Recursive least-squares learning algorithms for neural networks
Lewis, Paul S.; Hwang, Jenq N.
1990-11-01
This paper presents the development of a pair of recursive least squares (ItLS) algorithms for online training of multilayer perceptrons which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is 0(N2) where N is the number of network parameters. This is due to the estimation of the N x N inverse Hessian matrix. Less computationally intensive approximations of the ilLS algorithms can be easily derived by using only block diagonal elements of this matrix thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6 1 BACKGROUND Artificial neural networks (ANNs) offer an interesting and potentially useful paradigm for signal processing and pattern recognition. The majority of ANN applications employ the feed-forward multilayer perceptron (MLP) network architecture in which network parameters are " trained" by a supervised learning algorithm employing the generalized delta rule (GDIt) [1 2]. The GDR algorithm approximates a fixed step steepest descent algorithm using derivatives computed by error backpropagatiori. The GDII algorithm is sometimes referred to as the backpropagation algorithm. However in this paper we will use the term backpropagation to refer only to the process of computing error derivatives. While multilayer perceptrons provide a very powerful nonlinear modeling capability GDR training can be very slow and inefficient. In linear adaptive filtering the analog of the GDR algorithm is the leastmean- squares (LMS) algorithm. Steepest descent-based algorithms such as GDR or LMS are first order because they use only first derivative or gradient information about the training error to be minimized. To speed up the training process second order algorithms may be employed that take advantage of second derivative or Hessian matrix information. Second order information can be incorporated into MLP training in different ways. In many applications especially in the area of pattern recognition the training set is finite. In these cases block learning can be applied using standard nonlinear optimization techniques [3 4 5].
Aliouane, Leila; Ouadfeul, Sid-Ali; Boudella, Amar
2015-04-01
The main goal of the proposed idea is to use the artificial intelligence such as the neural network and fuzzy logic to predict the pore pressure in shale gas reservoirs. Pore pressure is a very important parameter that will be used or estimation of effective stress. This last is used to resolve well-bore stability problems, failure plan identification from Mohr-Coulomb circle and sweet spots identification. Many models have been proposed to estimate the pore pressure from well-logs data; we can cite for example the equivalent depth model, the horizontal model for undercompaction called the Eaton's modelâ€¦etc. All these models require a continuous measurement of the slowness of the primary wave, some thing that is not easy during well-logs data acquisition in shale gas formtions. Here, we suggest the use the fuzzy logic and the multilayer perceptron neural network to predict the pore pressure in two horizontal wells drilled in the lower Barnett shale formation. The first horizontal well is used for the training of the fuzzy set and the multilayer perecptron, the input is the natural gamma ray, the neutron porosity, the slowness of the compression and shear wave, however the desired output is the estimated pore pressure using Eaton's model. Data of another horizontal well are used for generalization. Obtained results clearly show the power of the fuzzy logic system than the multilayer perceptron neural network machine to predict the pore pressure in shale gas reservoirs. Keywords: artificial intelligence, fuzzy logic, pore pressure, multilayer perecptron, Barnett shale.
Nonlinear control structures based on embedded neural system models.
Lightbody, G; Irwin, G W
1997-01-01
This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper. PMID:18255659
Energy Technology Data Exchange (ETDEWEB)
ZareNezhad, B.; Aminian, A. [Semnan University, Semnan (Iran)
2010-05-15
Acidic combustion gases can cause rapid corrosion when they condense on pollution control or energy recovery equipments. Since the potential of sulfuric acid condensation from flue gases is of considerable economic significance, a multi-layer feed forward artificial neural network has been presented for accurate prediction of the flue gas sulfuric acid dew points to mitigate the corrosion problems in process and power plants. According to the network's training, validation and testing results, a three layer neural network with four neurons in the hidden layer is selected as the best architecture for accurate prediction of sulfuric acid dew points. The presented model is very accurate and reliable for predicting the acid dew points over wide ranges of sulfur trioxide and water vapor concentrations. Comparison of the suggested neural network model with the most important existing correlations shows that the proposed neuromorphic model outperforms the other alternatives both in accuracy and generality. The predicted flue gas sulfuric acid dew points are in excellent agreement with experimental data suggesting the accuracy of the proposed neural network model for predicting the sulfuric acid condensation in stacks, pollution control devices, economizers and flue gas recovery systems in process industries.
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)
Vassiliadis, V S
2006-01-01
A novel algorithm for tunable compression to within the precision of reproduction targets, or storage, is proposed. The new algorithm is termed the `Perceptron Algorithm', which utilises simple existing concepts in a novel way, has multiple immediate commercial application aspects as well as it opens up a multitude of fronts in computational science and technology. The aims of this paper are to present the concepts underlying the algorithm, observations by its application to some example cases, and the identification of a multitude of potential areas of applications such as: image compression by orders of magnitude, signal compression including sound as well, image analysis in a multilayered detailed analysis, pattern recognition and matching and rapid database searching (e.g. face recognition), motion analysis, biomedical applications e.g. in MRI and CAT scan image analysis and compression, as well as hints on the link of these ideas to the way how biological memory might work leading to new points of view i...
Fuzzy Artmap and Neural Network Approach to Online Processing of Inputs with Missing Values
Nelwamondo, Fulufhelo Vincent
2007-01-01
An ensemble based approach for dealing with missing data, without predicting or imputing the missing values is proposed. This technique is suitable for online operations of neural networks and as a result, is used for online condition monitoring. The proposed technique is tested in both classification and regression problems. An ensemble of Fuzzy-ARTMAPs is used for classification whereas an ensemble of multi-layer perceptrons is used for the regression problem. Results obtained using this ensemble-based technique are compared to those obtained using a combination of auto-associative neural networks and genetic algorithms and findings show that this method can perform up to 9% better in regression problems. Another advantage of the proposed technique is that it eliminates the need for finding the best estimate of the data, and hence, saves time.
Radar Signal Detection In Non-Gaussian Noise Using RBF Neural Network
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D. G. Khairnar
2008-01-01
Full Text Available In this paper, we suggest a neural network signal detector using radial basis function (RBF network. We employ this RBF Neural detector to detect the presence or absence of a known signal corrupted by different Gaussian, non-Gaussian and impulsive noise components. In case of non-Gaussian noise, experimental results show that RBF network signal detector has significant improvement in performance characteristics. Detection capability is better than to those obtained with multilayer perceptrons (BP and optimum matched filter (MF detector. This signal detector is also tested on the simulated signals impacted by impulsive noise produced by atmospheric events and short lived echoes from meteor trains. Tested Results show, improved detection capability to impulsive noise compare to BP signal detector. It also show better performance as a function of signal-tonoise ratio compared to BP and MF.
Artificial neural network for modeling the extraction of aromatic hydrocarbons from lube oil cuts
Energy Technology Data Exchange (ETDEWEB)
Mehrkesh, A.H.; Hajimirzaee, S. [Islamic Azad University, Majlesi Branch, Isfahan (Iran, Islamic Republic of); Hatamipour, M.S.; Tavakoli, T. [Department of Chemical Engineering, University of Isfahan, Isfahan (Iran, Islamic Republic of)
2011-03-15
An artificial neural network (ANN) approach was used to obtain a simulation model to predict the rotating disc contactor (RDC) performance during the extraction of aromatic hydrocarbons from lube oil cuts, to produce a lubricating base oil using furfural as solvent. The field data used for training the ANN model was obtained from a lubricating oil production company. The input parameters of the ANN model were the volumetric flow rates of feed and solvent, the temperatures of feed and solvent, and the disc rotation rate. The output parameters were the volumetric flow rate of the raffinate phase and the extraction yield. In this study, a feed-forward multi-layer perceptron neural network was successfully used to demonstrate the complex relationship between the mentioned input and output parameters. (Copyright copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)
Neural network model for a reactor subsystem using real time data
International Nuclear Information System (INIS)
Modern nuclear power plant is a very complex arrangement of machinery consisting of huge number of control and support systems. In real time it is possible to implement intelligent systems in the form of neural network, data mining, expert system etc. for modeling the power plant. This paper describes the development of an artificial neural network model for intermediate heat exchanger subsystem of fast breeder test reactor. Multilayer perceptron network using back propagation algorithm is implemented for training the safety critical, safety related real time data. It takes in to account the weight correction method. The results indicate a very good convergence of the algorithm. The model can be used as an operator support system for predictive measures of various parameters of the reactor subsystems. (author)
Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network
Efendioglu, Hasan S.; Yildirim, Tulay; Fidanboylu, Kemal
2009-01-01
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. PMID:22399991
Neural Network on Photodegradation of Octylphenol using Natural and Artificial UV Radiation
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Lorentz JÃ„NTSCHI
2011-09-01
Full Text Available The present paper comes up with an experimental design meant to point out the factors interferingin octylphenolâ€™s degradation in surface waters under solar radiation, underlining each factorâ€™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 neuralnetworkâ€™s case has almost double value versus the regression analysis.
Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)
International Nuclear Information System (INIS)
Investigations of failures of soil masses are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. Geotechnical engineers have to pay particular attention to geology, ground water, and shear strength of soils in assessing slope stability. Artificial neural networks (ANNs) are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are nonlinear. In this research, with respect to the above advantages, ANN systems consisting of multilayer perceptron networks are developed to predict slope stability in a specified location, based on the available site investigation data from Noabad, Mazandaran, Iran. Several important parameters, including total stress, effective stress, angle of slope, coefficient of cohesion, internal friction angle, and horizontal coefficient of earthquake, were used as the input parameters, while the slope stability was the output parameter. The results are compared with the classical methods of limit equilibrium to check the ANN model's validity. (author)
A Comparison between Neural Networks and Wavelet Networks in Nonlinear System Identification
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S. Ehsan Razavi
2012-01-01
Full Text Available In this study, identification of a nonlinear function will be presented by neural network and wavelet network methods. Behavior of a nonlinear system can be identified by intelligent methods. Two groups of the most common and at the same time the most effective of neural networks methods are multilayer perceptron and radial basis function that will be used for nonlinear system identification. The selected structure is series - parallel method that after network training by a series of training random data, the output is estimated and the nonlinear function is compared to a sinusoidal input. Then, wavelet network is used for identification and we will use Orthogonal Least Squares (OLS method for wavelet selection to reduce the volume of calculations and increase the convergence speed.
Neural network controller for Active Demand-Side Management with PV energy in the residential sector
International Nuclear Information System (INIS)
Highlights: ? We have developed a neural controller for Active Demand-Side Management. ? The controller consists of Multilayer Perceptrons evolved with a genetic algorithm. ? The architecture of the controller is distributed and modular. ? The simulations show that the electrical local behavior improves. ? Active Demand-Side Management helps users to control his energy behaviour. -- Abstract: In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation.
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.
Hybrid Learning Algorithm in Neural Network System for Enzyme Classification
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Mohd Haniff Osman
2010-07-01
Full Text Available Nucleic acid and protein sequences store a wealth of informationwhich ultimately determines their functions and characteristics.Protein sequences classification deals with the assignment ofsequences to known categories based on homology detectionproperties. In this paper, we developed a hybrid learning algorithm inneural network system called Neural Network Enzyme Classification(NNEC to classify an enzyme found in Protein Data Bank (PDB to agiven family of enzymes. NNEC was developed based on MultilayerPerceptron with hybrid learning algorithm combining the geneticalgorithm (GA and Backpropagation (BP, where one of them acts asan operator in the other. Here, BP is used as a mutation-like-operatorof the general GA search template. The proposed hybrid model wastested with different topologies of network architecture, especially indetermining the number of hidden nodes. The precision results arequite promising in classifying the enzyme accordingly.
Handwritten Farsi Character Recognition using Artificial Neural Network
Ahangar, Reza Gharoie
2009-01-01
Neural Networks are being used for character recognition from last many years but most of the work was confined to English character recognition. Till date, a very little work has been reported for Handwritten Farsi Character recognition. In this paper, we have made an attempt to recognize handwritten Farsi characters by using a multilayer perceptron with one hidden layer. The error backpropagation algorithm has been used to train the MLP network. In addition, an analysis has been carried out to determine the number of hidden nodes to achieve high performance of backpropagation network in the recognition of handwritten Farsi characters. The system has been trained using several different forms of handwriting provided by both male and female participants of different age groups. Finally, this rigorous training results an automatic HCR system using MLP network. In this work, the experiments were carried out on two hundred fifty samples of five writers. The results showed that the MLP networks trained by the err...
Neural Network Aided Glitch-Burst Discrimination and Glitch Classification
Rampone, Salvatore; Troiano, Luigi; Pinto, Innocenzo M
2014-01-01
We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts (glitches), in the data of a single detector. The further classification of glitches into typical sets is explored.In order to provide a proof of concept,we use the core-collapse supernova waveform catalog produced by H. Dimmelmeier and co-Workers, and the data base of glitches observed in laser interferometer gravitational wave observatory (LIGO) data maintained by P. Saulson and co-Workers to construct datasets of (windowed) transient waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian) noise with different signal-tonoise ratios (SNR). Principal component analysis (PCA) is next implemented for reducing data dimensionality, yielding results consistent with, and extending those in the literature. Then, a multilayer perceptron is trained by a backpropagation ...
Artificial neural networks: opening the black box.
Dayhoff, J E; DeLeo, J M
2001-04-15
Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, especially medicine. For example, neural networks can extract new medical information from raw data, build computer models that are useful for medical decision-making, and aid in the distribution of medical expertise. Because many important neural network applications currently are emerging, the authors have prepared this article to bring a clearer understanding of these biologically inspired computing paradigms to anyone interested in exploring their use in medicine. They discuss the historical development of neural networks and provide the basic operational mathematics for the popular multilayered perceptron. The authors also describe good training, validation, and testing techniques, and discuss measurements of performance and reliability, including the use of bootstrap methods to obtain confidence intervals. Because it is possible to predict outcomes for individual patients with a neural network, the authors discuss the paradigm shift that is taking place from previous "bin-model" approaches, in which patient outcome and management is assumed from the statistical groups in which the patient fits. The authors explain that with neural networks it is possible to mediate predictions for individual patients with prevalence and misclassification cost considerations using receiver operating characteristic methodology. The authors illustrate their findings with examples that include prostate carcinoma detection, coronary heart disease risk prediction, and medication dosing. The authors identify and discuss obstacles to success, including the need for expanded databases and the need to establish multidisciplinary teams. The authors believe that these obstacles can be overcome and that neural networks have a very important role in future medical decision support and the patient management systems employed in routine medical practice. PMID:11309760
Chattopadhyay, S
2006-01-01
In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the best predictive model.
Chattopadhyay, Surajit
2006-01-01
In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the ...
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.
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)
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
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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.
Scientific Electronic Library Online (English)
Gustavo A., García; Octavio, Salcedo.
2010-06-01
Full Text Available El presente artículo describe la implementación de un sistema de predicción de fallos en redes LAN (fallos de timeout y rechazo en las conexiones), utilizando redes neuronales artificiales Perceptrón Multicapa. Se describe como se implementó el sistema, las pruebas realizadas para la selección de lo [...] s parámetros propios de la red neuronal, como del algoritmo de entrenamiento y los resultados de evaluación obtenidos. Abstract in english The paper presents the implementation of a system for predicting failures in LAN (timeout failure and rejection of connections), using neural networks (multilayer perceptron). It describes the implementation of the system, experiments conducted for the selection of specific parameters of the neural [...] network, training algorithm and evaluation results.
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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.
Griessbach, Gert; Eiselt, Michael; Dörschel, Jens; Witte, Herbert; Galicki, Miroslaw
1997-08-01
In this study, a proposition of simultaneous training of the neural network (multilayer perceptron) and adaptive preprocessing unit is presented. This cooperation enables the network to affect the preprocessing and as a consequence to vary the locations of pattern vectors in a feature space. Thus, during the learning process the network tries to find a good separation of classes of patterns, which results in convergence of the whole learning process. The strategy was developed in order to make efficient EEG monitoring in neonates possible. A comparison of the method presented herein with the known learning strategies for neural networks shows the need for using it as an alternative learning process. The convergence of the whole system is also discussed. Copyright 1997 Elsevier Science Ltd. PMID:12662508
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.
Control on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller
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Hayder S. Abd Al-Amir
2011-01-01
Full Text Available An adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation, LCO. The structure of the controller consists of two models :the modified Elman neural network (MENN and the feed forward multi-layer Perceptron (MLP. The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge and pitch motion of the wing and this neural model acts as the identifier. The feed forward neural controller is trained off-line and adaptive weights are implemented on-line to find the flap angles, which controls the plunge and pitch motion of the wing. The general back propagation algorithm is used to learn the feed forward neural controller and the neural identifier. The simulation results show the effectiveness of the proposed control algorithm; this is demonstrated by the minimized tracking error to zero approximation with very acceptable settling time even with the existence of bounded external disturbances.
Laboratory implementation of a neural network trajectory controller for a DC motor
Energy Technology Data Exchange (ETDEWEB)
Weerasooriya, S.; El-Sharkawi, M.A. (Univ. of Washington, Seattle (United States))
1993-03-01
The paper describes the laboratory implementation of a neural network controller for high performance dc drives. The objective is to control the rotor speed and/or position to follow an arbitrarily selected trajectory at all time. The control strategy is based on indirect Model Reference Adaptive Control(MRAC). The motor characteristics are explicitly identified through a multi-layer perceptron type neural network. The output of the trained neural network is used to drive the motor in order to achieve a desired time trajectory of the controlled variable. The main feature of the proposed controller is a neural network which captures the unknown inverse dynamics of the motor through a supervised learning algorithm. The noise rejection and knowledge generalization capabilities of the neural network are effectively used in order to achieve a robust controller design applicable in a wide range of operating conditions. Performance of the control algorithm is evaluated through a laboratory implementation. The neural network controller is assembled in a commercially available PC-based real-time control system shell, using software subroutines. An H-bridge, dc/dc voltage converter is interfaced with the computer to generate the specified terminal voltage sequence for driving the motor. All software and hardware components are off the shelf.' The versatility of the motor/controller arrangement is displayed through real-time plots of the controlled states.
A neural network device for on-line particle identification in cosmic ray experiments
International Nuclear Information System (INIS)
On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification
Barron, Leon P; McEneff, Gillian L
2016-01-15
For the first time, the performance of a generalised artificial neural network (ANN) approach for the prediction of 2492 chromatographic retention times (tR) is presented for a total of 1117 chemically diverse compounds present in a range of complex matrices and across 10 gradient reversed-phase liquid chromatography-(high resolution) mass spectrometry methods. Probabilistic, generalised regression, radial basis function as well as 2- and 3-layer multilayer perceptron-type neural networks were investigated to determine the most robust and accurate model for this purpose. Multi-layer perceptrons most frequently yielded the best correlations in 8 out of 10 methods. Averaged correlations of predicted versus measured tR across all methods were R(2)=0.918, 0.924 and 0.898 for the training, verification and test sets respectively. Predictions of blind test compounds (n=8-84 cases) resulted in an average absolute accuracy of 1.02±0.54min for all methods. Within this variation, absolute accuracy was observed to marginally improve for shorter runtimes, but was found to be relatively consistent with respect to analyte retention ranges (~5%). Finally, optimised and replicated network dependency on molecular descriptor data is presented and critically discussed across all methods. Overall, ANNs were considered especially suitable for suspects screening applications and could potentially be utilised in bracketed-type analyses in combination with high resolution mass spectrometry. PMID:26592605
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Stefania Bonafoni
2009-01-01
Full Text Available In this study different approaches based on multilayer perceptron neural networks are proposed and evaluated with the aim to retrieve tropospheric profiles by using GPS radio occultation data. We employed a data set of 445 occultations covering the land surface within the Tropics, split into desert and vegetation zone. The neural networks were trained with refractivity profiles as input computed from geometrical occultation parameters provided by the FORMOSAT-3/COSMIC satellites, while the targets were the dry and wet refractivity profiles and the dry pressure profiles obtained from the contemporary European Centre for Medium-Range Weather Forecast data. Such a new retrieval algorithm was chosen to solve the atmospheric profiling problem without the constraint of an independent knowledge of one atmospheric parameter at each GPS occultation.
A coherent perceptron for all-optical learning
International Nuclear Information System (INIS)
We present nonlinear photonic circuit models for constructing programmable linear transformations and use these to realize a coherent perceptron, i.e., an all-optical linear classifier capable of learning the classification boundary iteratively from training data through a coherent feedback rule. Through extensive semi-classical stochastic simulations we demonstrate that the device nearly attains the theoretical error bound for a model classification problem. (orig.)
A coherent perceptron for all-optical learning
Energy Technology Data Exchange (ETDEWEB)
Tezak, Nikolas; Mabuchi, Hideo [Stanford University, Edward L. Ginzton Laboratory, Stanford, CA (United States)
2015-12-15
We present nonlinear photonic circuit models for constructing programmable linear transformations and use these to realize a coherent perceptron, i.e., an all-optical linear classifier capable of learning the classification boundary iteratively from training data through a coherent feedback rule. Through extensive semi-classical stochastic simulations we demonstrate that the device nearly attains the theoretical error bound for a model classification problem. (orig.)
Directory of Open Access Journals (Sweden)
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..
The principles of artificial neural network information processing
International Nuclear Information System (INIS)
In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as Perceptron, Back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally, the application of artificial neural network for Chinese Character Recognition is also given. (author)
Scientific Electronic Library Online (English)
Mayra Luiza Marques da Silva, Binoti; Helio Garcia, Leite; Daniel Henrique Breda, Binoti; José Marinaldo, Gleriani.
2015-03-01
Full Text Available Objetivou-se, neste estudo, treinar, aplicar e avaliar a eficiência de redes neurais artificiais (RNA) para realizar a prognose da produção de povoamentos equiâneos de clones de eucalipto. Os dados utilizados foram provenientes de povoamentos localizados no sul da Bahia, totalizando cerca de 2.000 h [...] ectares de floresta. Foram utilizadas variáveis numéricas, como: idade, área basal, volume e variáveis categóricas, como classe de solo, textura, tipos de espaçamento, relevo, projeto e clone. Os dados foram divididos aleatoriamente em dois grupos: treinamento (80%) e generalização (20%). Foram treinadas redes de três tipos: perceptron, perceptron de múltiplas camadas e redes de função de base radial. As RNA que apresentaram os melhores desempenhos no treinamento e generalização foram selecionadas para realizar a prognose com dados, a partir do primeiro inventário florestal. Conclui-se que as RNA apresentaram resultados satisfatórios, comprovando o potencial e aplicabilidade da técnica na solução dos problemas de mensuração e manejo florestal. Abstract in english The objective of this study was to train, implement and evaluate the efficiency of artificial neural networks (ANN) to perform production prognosis of even-aged stands of eucalyptus clones. The data used were from plantations located in southern Bahia, totaling about 2,000 acres of forest. Numeric v [...] ariables, such as age, basal area, volume and categorical variables, such as soil class texture, spacing, land relief, project and clone were used. The data were randomly divided into two groups: training (80%) and generalization (20%). Three types of networks were trained: perceptron, multilayer perceptron networks and radial basis function. The RNA that showed the best performance in training and generalization were selected to perform the prognosis with data from the first forest inventory. We conclude that the RNA had satisfactory results, showing the potential and applicability of the technique in solving measurement and forest management problems.
Static sign language recognition using 1D descriptors and neural networks
SolÃs, JosÃ© F.; Toxqui, Carina; Padilla, Alfonso; Santiago, CÃ©sar
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.
Evaluation of Starting Current of Induction Motors Using Artificial Neural Network
Directory of Open Access Journals (Sweden)
Iman Sadeghkhani
2014-07-01
Full Text Available Induction motors (IMs are widely used in industry including it be an electrical or not. However during starting period, their starting currents are so large that can damage equipment. Therefore, this current should be estimated accurately to prevent hazards caused by it. In this paper, the artificial neural network (ANN as an intelligent tool is used to evaluate starting current peak of IMs. Both Multilayer Perceptron (MLP and Radial Basis Function (RBF structures have been analyzed. Six learning algorithms, backpropagation (BP, delta-bar-delta (DBD, extended delta-bar-delta (EDBD, directed random search (DRS, quick propagation (QP, and levenberg marquardt (LM were used to train the MLP. The simulation results using MATLAB show that most developed ANNs can estimate the starting current peak of IMs with good accuracy. However, it is proven that LM and EDBD algorithms present better performance for starting current evaluation based on average of relative and absolute errors.
Comparison of Neural Network and K-Nearest Neighbor Methods in Daily Flow Forecasting
Directory of Open Access Journals (Sweden)
Mirkhalegh Z. Ahmadi
2010-01-01
Full Text Available This study illustrates the application of Multilayer perceptron (MLP Neural Network (NN for flow prediction of a Bakhtiari River. Since measurement of variables is time consuming and defining the efficient variable is essential for better performance of NN, alternative method of flow forecasting is needed. The K-Nearest Neighbor (K-NN method which is a non-parametric regression methodology as indicated by the absence of any parameterized analytical function of the input-output relationship is used in this study. The implementation of each time series technique is investigated and the performances of the models are then compared. It is concluded that discharge in one day-ahead and Antecedent Precipitation Index (API for seven days-ahead are the most important inputs and NN model has little better result than nearest neighbor method.
Early detection of incipient faults in power plants using accelerated neural network learning
International Nuclear Information System (INIS)
An important aspect of power plant automation is the development of computer systems able to detect and isolate incipient (slowly developing) faults at the earliest possible stages of their occurrence. In this paper, the development and testing of such a fault detection scheme is presented based on recognition of sensor signatures during various failure modes. An accelerated learning algorithm, namely adaptive backpropagation (ABP), has been developed that allows the training of a multilayer perceptron (MLP) network to a high degree of accuracy, with an order of magnitude improvement in convergence speed. An artificial neural network (ANN) has been successfully trained using the ABP algorithm, and it has been extensively tested with simulated data to detect and classify incipient faults of various types and severity and in the presence of varying sensor noise levels
Neural networks for emulation variational method for data assimilation in nonlinear dynamics
Energy Technology Data Exchange (ETDEWEB)
Morais Furtado, Helaine Cristina; Fraga de Campos Velho, Haroldo; Macau, Elbert E N, E-mail: helaine.furtado@lac.inpe.br, E-mail: haroldo@lac.inpe.br, E-mail: elbert@lac.inpe.br [Laboratorio Associado de Computacao e Matematica Aplicada, Sao Jose dos Campos (Brazil)
2011-03-01
Description of a physical phenomenon through differential equations has errors involved, since the mathematical model is always an approximation of reality. For an operational prediction system, one strategy to improve the prediction is to add some information from the real dynamics into mathematical model. This additional information consists of observations on the phenomenon. However, the observational data insertion should be done carefully, for avoiding a worse performance of the prediction. Technical data assimilation are tools to combine data from physical-mathematics model with observational data to obtain a better forecast. The goal of this work is to present the performance of the Neural Network Multilayer Perceptrons trained to emulate a Variational method in context of data assimilation. Techniques for data assimilation are applied for the Lorenz systems; which presents a strong nonlinearity and chaotic nature.
Scott, D J; Kilner, J A; Rossiny, J C H; McAlford, N N
2007-01-01
We describe the development of artificial neural networks (ANN) for the prediction of the properties of ceramic materials. The ceramics studied here include polycrystalline, inorganic, non-metallic materials and are investigated on the basis of their dielectric and ionic properties. Dielectric materials are of interest in telecommunication applications where they are used in tuning and filtering equipment. Ionic and mixed conductors are the subjects of a concerted effort in the search for new materials that can be incorporated into efficient, clean electrochemical devices of interest in energy production and greenhouse gas reduction applications. Multi-layer perceptron ANNs are trained using the back-propagation algorithm and utilise data obtained from the literature to learn composition-property relationships between the inputs and outputs of the system. The trained networks use compositional information to predict the relative permittivity and oxygen diffusion properties of ceramic materials. The results sh...
Modeling mechanical properties of cast aluminum alloy using artificial neural network
International Nuclear Information System (INIS)
Modeling is widely used to investigate the mechanical properties of engineering materials due to increasing demand of low cost and high strength to weight ratio for many engineering applications. The aluminum casting alloys are cost competitive material and possess the desired properties. The mechanical properties largely depend upon composition of alloys and their processing method. Alloy design involves controlling mechanical properties via optimization of the composition and processing parameters. For optimization the possible root is empirical modeling and its more refined version is the analysis of the wide range of data using ANN (Artificial Neural Networks) modeling. The modeling of mechanical properties of the aluminum alloys are the main objective of present work. For this purpose, some data were collected and experimentally prepared using conventional casting method. A MLP (Multilayer Perceptron) network was developed, which is trained by using the error back propagation algorithm. (author)
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)
Prediction of Atmospheric Pressure at Ground Level using Artificial Neural Network
Directory of Open Access Journals (Sweden)
Angshuman Ray
2013-01-01
Full Text Available Prediction of Atmospheric Pressure is one important and challenging task that needs lot of attention and study for analyzing atmospheric conditions. Advent of digital computers and development of data driven artificial intelligence approaches like Artificial Neural Networks (ANN have helped in numerical prediction of pressure. However, very few works have been done till now in this area. The present study developed an ANN model based on the past observations of several meteorological parameters like temperature, humidity, air pressure and vapour pressure as an input for training the model. The novel architecture of the proposed model contains several multilayer perceptron network (MLP to realize better performance. The model is enriched by analysis of alternative hybrid model of k-means clustering and MLP. The improvement of the performance in the prediction accuracy has been demonstrated by the automatic selection of the appropriate cluster
Concept and design of the fast H1 second level trigger using artificial neural networks
Energy Technology Data Exchange (ETDEWEB)
Kolanoski, H. [Humboldt-Universitaet, Berlin (Germany). Inst. fuer Physik; Getta, H.; Goldner, D. [Dortmund Univ. (Germany). Inst. fuer Physik] [and others
1996-07-01
The experiments at the HERA ep collider have to cope with machine background rates which exceed the rates of the majority ofinteresting physics interactions by several orders of magnitude. To deal with this the H1 experiment was designed with a four staged trigger system. The second trigger level is currently being equipped with a highly parallel computing system that is specialized to run Artificial Neural Network (ANN) algorithms. The trigger is based on CNAPS neurocomputer boards, which are able to compute multilayer perceptron networks with 64 inputs, 64 hidden nodes and one output node in less than 8 {mu}s. In this contribution we present the concept and design of this system. (author)
Concept and design of the fast H1 second level trigger using artificial neural networks
International Nuclear Information System (INIS)
The experiments at the HERA ep collider have to cope with machine background rates which exceed the rates of the majority of interesting physics interactions by several orders of magnitude. To deal with this the H1 experiment was designed with a four staged trigger system. The second trigger level is currently being equipped with a highly parallel computing system that is specialized to run Artificial Neural Network (ANN) algorithms. The trigger is based on CNAPS neurocomputer boards, which are able to compute multilayer perceptron networks with 64 inputs, 64 hidden nodes and one output node in less than 8 ?s. In this contribution we present the concept and design of this system. (author)
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)
Ali Abroudi
2013-04-01
Full Text Available This paper presents a hybrid approach with two phases for improving the performance of training artificial neural networks (ANNs by selection of the most important instances for training, and then reduction the dimensionality of features. The ANNs which are applied in this paper for validation, are included Multi-Layer Perceptron (MLP and Neuro-Fuzzy Network (NFN. In the first phase, the Modified Fast Condensed Nearest Neighbor (MFCNN algorithm is used to construct the subset with instances very close to the decision boundary. It leads to achieve the instances more useful for training the network. And in the second phase, an Ant-based approach to the supervised reduction of feature dimensionality is introduced, aims to reduce the complexity, and improve the accuracy of learning the ANN. The main purpose of this method is to enhance the classification performance by improving the quality of the training set. Experimental results illustrated the efficiency of the proposed approach.
Energy and Carbon Flux Coupling: Multi-ecosystem Comparisons Using Artificial Neural Network
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Assefa M. Melesse
2005-01-01
Full Text Available A multi-ecosystems carbon flux simulation from energy fluxes is presented. A new statistical learning technique based on Artificial Neural Network (ANN back propagation algorithm and multi-layer perceptron architecture was used in the CO2 simulation. Four input layers (net radiation, soil heat flux, sensible and latent heat flux were used for training (calibration and testing (verification of model outputs. The 15-days half-hourly (grassland and hourly (forest and cropland micrometeorological data from eddy covariance observations of AmeriFlux towers were divided into training (5-days and testing (10-days sets. Results show that the ANN-based technique predicts CO2 flux with testing R2 values of 0.86, 0.75 and 0.94 for forest, grassland and cropland ecosystems, respectively. The technique is reliable and efficient to estimate regional or global CO2 fluxes from point measurements and understand the spatiotemporal budget of the CO2 fluxes.
A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?
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Elsy GÃ³mez-Ramos
2013-12-01
Full Text Available At the beginning of the 90â€™s, Artificial Neural Networks (ANNs started their applications in finance. The ANNs are data-drive, self-adaptive and non-linear methods that do not require specific assumptions about the underlying model. In general, there are five groups of networks used as forecasting tools: 1 Feedforward Networks, like the Multilayer Perceptron (MLP, 2 Recurrent Networks, 3 Polynomial Networks, 4 Modular Networks, and 5 Support Vector Machine. This paper carries out a review of the specialized literature on ANNs and makes a comparative analysis according to their performance in forecasting stock indices and exchange rates. The objective is to assess the performance when applying different types of networks in relation to MLP. It is shown that the MLP is the best network in forecasting time series. However, it is shown that the MLP has important delimitations in several respects: network architecture, basic functions and initialization weights.
Directory of Open Access Journals (Sweden)
A. Ghaemi
2008-01-01
Full Text Available In this study, an Artificial Neural Network (ANN model has been developed for aqueous solutions of electrolyte systems. Multilayer Perceptron (MLP and Radial Basis Function (RBF networks were applied to estimate vapor-liquid equilibrium data for ternary system of NH3-CO2-H2O. Experimental data, taken from the literature were divided into three sections of training, validating and testing. Mean Absolute Errors (MAE of the networks for training set are used as network selection criterion and to find optimal design of the networks. The performance of ANN models to predict partial and total pressures of NH3-CO2-H2O system were evaluated by comparing their results with the predictions of some thermodynamic models. The criterion for this comparison was the error between models perditions and the experimental data. The comparison indicated that both MLP and RBF models predict the system better than the thermodynamic models.
Neural networks for emulation variational method for data assimilation in nonlinear dynamics
International Nuclear Information System (INIS)
Description of a physical phenomenon through differential equations has errors involved, since the mathematical model is always an approximation of reality. For an operational prediction system, one strategy to improve the prediction is to add some information from the real dynamics into mathematical model. This additional information consists of observations on the phenomenon. However, the observational data insertion should be done carefully, for avoiding a worse performance of the prediction. Technical data assimilation are tools to combine data from physical-mathematics model with observational data to obtain a better forecast. The goal of this work is to present the performance of the Neural Network Multilayer Perceptrons trained to emulate a Variational method in context of data assimilation. Techniques for data assimilation are applied for the Lorenz systems; which presents a strong nonlinearity and chaotic nature.
Cintra, Rosangela S
2014-01-01
This paper presents an approach for employing artificial neural networks (NN) to emulate an ensemble Kalman filter (EnKF) as a method of data assimilation. The assimilation methods are tested in the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localization of balloon soundings. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLP-NN), is applied. The MLP-NN are able to emulate the analysis from the local ensemble transform Kalman filter (LETKF). After the training process, the method using the MLP-NN is seen as a function of data assimilation. The NN were trained with data from first three months of 1982, 1983, and 1984. A hind-casting experiment for the 1985 data assimilation cycle using MLP-NN were performed with synthetic observations for January 1985. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilati...
Viscosity Calculation at Moderate Pressure for Nonpolar Gases via Neural Network
Directory of Open Access Journals (Sweden)
A. Bouzidi
2007-01-01
Full Text Available A new method, based on Artificial Neural Networks (ANN of Multi-Layer Perceptron (MLP type, has been developed to estimate the viscosity at moderate pressure for pure nonpolar gases over a wide range of temperatures. An ANN was trained, using four physicochemical properties: Molecular weight (M, boiling point (Tb, critical Temperature (Tc and critical Pressure (Pc combined with absolute Temperature (T as its inputs, to correlate and predict viscosity. A group of 52 nonpolar gases were used to train and test the performance of the ANN. The viscosity and input data for each individual gas was compiled on average at fifty different temperatures, ranging from the boiling points for each of the chosen gases to 1100 K. The maximum absolute error in viscosity, predicted by the ANN, was approximately 15%.
Solar geomagnetic activity prediction using the fractal analysis and neural network
Ouadfeul, Sid-Ali; Aliouane, Leila
2010-05-01
The main goal of this work is to predict the Solar geomagnetic field activity using the neural network combined with the fractal analysis, first a multilayer perceptron neural network model is proposed to predict the future Solar geomagnetic field, the input of this machine is the geographic Coordinates and the time .The output is the three geomagnetic field components and the total field intensity recorded by the Orsted Satellite Mission. Holder Exponents of the measured geomagnetic field components and the total field intensity are calculated using the continuous wavelet transform. The Set of Holder exponents is used to train a Kohonen's Self-Organizing Map (SOM) neural machine which will become a classifier of the solar magnetic activity nature. The SOM neural network machine is used to predict the future solar magnetic storms, in this step the input is the calculated set of the Holder exponents of the predicted geomagnetic field components and the total field intensity. Obtained results show that the proposed technique is a powerful tool and can enhance the solar magnetic field activity prediction. Keywords: Solar geomagnetic activity, neural network, prediction, Orsted, Holder Exponents, Solar magnetic storms.
A new source difference artificial neural network for enhanced positioning accuracy
International Nuclear Information System (INIS)
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 inputâ€“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
Evaluation of Neural Networks Performance in Active Cancellation of Acoustic Noise
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Mehrshad Salmasi,
2014-12-01
Full Text Available Active Noise Control (ANC works on the principle of destructive interference between the primary disturbance field heard as undesired noise and the secondary field which is generated from control actuators. In the simplest system, the disturbance field can be a simple sine wave, and the secondary field is the same sine wave but 180 degrees out of phase. This research presents an investigation on the use of different types of neural networks in active noise control. Performance of the multilayer perceptron (MLP, Elman and generalized regression neural networks (GRNN in active cancellation of acoustic noise signals is investigated and compared in this paper. Acoustic noise signals are selected from a Signal Processing Information Base (SPIB database. In order to compare the networks appropriately, similar structures and similar training and test samples are deduced for neural networks. The simulation results show that MLP, GRNN, and Elman neural networks present proper performance in active cancellation of acoustic noise. It is concluded that Elman and MLP neural networks have better performance than GRNN in noise attenuation. It is demonstrated that designed ANC system achieve good noise reduction in low frequencies.
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.
LOCALIZATION FOR WIRELESS SENSOR NETWORKS: A NEURAL NETWORK APPROACH
Directory of Open Access Journals (Sweden)
Shiu Kumar
2016-01-01
Full Text Available As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
Neural Network Based Lna Design for Mobile Satellite Receiver
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Abhijeet Upadhya
2014-08-01
Full Text Available Paper presents a Neural Network Modelling approach to microwave LNA design. To acknowledge the specifications of the amplifier, Mobile Satellite Systems are analyzed. Scattering parameters of the LNA in the frequency range 0.5 to 18 GHz are calculated using a Multilayer Perceptron Artificial Neural Network model and corresponding smith charts and polar charts are plotted as output to the model. From these plots, the microwave scattering parameter description of the LNA are obtained. Model is efficiently trained using Agilent ATF 331M4 InGaAs/InP Low Noise pHEMT amplifier datasheet and the neural model’s output seem to follow the various device characteristic curves with high regression. Next, Maximum Allowable Gain and Noise figure of the device are modelled and plotted for the same frequency range. Finally, the optimized model is utilized as an interpolator and the resolution of the amplifying capability with noise characteristics are obtained for the L Band of MSS operation.
Artificial Neural Network Approach in Radar Target Classification
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N. K. Ibrahim
2009-01-01
Full Text Available Problem statement: This study unveils the potential and utilization of Neural Network (NN in radar applications for target classification. The radar system under test is a special of it kinds and known as Forward Scattering Radar (FSR. In this study the target is a ground vehicle which is represented by typical public road transport. The features from raw radar signal were extracted manually prior to classification process using Neural Network (NN. Features given to the proposed network model are identified through radar theoretical analysis. Multi-Layer Perceptron (MLP back-propagation neural network trained with three back-propagation algorithm was implemented and analyzed. In NN classifier, the unknown target is sent to the network trained by the known targets to attain the accurate output. Approach: Two types of classifications were analyzed. The first one is to classify the exact type of vehicle, four vehicle types were selected. The second objective is to grouped vehicle into their categories. The proposed NN architecture is compared to the K Nearest Neighbor classifier and the performance is evaluated. Results: Based on the results, the proposed NN provides a higher percentage of successful classification than the KNN classifier. Conclusion/Recommendation: The result presented here show that NN can be effectively employed in radar classification applications.
Directory of Open Access Journals (Sweden)
G. Sánchez
2004-01-01
Full Text Available Este artículo propone una red neuronal de tipo perceptron multicapas (MLP que optimiza tanto su matriz de pesos como el número de neuronas ocultas. Inicialmente el sistema propuesto usa un número reducido de neuronas ocultas, optimizándose la matriz de pesos mediante un algoritmo de perturbación simultánea. Una vez que la red converge se analiza su funcionamiento y si este no es el esperado se agrega una neurona oculta. Este proceso se repite hasta obtener el funcionamiento deseado. Los resultados obtenidos muestran que el sistema propuesto presenta un funcionamiento muy similar al de un MLP convencional, cuando éste tiene un número óptimo de nodos en la capa oculta y disminuye la complejidad computacional durante la etapa de entrenamiento.This paper proposes a multilayer perceptron neural network (MLP which optimizes both the matrix weights and the numbers of hidden neurons. Initially, the proposed system uses a reduced number of hidden neurons, optimizing the matrix weights by using a simultaneous perturbation algorithm. Once the network converges, its function is analyzed and if this is not as expected, a hidden neuron is added. This process is repeated until achieving the desired functioning. The results obtained show that the proposed system functions similarly to that of a conventional MLP when this has an optimal number of nodes in the hidden layer, decreasing the computational complexity during the training step.
Representations of highly-varying functions by perceptron networks.
Czech Academy of Sciences Publication Activity Database
K?rková, V?ra
North Charleston : CreateSpace Independent Publishing Platform, 2013 - (Vina?, T.; Hole?a, M.; Lexa, M.; Peška, L.; Vojtáš, P.), s. 73-76 ISBN 978-1-4909-5208-6. [ITAT 2013. Conference on Theory and Practice of Information Technologies. Donovaly (SK), 11.09.2013-15.09.2013] R&D Projects: GA ?R GAP202/11/1368 Institutional support: RVO:67985807 Keywords : one-hidden-layer networks * perceptrons * Boolean functions * network complexity Subject RIV: IN - Informatics, Computer Science
Prediction of the local power factor in BWR fuel cells by means of a multilayer neural network
International Nuclear Information System (INIS)
To the beginning of a new operation cycle in a BWR reactor the reactivity of this it increases by means of the introduction of fresh fuel, the one denominated reload fuel. The problem of the definition of the characteristics of this reload fuel represents a combinatory optimization problem that requires significantly a great quantity of CPU time for their determination. This situation has motivated to study the possibility to substitute the Helios code, the one which is used to generate the new cells of the reload fuel parameters, by an artificial neuronal network, with the purpose of predicting the parameters of the fuel reload cell of a BWR reactor. In this work the results of the one training of a multilayer neuronal net that can predict the local power factor (LPPF) in such fuel cells are presented. The prediction of the LPPF is carried out in those condition of beginning of the life of the cell (0.0 MWD/T, to 40% of holes in the one moderator, temperature of 793 K in the fuel and a moderator temperature of 560 K. The cells considered in the present study consist of an arrangement of 10x10 bars, of those which 92 contains U235, some of these bars also contain a concentration of Gd2O3 and 8 of them contain only water. The axial location inside the one assembles of recharge of these cells it is exactly up of the cells that contain natural uranium in the base of the reactor core. The training of the neuronal net is carried out by means of a retro-propagation algorithm that uses a space of training formed starting from previous evaluations of cells by means of the Helios code. They are also presented the results of the application of the neuronal net found for the prediction of the LPPF of some cells used in the real operation of the Unit One of the Laguna Verde Nuclear Power station. (Author)
Elías, A; Ibarra-Berastegi, G; Arias, R; Barona, A
2006-07-01
Based on an experimental database consisting of 194 daily cases, artificial neural networks were used to model the removal efficiency of a biofilter for treating hydrogen sulphide (H2S). In this work, the removal efficiency of the reactor was considered as a function of the changes in the air flow and concentration of H2S entering the biofilter. In order to obtain true representative values, the removal efficiencies (outputs) were measured 24 h after each input was changed. A MLP (multilayer perceptron 2-2-1) model with two input variables (unit flow and concentration of the contaminant fed into the biofilter) rendered good prediction values with a determination coefficient of 0.92 for the removal efficiency within the range studied. This means that the MLP model can explain 92% of the overall variability detected in the biofilter corresponding to a wide range of operating conditions. PMID:16770593
Sun, Y.; Wendi, D.; Kim, D. E.; Liong, S.-Y.
2015-09-01
Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a swamp forest of Singapore. A standard multilayer perceptron (MLP) is selected, trained with the Levenberg-Marquardt (LM) algorithm. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce accurate forecast with a leading time up to 7 days, whereas the performance slightly decreases when leading time increases.
Evrendilek, Fatih; Denizli, Haluk; Yetis, Hakan; Karakaya, Nusret
2013-07-01
Concentrations of outdoor radon-222 ((222)Rn) in temperate grazed peatland and deciduous forest in northwestern Turkey were measured, compared, and modeled using artificial neural networks (ANNs) and multiple nonlinear regression (MNLR) models. The best-performing multilayer perceptron model selected out of 28 ANNs considerably enhanced accuracy metrics in emulating (222)Rn concentrations relative to the MNLR model. The two ecosystems had similar diel patterns with the lowest (222)Rn concentrations in the afternoon and the highest ones near dawn. Mean level (5.1?+?2.5 Bq?m(-3) h(-1)) of (222)Rn in the forest was three times smaller than that (15.8?+?9.7 Bq?m(-3)) of (222)Rn in the peatland. Mean (222)Rn level had negative and positive relationships with air temperature and relative humidity, respectively. PMID:23096138
Prosopagnosia in high capacity neural networks storing uncorrelated classes
Franz, S; Amit, D J; Virasoro, M.A.
1990-01-01
We display a synaptic matrix that can efficiently store, in attractor neural networks (ANN) and perceptrons, patterns organized in uncorrelated classes. We find a storage capacity limit increasing with m, the overlap of a pattern with its class ancestor, and diverging as m ? 1. The probability distribution of the local stability parameters is studied, leading to a complete analysis of the performance of a perceptron with this synaptic matrix, and to a qualitative understanding of the behavior...
Integrated On-line Plant Monitoring System for HTTR with Neural Networks
Nabeshima, Kunihiko; Subekti, Muhammad; Matsuishi, Tomomi; Ohno, Tomio; Kudo, Kazuhiko; Nakagawa, Shigeaki
The neural networks have been utilized in on-line monitoring-system of High Temperature Engineering Tested Reactor (HTTR) with thermal power of 30MW. In this system, several neural networks can independently model the plant dynamics with different architecture, input and output signals and learning algorithm. Monitoring task of each neural network is also different, respectively. Those parallel method applications require distributed architecture of computer network for performing real-time tasks. One of main task is real-time plant monitoring by Multi-Layer Perceptron (MLP) in auto-associative mode, which can model and estimate the whole plant dynamics by training normal operational data only. The basic principle of the anomaly detection is to monitor the difference between process signals measured from the actual plant and the corresponding values estimated by MLP. Other tasks are on-line reactivity prediction, reactivity and helium leak monitoring, respectively. From the on-line monitoring results at the safety demonstration tests, each neural network shows good prediction and reliable detection performances.
Integrated on-line plant monitoring system for HTTR using neural networks
International Nuclear Information System (INIS)
The neural networks have been utilized in on-line monitoring-system of High Temperature Engineering Tested Reactor (HTTR) with thermal power of 30 MW. In this system, several neural networks can independently model the plant dynamics with different architecture, input and output signals and learning algorithm. Monitoring task of each neural network is also different, respectively. Those parallel method applications require distributed architecture of computer network for performing real-time tasks. One of main task is real-time monitoring by Multi-Layer Perceptron (MLP) in auto-associative mode, which can model and estimate the whole plant dynamics by training normal operational data only. The basic principle of the anomaly detection is to monitor the difference between process signals measured from the actual plant and the corresponding values estimated by MLP. Other tasks are on-line reactivity prediction, reactivity and helium leak monitoring, respectively. From the on-line test results, each neural network shows good prediction and reliable detection performances. (author)
Application of Artificial Neural Networks for Efficient High-Resolution 2D DOA Estimation
Directory of Open Access Journals (Sweden)
M. Agatonovi?
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.
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 of the river while multi-layer perceptron is more efficient for the downstream stretches. PMID:26451249
Chaotic diagonal recurrent neural network
International Nuclear Information System (INIS)
We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)
Fast cosmological parameter estimation using neural networks
Auld, T; Hobson, M P; Gull, S F
2006-01-01
We present a method for accelerating the calculation of CMB power spectra, matter power spectra and likelihood functions for use in cosmological parameter estimation. The algorithm, called CosmoNet, is based on training a multilayer perceptron neural network and shares all the advantages of the recently released Pico algorithm of Fendt & Wandelt, but has several additional benefits in terms of simplicity, computational speed, memory requirements and ease of training. We demonstrate the capabilities of CosmoNet by computing CMB power spectra over a box in the parameter space of flat \\Lambda CDM models containing the 3\\sigma WMAP1 confidence region. We also use CosmoNet to compute the WMAP3 likelihood for flat \\Lambda CDM models and show that marginalised posteriors on parameters derived are very similar to those obtained using CAMB and the WMAP3 code. We find that the average error in the power spectra is typically 2-3% of cosmic variance, and that CosmoNet is \\sim 7 \\times 10^4 faster than CAMB (for flat ...
The reactor safety study with help of artificial neuron networks (multilayer perceptrons)
International Nuclear Information System (INIS)
One deals with deposition of insulation large amounts on settling tank components that may result in malfunction of residual heat removal systems. Paper describes briefly simulation of pressure drops in confinement systems by means of an artificial neuron nets and compares the simulation data with the experiment ones
Multilayer perceptron for simulation models reduction: application to a sawmill workshop
Thomas, Philippe; Thomas, André
2011-01-01
Simulation is often used to evaluate supply chain or workshop management. This simulation task needs models, which are difficult to construct. The aim of this work is to reduce the complexity of a simulation model design. The proposed approach combines discrete and continuous approaches in order to construct speeder and simpler reduced model. The simulation model focuses on bottlenecks with a discrete approach according to the theory of constraints. The remaining of the workshop must be taken...
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.
Analysis of JET charge exchange spectra using neural networks
International Nuclear Information System (INIS)
Active charge exchange spectra representing the local interaction of injected neutral beams and fully stripped impurity ions are hard to analyse due to strong blending with passive emission from the plasma edge. As a result, the deduced plasma parameters (e.g. ion temperature, rotation velocity, impurity density) cannot always be determined unambiguously. Also, the speed of the analysis is limited by the time consuming nonlinear least-squares minimization procedure. In practice, semi-manual analysis is necessary and fast, automatic analysis, based on currently used techniques, does not seem feasible. In this paper the development of a robust and accurate analysis procedure based on multi-layer perceptron (MLP) neural networks is described. This procedure is fully automatic and fast, thus enabling a real-time analysis of charge exchange spectra. Accuracy has been increased in several ways as compared to earlier straightforward neural network implementations and is comparable to a standard least-squares based analysis. Robustness is achieved by using a combination of different confidence measures. A novel technique for the creation of training data, suitable for high-dimensional inverse problems has been developed and used extensively. A new method for fast calculation of error bars directly from the hidden neurons in a MLP network is also described, and used as part of the confidence calculations. For demonstration purposes, a real-time ion temperature profile diagnostic based on this work has been implemented. (author)
Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm
Directory of Open Access Journals (Sweden)
B. Samanta
2004-03-01
Full Text Available A study is presented to compare the performance of bearing fault detection using three types of artificial neural networks (ANNs, namely, multilayer perceptron (MLP, radial basis function (RBF network, and probabilistic neural network (PNN. The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to all three ANN classifiers: MLP, RBF, and PNN for two-class (normal or fault recognition. The characteristic parameters like number of nodes in the hidden layer of MLP and the width of RBF, in case of RBF and PNN along with the selection of input features, are optimized using genetic algorithms (GA. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine with and without bearing faults. The results show the relative effectiveness of three classifiers in detection of the bearing condition.
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. PMID:24210290
Implementation of a spike-based perceptron learning rule using TiO2-x memristors.
Mostafa, Hesham; Khiat, Ali; Serb, Alexander; Mayr, Christian G; Indiveri, Giacomo; Prodromakis, Themis
2015-01-01
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 TiO2-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. PMID:26483629
Implementation of a spike-based perceptron learning rule using TiO2?x memristors
Mostafa, Hesham; Khiat, Ali; Serb, Alexander; Mayr, Christian G.; Indiveri, Giacomo; Prodromakis, Themis
2015-01-01
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 TiO2?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. PMID:26483629
Handwritten Arabic Numeral Recognition using a Multi Layer Perceptron
Das, Nibaran; Saha, Sudip; Haque, Syed Sahidul
2010-01-01
Handwritten numeral recognition is in general a benchmark problem of Pattern Recognition and Artificial Intelligence. Compared to the problem of printed numeral recognition, the problem of handwritten numeral recognition is compounded due to variations in shapes and sizes of handwritten characters. Considering all these, the problem of handwritten numeral recognition is addressed under the present work in respect to handwritten Arabic numerals. Arabic is spoken throughout the Arab World and the fifth most popular language in the world slightly before Portuguese and Bengali. For the present work, we have developed a feature set of 88 features is designed to represent samples of handwritten Arabic numerals for this work. It includes 72 shadow and 16 octant features. A Multi Layer Perceptron (MLP) based classifier is used here for recognition handwritten Arabic digits represented with the said feature set. On experimentation with a database of 3000 samples, the technique yields an average recognition rate of 94....
A Neural Network Based Real Time Controller for Turning Process
Bahaa Ibraheem Kazem; Nihad F. H. Zangana
2007-01-01
In this paper, the design and implementation of an effective neural network model for turning process identification as well as a neural network controller to track a desired vibration level of the turning machine is as an example of using the neural network for manufacturing process control. Multi – Layer Perceptron (MLP) neural network architecture with Levenberg Marquardt (LM) algorithm has been utilized to train the turning process identifier. Two different strategies have been used for t...
Tagging b quark events in ALEPH with neural networks
International Nuclear Information System (INIS)
Comparison of different methods to tag b quark events are presented: multilayered perceptron (MLP), Learning Vector Quantization (LVQ), discriminant analysis, combination of any two of the above methods. The sample events come from the ALEPH Monte Carlo and data, from the 1990 ALEPH runs. (authors) 12 refs., 16 figs., 5 tabs
Directory of Open Access Journals (Sweden)
Bednyakov Dmitriy Andreevich
2012-11-01
Full Text Available The article is devoted to the simulation of the influence of environmental osmotic pressure on the changes of the level of ?-amylase activity of mucous tunic of the intestine of Russian sturgeon (Acipenser güldenstädtii Brandt. For the solving of this problem the apparatus of neural networks is used. The designed model can be classified as multilayer perceptrone and has rather transparent structure. The conformities of this influence are examined and the model with high approximating and generalizing properties is created. The conclusion about high availability of application of the approach in the studies of adaptations of the digestive system of aquatic organisms to the influence of environmental factors with some qualifications about used rate of exactness of the simulation is made.
Witte, H; Doering, A; Galicki, M; Dörschel, J; Krajca, V; Eiselt, M
1995-01-01
The main goal of this study is to demonstrate the possibility of training the Neural Network (multilayer perceptron) classifier and preprocessing units simultaneously, i.e., that properties of preprocessing are chosen automatically during the training phase. In the first realization step, adaptive recursive estimation of the power within a frequency band was used as a preprocessing unit. To improve the efficiency of special units, the power and momentary frequency estimation was replaced by methods that are based on adaptive Hilbert transformers. The strategy was developed to obtain optimized recognition units that can be efficiently integrated into strategies for monitoring the cerebral status of neonates. Therefore, applications (e.g., in neonatal EEG pattern recognition) will be shown. Additionally, a method of minimizing the error function was used, where this minimization is based on optimizing the network structure. The results of structure optimization in the field of EEG pattern recognition in epileptic patients can be demonstrated. PMID:8591340
Machine and component residual life estimation through the application of neural networks
International Nuclear Information System (INIS)
This paper concerns the use of neural networks for predicting the residual life of machines and components. In addition, the advantage of using condition-monitoring data to enhance the predictive capability of these neural networks was also investigated. A number of neural network variations were trained and tested with the data of two different reliability-related datasets. The first dataset represents the renewal case where the failed unit is repaired and restored to a good-as-new condition. Data were collected in the laboratory by subjecting a series of similar test pieces to fatigue loading with a hydraulic actuator. The average prediction error of the various neural networks being compared varied from 431 to 841 s on this dataset, where test pieces had a characteristic life of 8971 s. The second dataset were collected from a group of pumps used to circulate a water and magnetite solution within a plant. The data therefore originated from a repaired system affected by reliability degradation. When optimized, the multi-layer perceptron neural networks trained with the Levenberg-Marquardt algorithm and the general regression neural network produced a sum-of-squares error within 11.1% of each other for the renewal dataset. The small number of inputs and poorly mapped input space on the second dataset meant that much larger errors were recorded on some of the test data. The potential for using neural networks for residual life prediction and the advantage of incorporating condition-based data into the model was nevertheless proven for both examples
Directory of Open Access Journals (Sweden)
Satish Kumar
2012-09-01
Full Text Available In this study, a method of artificial neural network applied for the solution of inverse kinematics of 2-link serial chain manipulator. The method is multilayer perceptrons neural network has applied. This unsupervised method learns the functional relationship between input (Cartesian space and output (joint space based on a localized adaptation of the mapping, by using the manipulator itself under joint control and adapting the solution based on a comparison between the resulting locations of the manipulator's end effectors in Cartesian space with the desired location. Even when a manipulator is not available; the approach is still valid if the forward kinematic equations are used as a model of the manipulator. The forward kinematic equations always have a unique solution, and the resulting Neural net can be used as a starting point for further refinement when the manipulator does become available. Artificial neural network especially MLP are used to learn the forward and the inverse kinematic equations of two degrees freedom robot arm. A set of some data sets were first generated as per the formula equation for this the input parameter X and Y coordinates in inches. Using these data sets was basis for the training and evaluation or testing the MLP model. Out of the sets data points, maximum were used as training data and some were used for testing for MLP. Backpropagation algorithm was used for training the network and for updating the desired weights. In this work epoch based training method was applied.
Artificial neural network as the tool in prediction rheological features of raw minced meat
Directory of Open Access Journals (Sweden)
Edyta Balejko
2012-09-01
Full Text Available Â Background. 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. Material and methods. 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. Results. 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. Conclusion. 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. Â
Modeling soil temperatures at different depths by using three different neural computing techniques
Kisi, Ozgur; Tombul, Mustafa; Kermani, Mohammad Zounemat
2015-07-01
This study compares the accuracy of three different neural computing techniques, multi-layer perceptron (MLP), radial basis neural networks (RBNN), and generalized regression neural networks (GRNN), in modeling soil temperatures (ST) at different depths. Climatic data of air temperature, wind speed, solar radiation, and relative humidity from Mersin Station, Turkey, were used as inputs to the models to estimate monthly ST values. In the first part of the study, the effect of each climatic variable on ST was investigated by using GRNN models. Air temperature was found to be the most effective variable in modeling monthly ST. In the second part of the study, the accuracy of GRNN models was compared with MLP, RBNN, and multiple linear regression (MLR) models. RBNN models were found to be better than the GRNN, MLP, and MLR models in estimating monthly ST at the depths of 5 and 10 cm while the MLR and GRNN models gave the best accuracy in the case of 50- and 100-cm depths, respectively. In the third part of the study, the effect of periodicity on the training, validation, and test accuracy of the applied models was investigated. The results indicated that the adding periodicity component significantly increase models' accuracies in estimating monthly ST at different depths. Root mean square errors of the GRNN, MLP, RBNN, and MLR models were decreased by 19, 15, 19, and 15 % using periodicity in estimating monthly ST at 5-cm depth.
A research about breast cancer detection using different neural networks and K-MICA algorithm
Directory of Open Access Journals (Sweden)
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.
Global load demand forecasting of Spain using a MLP neural network model
Energy Technology Data Exchange (ETDEWEB)
Senabre, C.; Valero, S.; Ortiz, M. [Miguel Hernandez Univ., Elche (Spain); Gabaldon, A. [Cartagena Polytechnical Univ., Cartagena (Spain). Dept. of Electrical Engineering
2008-07-01
Load forecasting is extremely important for the operation of power systems. It is necessary for the daily scheduling of power plants. Accurate short term electrical load forecasting avoids unnecessary costs and risky operational conditions, and enables utilities to commit their production resources to optimize energy prices and exchange with vendors and clients. This research utilized neural networks as a short-term forecasting method for the global load demand curve of Spain. Specifically, the paper discussed short-term load forecasting with a neural network model including multilayer perceptron neural network for prediction. A case study and input data were also presented. Pre-processing of the input data and post-processing of the output data were discussed. A description of the model was then provided. Error measurement of the forecasted curves and tests that were conducted were also outlined. It was concluded that it is possible to accurately forecast the global daily load curve from the load and temperature data of the previous three months. It is critical to choose the number of hidden neurons, training epochs and the adequate training algorithm. 18 refs., 5 tabs., 5 figs.
Identification and control of plasma vertical position using neural network in Damavand tokamak
Rasouli, H.; Rasouli, C.; Koohi, A.
2013-02-01
In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.
Identification and control of plasma vertical position using neural network in Damavand tokamak
International Nuclear Information System (INIS)
In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg–Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.
Neural Network Methods for Boundary Value Problems Defined in Arbitrarily Shaped Domains
Lagaris, I E; Papageorgiou, D G
1998-01-01
Partial differential equations (PDEs) with Dirichlet boundary conditions defined on boundaries with simple geomerty have been succesfuly treated using sigmoidal multilayer perceptrons in previous works. This article deals with the case of complex boundary geometry, where the boundary is determined by a number of points that belong to it and are closely located, so as to offer a reasonable representation. Two networks are employed: a multilayer perceptron and a radial basis function network. The later is used to account for the satisfaction of the boundary conditions. The method has been succesfuly tested on two-dimensional and three-dimensional PDEs and has yielded accurate solutions.
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...... damage, is essentially a pattern recognition problem.......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...
Akhbardeh, Alireza; Junnila, Sakari; Koivistoinen, Teemu; Värri, Alpo
2007-02-01
This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and Multi Layer Perceptrons (MLP) neural networks over Ballistocardiogram (BCG) signal recognition. To extract essential features of the BCG signal, we applied Biorthogonal wavelets. SF-ART performs classification on two levels. At first level, pre-classifier which is self-organized fuzzy ART tuned for fast learning classifies the input data roughly to arbitrary (M) classes. At the second level, post-classification level, a special array called Affine Look-up Table (ALT) with M elements stores the labels of corresponding input samples in the address equal to the index of fuzzy ART winner. However, in running (testing) mode, the content of an ALT cell with address equal to the index of fuzzy ART winner output will be read. The read value declares the final class that input data belongs to. In this paper, we used two well-known patterns (IRIS and Vowel data) and a medical application (Ballistocardiogram data) to evaluate and check SF-ART stability, reliability, learning speed and computational load. Initial tests with BCG from six subjects (both healthy and unhealthy people) indicate that the SF-ART is capable to perform with a high classification performance, high learning speed (elapsed time for learning around half second), and very low computational load compared to the well-known neural networks such as MLP which needs minutes to learn the training material. Moreover, to extract essential features of the BCG signal, we applied Biorthogonal wavelets. The applied wavelet transform requires no prior knowledge of the statistical distribution of data samples. PMID:17283924
Data acquisition in modeling using neural networks and decision trees
Directory of Open Access Journals (Sweden)
R. Sika
2011-04-01
Full Text Available The paper presents a comparison of selected models from area of artificial neural networks and decision trees in relation with actualconditions of foundry processes. The work contains short descriptions of used algorithms, their destination and method of data preparation,which is a domain of work of Data Mining systems. First part concerns data acquisition realized in selected iron foundry, indicating problems to solve in aspect of casting process modeling. Second part is a comparison of selected algorithms: a decision tree and artificial neural network, that is CART (Classification And Regression Trees and BP (Backpropagation in MLP (Multilayer Perceptron networks algorithms.Aim of the paper is to show an aspect of selecting data for modeling, cleaning it and reducing, for example due to too strong correlationbetween some of recorded process parameters. Also, it has been shown what results can be obtained using two different approaches:first when modeling using available commercial software, for example Statistica, second when modeling step by step using Excel spreadsheetbasing on the same algorithm, like BP-MLP. Discrepancy of results obtained from these two approaches originates from a priorimade assumptions. Mentioned earlier Statistica universal software package, when used without awareness of relations of technologicalparameters, i.e. without user having experience in foundry and without scheduling ranks of particular parameters basing on acquisition, can not give credible basis to predict the quality of the castings. Also, a decisive influence of data acquisition method has been clearly indicated, the acquisition should be conducted according to repetitive measurement and control procedures. This paper is based on about 250 records of actual data, for one assortment for 6 month period, where only 12 data sets were complete (including two that were used for validation of neural network and useful for creating a model. It is definitely too small portion in case of artificial neural networks, but it shows a scale of danger of unprofessional data acquisition.
Neural network based method for conversion of solar radiation data
International Nuclear Information System (INIS)
Highlights: ? Generalized regression neural network is used to predict the solar radiation on tilted surfaces. ? The above network, amongst many such as multilayer perceptron, is the most successful one. ? The present neural network returns a relative mean absolute error value of 9.1%. ? The present model leads to a mean absolute error value of estimate of 14.9 Wh/m2. - Abstract: The receiving ends of the solar energy conversion systems that generate heat or electricity from radiation is usually tilted at an optimum angle to increase the solar incident on the surface. Solar irradiation data measured on horizontal surfaces is readily available for many locations where such solar energy conversion systems are installed. Various equations have been developed to convert solar irradiation data measured on horizontal surface to that on tilted one. These equations constitute the conventional approach. In this article, an alternative approach, generalized regression type of neural network, is used to predict the solar irradiation on tilted surfaces, using the minimum number of variables involved in the physical process, namely the global solar irradiation on horizontal surface, declination and hour angles. Artificial neural networks have been successfully used in recent years for optimization, prediction and modeling in energy systems as alternative to conventional modeling approaches. To show the merit of the presently developed neural network, the solar irradiation data predicted from the novel model was compared to that from the conventional approach (isotropic and anisotropic models), with strict reference to the irradiation data measured in the same location. The present neural network model was found to provide closer solar irradiation values to the measured than the conventional approach, with a mean absolute error value of 14.9 Wh/m2. The other statistical values of coefficient of determination and relative mean absolute error also indicate the advantage of the neural network approach over the conventional one. In terms of the coefficient of determination, the neural network model results in a value of 0.987 whereas the isotropic and anisotropic approaches result in values of 0.959 and 0.966, respectively. On the other hand, the isotropic and anisotropic approaches give relative mean absolute error values of 11.4% and 11.5%, respectively, while that of the neural network model is 9.1%
On-line learning of non-monotonic rules by simple perceptron
Inoue, J; Kabashima, Yoshiyuki; Inoue, Jun-ichi; Nishimori, Hidetoshi; Kabashima, Yoshiyuki
1997-01-01
We study the generalization ability of a simple perceptron which learns unlearnable rules. The rules are presented by a teacher perceptron with a non-monotonic transfer function. The student is trained in the on-line mode. The asymptotic behaviour of the generalization error is estimated under various conditions. Several learning strategies are proposed and improved to obtain the theoretical lower bound of the generalization error.
Convergence Analysis of Adaptive Recurrent Neural Network
Hong Li; Ali Setoodehnia
2014-01-01
This paper presents analysis of a modified Feed Forward Multilayer Perceptron (FMP) by inserting an ARMA (Auto Regressive Moving Average) model at each neuron (processor node) with the Backp ropagation learning algorithm. The stability analysis is presented to establish the convergence theory of the Back propagation algorithm based on the Lyapunov function. Furthermore, the analysis extends the Back propagation learning rule by introducing the adaptive learning factors. A rang...
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hojat moayedi rad
2012-02-01
Full Text Available Due to simplicity and low cost, induction motors are more useful than direct current motors. Hence the control of these motors is important. The pervious methods are fitted normally for a limited speed range and could not be used for high, low and very low speeds. The voltage model is suitable for high speed because the voltage drop of stator resistance is not small in low speed. The current model is suitable for low speed because of the problems of flux saturation at high speed. This research presents a new method of PWM pulse generating in induction motors based on artificial neural networks in which, the switching pulses are generated by a multilayer feed-forward neural network that is trained by the voltage and current references. Also, for the estimation of required torque and flux information a multilayer perceptron is used. By application of this new method, there is no problem of stability at low and high speeds. The simulation results by matlab-simulink verify the proposed method in transient and steady-state operating modes.
Souza Reinaldo C.; Villa FernÃ¡n Alonso; VelÃ¡squez Juan David
2010-01-01
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 art...
A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies
Akhoondzadeh, M.
2013-06-01
Anomaly detection is extremely important for earthquake parameters estimation. In this paper, an application of Artificial Neural Networks (ANNs) in the earthquake precursor's domain has been developed. This study is concerned with investigating the Total Electron Content (TEC) time series by using a Multi-Layer Perceptron (MLP) neural network to detect seismo-ionospheric anomalous variations induced by the powerful Tohoku earthquake of March 11, 2011.The duration of TEC time series dataset is 120 days at time resolution of 2 h. The results show that the MLP presents anomalies better than referenced and conventional methods such as Auto-Regressive Integrated Moving Average (ARIMA) technique. In this study, also the detected TEC anomalies using the proposed method, are compared to the previous results (Akhoondzadeh, 2012) dealing with the observed TEC anomalies by applying the mean, median, wavelet and Kalman filter methods. The MLP detected anomalies are similar to those detected using the previous methods applied on the same case study. The results indicate that a MLP feed-forward neural network can be a suitable non-parametric method to detect changes of a non linear time series such as variations of earthquake precursors.
Hariharan, M; Sindhu, R; Yaacob, Sazali
2012-11-01
Crying is the most noticeable behavior of infancy. Infant cry signals can be used to identify physical or psychological status of an infant. Recently, acoustic analysis of infant cry signal has shown promising results and it has been proven to be an excellent tool to investigate the pathological status of an infant. This paper proposes short-time Fourier transform (STFT) based time-frequency analysis of infant cry signals. Few statistical features are derived from the time-frequency plot of infant cry signals and used as features to quantify infant cry signals. General Regression Neural Network (GRNN) is employed as a classifier for discriminating infant cry signals. Two classes of infant cry signals are considered such as normal cry signals and pathological cry signals from deaf infants. To prove the reliability of the proposed features, two neural network models such as Multilayer Perceptron (MLP) and Time-Delay Neural Network (TDNN) trained by scaled conjugate gradient algorithm are also used as classifiers. The experimental results show that the GRNN classifier gives very promising classification accuracy compared to MLP and TDNN and the proposed method can effectively classify normal and pathological infant cries. PMID:21824676
International Nuclear Information System (INIS)
In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer â€˜occurredâ€™ and transfer â€˜not occurredâ€™. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies. (condensed matter: structural, mechanical, and thermal properties)
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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.
International Nuclear Information System (INIS)
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 were obtained, indicating that the Neural Network can be considered an interesting alternative among the neutron spectrum unfolding methodologies. (author)
Energy demand estimation of South Korea using artificial neural network
International Nuclear Information System (INIS)
Because South Korea's industries depend heavily on imported energy sources (fifth largest importer of oil and second largest importer of liquefied natural gas in the world), the accurate estimating of its energy demand is critical in energy policy-making. This research proposes an artificial neural network model (a structure with feed-forward multilayer perceptron, error back-propagation algorithm, momentum process, and scaled data) to efficiently estimate the energy demand for South Korea. The model has four independent variables, such as gross domestic product (GDP), population, import, and export amounts. The data are obtained from diverse local and international sources. The proposed model better estimated energy demand than a linear regression model (a structure with multiple linear variables and least square method) or an exponential model (a structure with mixed integer variables, branch and bound method, and Broyden-Fletcher-Goldfarb-Shanno (BFGS) method) in terms of root mean squared error (RMSE). The model also forecasted better than the other two models in terms of RMSE without any over-fitting problem. Further testing with four scenarios based upon reliable source data showed unanticipated results. Instead of growing permanently, the energy demands peaked at certain points, and then decreased gradually. This trend is quite different from the results by regression or exponential model.
AN EFFICIENT NEURAL NETWORK FOR RECOGNIZING GESTURAL HINDI DIGITS
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Nidal Fawzi Shilbayeh
2013-01-01
Full Text Available Handwritten Hindi digit recognition plays an important role in eastern Arab countries especially in the courtesy amounts of Arab bank checks, recognizing numbers in car plates, or in postal code for mail sorting. In our study, we proposed an efficient Hindi Digit Recognition System drawn by the mouse and developed using Multilayer Perceptron Neural Network (MLP with backpropagation. The system has been designed, implemented and tested successfully. Analysis has been carried out to determine the number of hidden nodes that achieves high performance. The proposed system has been trained on samples of 800 images and tested on samples of 300 images written by different users selected from different ages. An experimental result shows high accuracy of about 91% on the testing samples and very close to 100% on the training samples. Experiments showed that our result is high in comparison with other Hindi digit recognition systems especially if we consider the way of writing (mouse and children in our trained and tested results.
Monthly monsoon rainfall forecasting using artificial neural networks
Ganti, Ravikumar
2014-10-01
Indian agriculture sector heavily depends on monsoon rainfall for successful harvesting. In the past, prediction of rainfall was mainly performed using regression models, which provide reasonable accuracy in the modelling and forecasting of complex physical systems. Recently, Artificial Neural Networks (ANNs) have been proposed as efficient tools for modelling and forecasting. A feed-forward multi-layer perceptron type of ANN architecture trained using the popular back-propagation algorithm was employed in this study. Other techniques investigated for modeling monthly monsoon rainfall include linear and non-linear regression models for comparison purposes. The data employed in this study include monthly rainfall and monthly average of the daily maximum temperature in the North Central region in India. Specifically, four regression models and two ANN model's were developed. The performance of various models was evaluated using a wide variety of standard statistical parameters and scatter plots. The results obtained in this study for forecasting monsoon rainfalls using ANNs have been encouraging. India's economy and agricultural activities can be effectively managed with the help of the availability of the accurate monsoon rainfall forecasts.
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.
Hidden Conditional Neural Fields for Continuous Phoneme Speech Recognition
Fujii, Yasuhisa; Yamamoto, Kazumasa; Nakagawa, Seiichi
In this paper, we propose Hidden Conditional Neural Fields (HCNF) for continuous phoneme speech recognition, which are a combination of Hidden Conditional Random Fields (HCRF) and a Multi-Layer Perceptron (MLP), and inherit their merits, namely, the discriminative property for sequences from HCRF and the ability to extract non-linear features from an MLP. HCNF can incorporate many types of features from which non-linear features can be extracted, and is trained by sequential criteria. We first present the formulation of HCNF and then examine three methods to further improve automatic speech recognition using HCNF, which is an objective function that explicitly considers training errors, provides a hierarchical tandem-style feature and includes a deep non-linear feature extractor for the observation function. We show that HCNF can be trained realistically without any initial model and outperforms HCRF and the triphone hidden Markov model trained by the minimum phone error (MPE) manner using experimental results for continuous English phoneme recognition on the TIMIT core test set and Japanese phoneme recognition on the IPA 100 test set.
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.
Scientific Electronic Library Online (English)
Juan David, Velásquez Henao; Mario Alberto, Aldana Dumar.
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. Abstract in english 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.
Scientific Electronic Library Online (English)
JUAN DAVID, VELÁSQUEZ HENAO; SANTIAGO FERNANDO, MONTOYA MORENO.
2005-11-01
Full Text Available Un nuevo modelo híbrido es propuesto para pronosticar el índice colombiano de precios al consumidor. Este es basado en una descomposición estructural de la serie temporal con el objetivo de remover cualquier patrón fácilmente detectable en los datos, y en el uso de un perceptron multicapa para model [...] ar las relaciones ocultas en la serie de tiempo. Los resultados superan las aproximaciones clásicas basadas en la aproximación de Box y Jenkins, y los modelos convencionales de Redes Neuronales, e incentivan el estudio de este tipo de aproximación híbrida para modelar otras series temporales. Abstract in english A new hybrid model is proposed to forecasting the Colombian Consumer Price Index. It’s based on the structural decomposition of the original time series with the aim of remove any easily detected pattern in the data, and in the use of multilayer perceptron to model hidden relationships in the studie [...] d time series. The results overcome classical approaches based on Box-Jenkins methodology and conventional neural networks methodology, and encourage the study of this hybrid approach to modelling other time series.
Effect of Heat Fluxes on Ammonia Emission from Swine Waste Lagoon Based on Neural Network Analyses
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N. Lovanh
2014-01-01
Full Text Available Understanding factors that affect ammonia emissions from swine waste lagoons or any animal waste receptacles is a necessary first step in deploying potential remediation options. In this study, we examined the various meteorological factors (i.e., air temperatures, solar radiation and heat fluxes that potentially affect ammonia emissions from swine waste lagoon. Ammonia concentrations were monitored using a photoacoustic gas analyzer. The ammonia emissions from the lagoon were monitored continuously for a 24 h cycle, twice a week during a winter month at a height of 50 cm above the lagoon surface. Meteorological data were also monitored simultaneously. Heat fluxes were tabulated and correlated to the averaged ammonia concentrations (range of zero to 8.0 ppmv. Multi-layer Perceptron (MLP neural network predictive model was built based on the most important meteorological parameters. The results from MLP neural networks analysis show that ammonia emissions from the swine waste lagoon were affected by heat fluxes such as net solar radiation, sensible heat and latent heat of vaporization. Thus it is important to consider environmental conditions (i.e., meteorological parameters such as solar radiation, latent heat and etc. in formulating management or abatement strategies for reducing ammonia emissions from swine waste lagoons or any other air pollutant emissions from livestock waste receptacles.
International Nuclear Information System (INIS)
Numerical simulation of natural circulation boiling water reactor is important in order to study its performance for different designs and under various off-design conditions. Numerical simulations can be performed by using thermal-hydraulic codes. Very fast numerical simulations, useful for extensive parametric studies and for solving design optimization problems, can be achieved by using an artificial neural network (ANN) model of the system. In the present work, numerical simulations of natural circulation boiling water reactor have been performed with RELAP5 code for different values of design parameters and operational conditions. Parametric trends observed have been discussed. The data obtained from these simulations have been used to train artificial neural networks, which in turn have been used for further parametric studies and design optimization. The ANN models showed error within ±5% for all the simulated data. Two most popular methods, multilayer perceptron (MLP) and radial basis function (RBF) networks, have been used for the training of ANN model. Sequential quadratic programming (SQP) has been used for optimization
International Nuclear Information System (INIS)
A nuclear power plant's (NPP's) status is usually monitored by a human operator. Any classifier system used to enhance the operator's capability to diagnose a safety-critical system like an NPP should classify a novel transient as ''don't-know'' if it is not contained within its accumulated knowledge base. In particular, the classifier needs some kind of proximity measure between the new data and its training set. Artificial neural networks have been proposed as NPP classifiers, the most popular ones being the multilayered perceptron (MLP) type. However, MLPs do not have a proximity measure, while learning vector quantization, probabilistic neural networks (PNNs), and some others do. This proximity measure may also serve as an explanation to the classifier's decision in the way that case-based-reasoning expert systems do. The capability of a PNN network as a classifier is demonstrated using simulator data for the three-loop 436-MW(electric) Westinghouse San Onofre unit 1 pressurized water reactor. A transient's classification history is used in an ''evidence accumulation'' technique to enhance a classifier's accuracy as well as its consistency
On-line control of the COMPASS-D tokamak using a neural network
International Nuclear Information System (INIS)
Multi-layer perceptron (MLP) networks are particularly appropriate for performing rapid non-linear mapping. In the application discussed in this Paper the position and shape of the plasma within the experimental fusion research tokamak COMPASS-D at UKAEA's Culham Laboratory is determined from a series of magnetic sensors placed around the vacuum vessel, close to the plasma boundary. By using a real-time analogue neural network it is possible to achieve control within a sub-millisecond time-scale. In this application the neural network is needed to solve an inverse problem. Numerical codes exist that are able to calculate the signals expected on the magnetic sensors for a given plasma position and profile. The problem is well defined from the solution of the Grad-Shafranov equation. However, no easy analytical formalism exists to reverse the problem - to calculate the plasma parameters given the magnetic signals. It is this mapping, from the set of magnetic diagnostic input signals to the parameters of the plasma, that an MLP network can be trained to solve. The training data are some 2000 example plasma equilibria, covering the likely possible configurations of the plasma, solved by numerical methods. The desired aim, to control the plasma boundary position to within a few millimetres, has now been achieved. (author)
Evaluation of oil thickness by neural network analysis of IR imagery
International Nuclear Information System (INIS)
The feasibility of using neural network analysis of conventional thermal infra-red data gathered from surveillance aircraft to determine the thickness of oil at sea, was examined. Sea trial data was examined using Multi-Layer Perceptron neural network architecture, based on indications that it was the most appropriate configuration for determining oil thickness. Core input variables included oil brightness, time of day, sea brightness, wind speed, oil type, and sea temperature. Other variables, such as altitude, wave height, air temperature, camera gain, and others, did not appear to produce any significant difference in the prediction performance. By using only a restricted sea trial data set in training the network, it was found that it was possible to correctly classify about 80 per cent of the data into one of four thickness classes. Since there was no additional data available to validate the network, these results were considered encouraging, but not definitive. Additional data will be collected in planned future sea trials to further evaluate the accuracy of the trained network. 4 refs., 6 tabs., 4 figs
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Chakraborty Prithviraj
2015-01-01
Full Text Available This study aimed to apply the simultaneous optimization method incorporating artificial neural network (ANN using multi-layer perceptron (MLP model to develop buccoadhesive pharmaceutical wafers containing loratadine with an optimized physicochemical property and drug release. The amount of sodium carboxymethyl cellulose and lactose monohydrate at three levels (?1, 0, +1 for each was selected as casual factors. Bioadhesive strength, disintegration time, percent swelling index and t 70% as wafer properties were selected as output variables. Nine buccoadhesive wafers were prepared according to a 3 2 factorial design and their physicochemical property and dissolution tests were performed. Commercially available Statistica Neural Network Software (Stat Soft, Inc., Tulsa, OK, USA was used throughout the study. The training process of MLP was completed until a satisfactory value of root mean square for the test data was obtained using back propagation, conjugate gradient descent method. This work exemplifies the probability for an ANN with MLP, to support in development of buccoadhesive wafers with enviable characteristics.
Marmo, Roberto; Amodio, Sabrina; Tagliaferri, Roberto; Ferreri, Vittoria; Longo, Giuseppe
2005-06-01
Using more than 1000 thin section photos of ancient (Phanerozoic) carbonates from different marine environments (pelagic to shallow-water) a new numerical methodology, based on digitized images of thin sections, is proposed here. In accordance with the Dunham classification, it allows the user to automatically identify carbonate textures unaffected by post-depositional modifications (recrystallization, dolomitization, meteoric dissolution and so on). The methodology uses, as input, 256 grey-tone digital image and by image processing gives, as output, a set of 23 values of numerical features measured on the whole image including the "white areas" (calcite cement). A multi-layer perceptron neural network takes as input this features and gives, as output, the estimated class. We used 532 images of thin sections to train the neural network, whereas to test the methodology we used 268 images taken from the same photo collection and 215 images from San Lorenzello carbonate sequence (Matese Mountains, southern Italy), Early Cretaceous in age. This technique has shown 93.3% and 93.5% of accuracy to classify automatically textures of carbonate rocks using digitized images on the 268 and 215 test sets, respectively. Therefore, the proposed methodology is a further promising application to the geosciences allowing carbonate textures of many thin sections to be identified in a rapid and accurate way. A MATLAB-based computer code has been developed for the processing and display of images.
Artificial neural networks for simulating wind effects on sprinkler distribution patterns
Energy Technology Data Exchange (ETDEWEB)
Sayyadi, H.; Sadraddini, A. A.; Farsadi Zadeh, D.; Montero, J.
2012-07-01
A new approach based on Artificial Neural Networks (ANNs) is presented to simulate the effects of wind on the distribution pattern of a single sprinkler under a center pivot or block irrigation system. Field experiments were performed under various wind conditions (speed and direction). An experimental data from different distribution patterns using a Nelson R3000 Rotator sprinkler have been split into three and used for model training, validation and testing. Parameters affecting the distribution pattern were defined. To find an optimal structure, various networks with different architectures have been trained using an Early Stopping method. The selected structure produced R2 0.929 and RMSE = 6.69 mL for the test subset, consisting of a Multi-Layer Perceptron (MLP) neural network with a backpropagation training algorithm; two hidden layers (twenty neurons in the first hidden layer and six neurons in the second hidden layer) and a tangent-sigmoid transfer function. This optimal network was implemented in MATLAB to develop a model termed ISSP (Intelligent Simulator of Sprinkler Pattern). ISSP uses wind speed and direction as input variables and is able to simulate the distorted distribution pattern from a R3000 Rotator sprinkler with reasonable accuracy (R{sup 2} > 0.935). Results of model evaluation confirm the accuracy and robustness of ANNs for simulation of a single sprinkler distribution pattern under real field conditions. (Author) 41 refs.
Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad
2016-01-01
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.
Foulkes, Stephen B.; Booth, David M.
1997-07-01
Object segmentation is the process by which a mask is generated which identifies the area of an image which is occupied by an object. Many object recognition techniques depend on the quality of such masks for shape and underlying brightness information, however, segmentation remains notoriously unreliable. This paper considers how the image restoration technique of Geman and Geman can be applied to the improvement of object segmentations generated by a locally adaptive background subtraction technique. Also presented is how an artificial neural network hybrid, consisting of a single layer Kohonen network with each of its nodes connected to a different multi-layer perceptron, can be used to approximate the image restoration process. It is shown that the restoration techniques are very well suited for parallel processing and in particular the artificial neural network hybrid has the potential for near real time image processing. Results are presented for the detection of ships in SPOT panchromatic imagery and the detection of vehicles in infrared linescan images, these being a fair representation of the wider class of problem.
Presenting an Appropriate Neural Network for Optimal Mix Design of Roller Compacted Concrete Dams
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Taha Mehmannavaz
2014-03-01
Full Text Available In general, one of the main targets to achieve the optimal mix design of concrete dams is to reduce the amount of cement, heat of hydration, increasing the size of aggregate (coarse and reduced the permeability. Thus, one of the methods which is used in construction of concrete and soil dams as a suitable replacement is construction of dams in roller compacted concrete method. Spending fewer budgets, using road building machinery, short time of construction and continuation of construction all are the specifications of this construction method, which have caused priority of these two methods and finally this method has been known as a suitable replacement for constructing dams in different parts of the world. On the other hand, expansion of the materials used in this type of concrete, complexity of its mix design, effect of different parameters on its mix design and also finding relations between different parameters of its mix design have necessitated the presentation of a model for roller compacted concretemix design. Artificial neural networks are one of the modeling methods which have shown very high power for adjustment to engineering problems. A kind of these networks, called Multi-Layer Perceptron (MLP neural networks, was used as the main core of modeling in this study along with error-back propagation training algorithm, which is mostly applied in modeling mapping behaviors.
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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.
Parinet, Julien; Julien, Maxime; Nun, Pierrick; Robins, Richard J; Remaud, Gerald; Höhener, Patrick
2015-09-01
We aim at predicting the effect of structure and isotopic substitutions on the equilibrium vapour pressure isotope effect of various organic compounds (alcohols, acids, alkanes, alkenes and aromatics) at intermediate temperatures. We attempt to explore quantitative structure property relationships by using artificial neural networks (ANN); the multi-layer perceptron (MLP) and compare the performances of it with multi-linear regression (MLR). These approaches are based on the relationship between the molecular structure (organic chain, polar functions, type of functions, type of isotope involved) of the organic compounds, and their equilibrium vapour pressure. A data set of 130 equilibrium vapour pressure isotope effects was used: 112 were used in the training set and the remaining 18 were used for the test/validation dataset. Two sets of descriptors were tested, a set with all the descriptors: number of(12)C, (13)C, (16)O, (18)O, (1)H, (2)H, OH functions, OD functions, CO functions, Connolly Solvent Accessible Surface Area (CSA) and temperature and a reduced set of descriptors. The dependent variable (the output) is the natural logarithm of the ratios of vapour pressures (ln R), expressed as light/heavy as in classical literature. Since the database is rather small, the leave-one-out procedure was used to validate both models. Considering higher determination coefficients and lower error values, it is concluded that the multi-layer perceptron provided better results compared to multi-linear regression. The stepwise regression procedure is a useful tool to reduce the number of descriptors. To our knowledge, a Quantitative Structure Property Relationship (QSPR) approach for isotopic studies is novel. PMID:25559176
Modeling of gamma-ray energy absorption buildup factors using neural network
International Nuclear Information System (INIS)
This paper presents a new approach based on multilayered perceptrons (MLPs) to compute energy absorption buildup factors. The MLP has been trained by a Levenberg-Marquardt learning algorithm. The model is fast and does not require tremendous computational efforts. The results obtained by using the proposed model are in good agreement with the ANSI/ANS-6.4.3 standard data set
Variants of Memetic and Hybrid Learning of Perceptron Networks.
Czech Academy of Sciences Publication Activity Database
Neruda, Roman; Slušný, Stanislav
Los Alamitos : IEEE, 2007 - (Tjoa, A.; Wagner, R.), s. 158-162 ISBN 978-0-7695-2932-5. [ETID '07. International Workshop on Evolutionary Techniques /1./, DEXA 2007 International Conference /18./. Regensburg (DE), 03.09.2007-07.09.2007] R&D Projects: GA AV ?R 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : memetic learning * evolutionary learning * neural networks Subject RIV: IN - Informatics, Computer Science
Abnormal Control Chart Pattern Classification Optimisation Using Multi Layered Perceptron
Mitra Mahdiani; Hairulliza Mohd. Judi; Noraidah Sahari Ashaari
2014-01-01
In today's industry, control charts are widely used to monitor production process. The abnormal patterns of a quality control chart could reveal problems that occur in the process. In the recent years, as an alternative of the traditional process quality management methods, such as Shewhart Statistical Process Control (SPC), Artificial Neural Networks (ANN) have been widely used to recognize the abnormal pattern of control charts. Various types of patterns are observed in control charts. Iden...
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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.
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Chennai Salim
2011-09-01
Full Text Available The increased use of nonlinear devices in the industry has resulted in the direct increase of harmonic distortion in power systems during these last years. Active filter systems are proposed to mitigate current harmonics generated by nonlinear loads. The conventional scheme based on a two-level voltage source inverter controlled by a hysteresis controller has several disadvantages and cannot be used for medium or high-power applications. To overcome these drawbacks and improve the APF performance, thereâ€™s a great tendency to use multilevel inverters controlled by intelligent controllers. Three level (NPC inverter is one of the most widely used topologies in various industrial applications such as machine drives and power factor compensators. On the other hand, artificial neural networks are under study and investigation in other power electronics applications. In order to gain the advantages of the three-level inverter and artificial neural networks and to reduce the complexity of classical control schemes, a new active power filter configuration controlled by two MLPNN (Multi-Layer Perceptron Neural Network is proposed in this paper. The first ANN is used to replace the PWM current controller, and the second one to maintain a constant dc link voltage across the capacitors and compensate the inverter power losses. The performance of the global system, including power and control circuits is evaluated by Matlab-Simulink and SimPowerSystem Toolbox simulation. The obtained results confirm the effectiveness of the proposed control scheme.
Scientific Electronic Library Online (English)
Jesús D., Villalba; Ivan D., Gómez; José E., Laier.
2012-06-01
Full Text Available En este trabajo se presenta una red neuronal perceptron multicapa combinada con el método Nelder-Mead Simplex para detectar daño en vigas. Los parámetros de entrada a la red se basan en frecuencias naturales y flexibilidad modal. Se considera que solo una cantidad específica de modos fueron identifi [...] cados y que se dispone de mediciones en grados de libertad verticales. La confiabilidad de la metodología propuesta se evalúa a partir de escenarios de daño aleatorios y de la definición de 3 tipos de error que la red puede cometer durante el proceso de detección del daño. Los resultados muestran que la metodología puede determinar confiablemente los escenarios de daño buscados. Sin embargo, su aplicación a vigas de gran tamaño puede verse limitada por el elevado costo computacional asociado al entrenamiento de la red. Abstract in english In this paper is presented a multilayer perceptron neural network combined with the Nelder-Mead Simplex method to detect damage in multiple support beams. The input parameters are based on natural frequencies and modal flexibility. It was considered that only a number of modes were available and tha [...] t only vertical degrees of freedom were measured. The reliability of the proposed methodology is assessed from the generation of random damages scenarios and the definition of three types of errors, which can be found during the damage identification process. Results show that the methodology can reliably determine the damage scenarios. However, its application to large beams may be limited by the high computational cost of training the neural network.
Baghdadi, N.; Cresson, R.; El Hajj, M.; Ludwig, R.; La Jeunesse, I.
2012-03-01
The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on Multi-Layer Perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases in using or not a priori knowledge on soil parameters. The inversion approach was then validated in using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters ?1 and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm3 cm-3) and surface roughness (lower or higher than 1.5 cm). Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 without a priori information on soil parameters and 0.065 cm3 cm-3 (RMSE) applying a priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm). Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with a RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.
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.
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M. Aquilino
2014-01-01
The historical archive of LANDSAT imagery dating back to the launch of ERTS in 1972 provides a comprehensive and permanent data source for tracking change on the planetâ€Ÿs land surface. In this study case the imagery acquisition dates of 1987, 2002 and 2011 were selected to cover a time trend of 24 years. Land cover categories were based on classes outlined by the Curve Number method with the aim of characterizing land use according to the level of surface imperviousness. After comparing two land use classification methods, i.e. Maximum Likelihood Classifier (MLC and Multi-Layer Perceptron (MLP neural network, the Artificial Neural Networks (ANN approach was found the best reliable and efficient method in the absence of ground reference data. The ANN approach has a distinct advantage over statistical classification methods in that it is non-parametric and requires little or no a priori knowledge on the distribution model of input data. The results quantify land cover change patterns in the river basin area under study and demonstrate the potential of multitemporal LANDSAT data to provide an accurate and cost-effective means to map and analyse land cover changes over time that can be used as input in land management and policy decision-making.
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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.
Scientific Electronic Library Online (English)
Sandra P, Mateus; Natalia, González; John W, Branch.
Full Text Available En este trabajo se presenta la creación de dos Entornos Virtuales Inteligentes (EVI) con Redes Neuronales Artificiales (RNA). En un EVI se realiza el diagnóstico de problemas visuales como astigmatismo, miopía e hipermetropía. El otro se enfoca, en la percepción y el razonamiento de señales de adver [...] tencia en un entorno laboral. En el desarrollo del trabajo, se hace primero una caracterización de las Redes Neuronales Artificiales y luego se hace una simulación de ellas; de acuerdo a los resultados obtenidos, se selecciona una arquitectura de red (Perceptrón Multicapa) y ésa es la que se implementa en los EVI. Finalmente se abordan las limitantes del tiempo en el aprendizaje de la RNA y en el costo computacional aplicados a los EVI. Abstract in english In this paper, the creation and application of two Intelligent Virtual Environments (IVE) with Artificial Neural Networks (ANN) are presented. In one EVI, the diagnosis of vision problems like astigmatism, myopia and hyperopia is studied. The other one focuses to the perception and reasoning of warn [...] ing signals in a work environment. For the development of this paper, the characterization of Artificial Neural Networks is done, followed by the simulation; according to the results one network architecture is selected (Multilayer Perceptron) and then implemented in the IVE. Finally the time constraints in ANN learning and in computational cost applied to IVE are discussed.
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...
International Nuclear Information System (INIS)
In this research, feed forward ANN (Artificial Neural Network) model is developed and validated for predicting the pH at 10 different locations of the distribution system of drinking water of Hyderabad city. The developed model is MLP (Multilayer Perceptron) with back propagation algorithm. The data for the training and testing of the model are collected through an experimental analysis on weekly basis in a routine examination for maintaining the quality of drinking water in the city. 17 parameters are taken into consideration including pH. These all parameters are taken as input variables for the model and then pH is predicted for 03 phases;raw water of river Indus,treated water in the treatment plants and then treated water in the distribution system of drinking water. The training and testing results of this model reveal that MLP neural networks are exceedingly extrapolative for predicting the pH of river water, untreated and treated water at all locations of the distribution system of drinking water of Hyderabad city. The optimum input and output weights are generated with minimum MSE (Mean Square Error) < 5%. Experimental, predicted and tested values of pH are plotted and the effectiveness of the model is determined by calculating the coefficient of correlation (R2=0.999) of trained and tested results. (author)
Scientific Electronic Library Online (English)
Rodrigo Mikosz, Gonçalves; Leandro dos Santos, Coelho; Claudia Pereira, Krueger; Bernhard, Heck.
2010-09-01
Full Text Available Estudar modelagens através de dados geodésicos temporais com a possibilidade de predizer a posição de linha de costa é uma tarefa importante e pode auxiliar significativamente na gestão costeira. A área de estudo neste trabalho se refere ao município de Matinhos no estado do Paraná, Brasil. As linha [...] s de costa temporais utilizadas para testar a modelagem preditiva são provenientes respectivamente da fotogrametria analógica para anos 1954, 1963, 1980, 1991 e 1997 e de levantamentos geodésicos utilizando GPS (Global Position System) para 2001, 2002, 2005 e 2008 (como controle). Dois testes com as redes neurais artificiais foram organizados mudando alguns parâmetros como: arquitetura, número de neurônios nas camadas ocultas e algoritmos de treinamentos. Quando comparados o valor dos resíduos entre a predição e a linha de costa de controle, os melhores resultados estatísticos indicam que o MAPE (mean absolute percentage error) são 0,28% utilizando a rede neural parcialmente recorrente de Elman com o algoritmo de treinamento quase-Newton e 0,46% para o caso da rede neural perceptron multicamadas com o algoritmo de treinamento utilizando o método Bayesiano com regularização. Abstract in english The study of models using geodetic temporal data which can possibly predict the shoreline position is an important task and can significantly contribute to coastal management. The studied area is located at municipality of Matinhos in the Paraná State, Brazil. The temporal shoreline used to test the [...] prediction model is respectively from analog photogrammetric data, related to the years 1954, 1963, 1980, 1991 and 1997, and GPS (Global Position System) geodetic surveys for 2001, 2002, 2005 and 2008 (as control). Two different tests with artificial neural network were organized setting the parameters like: architecture, number of neuron in hidden layers and the training algorithms. Comparing the residuals between the prediction to the shoreline of control, the best statistical results show the MAPE (Mean Absolute Percentage Error) is 0,28% using the Elman partially recurrent network with quasi-Newton training function and 0,46% using the neural network multilayer perceptron with Bayesian regulation training function.
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Teodorico Alves Sobrinho
2011-06-01
Full Text Available A estimativa da evapotranspiração por métodos indiretos propicia, de modo facilitado, a geração de dados para o planejamento de sistemas de irrigação e aplicação de modelos meteorológicos e hidrológicos, ambos, úteis na gestão de bacias hidrográficas. O objetivo deste trabalho foi elaborar uma Rede Neural Artificial (RNA para estimar a evapotranspiração de referência (Eto em função de dados diários de temperatura do ar. A RNA, do tipo FeedForward Multilayer Perceptron, foi treinada tomando-se por referência a Eto diária obtida pelo método de Penman-Monteith. Nas camadas intermediárias e de saída foram utilizadas funções de ativação do tipo tan-sigmóide e lineares, respectivamente. Os valores de Eto gerados pela RNA foram comparados com os obtidos pelos métodos de Blanney-Criddle e Hargreaves considerando meses referentes às quatro estações do ano. Em relação aos outros métodos analisados, os resultados obtidos a partir da RNA foram mais próximos ao método padrão Penman-Monteith. Assim, o desempenho da RNA desenvolvida foi satisfatório, podendo-se considerá-la como integrante do conjunto de métodos indiretos para estimativa da evapotranspiração, além de representar uma diminuição dos custos de aquisição de dados para estimativa desta variável.The estimation of evapotranspiration by indirect methods provides synthetic data for planning irrigation systems and application on meteorological and hydrological models, both useful in watershed management. The objective of this study was to develop an Artificial Neural Network (ANN to estimate the reference evapotranspiration (Eto based on daily air temperature data. The ANN model of Feedforward Multilayer Perceptron type, was trained using as a reference the daily Eto obtained by the Penman-Monteith method. In the intermediate and output layers were used activation functions like tan-sigmoid and linear, respectively. Eto values generated by ANN were compared with those obtained by the methods of Blanney-Criddle and Hargreaves considering the months of the four seasons. Comparing to the other analyzed methods, the results obtained from the ANN were closer to the standard Penman-Monteith method. Thus, the performance of the developed ANN was satisfactory, and the ANN model can be considered as one indirect method for estimating evapotranspiration and allows a cost reduction on data acquisition to estimate this variable.
Scientific Electronic Library Online (English)
Teodorico, Alves Sobrinho; Dulce Buchala Bicca, Rodrigues; Paulo Tarso Sanches de, Oliveira; Lais Cristina Soares, Rebucci; Caroline Alvarenga, Pertussatti.
2011-06-01
Full Text Available A estimativa da evapotranspiração por métodos indiretos propicia, de modo facilitado, a geração de dados para o planejamento de sistemas de irrigação e aplicação de modelos meteorológicos e hidrológicos, ambos, úteis na gestão de bacias hidrográficas. O objetivo deste trabalho foi elaborar uma Rede [...] Neural Artificial (RNA) para estimar a evapotranspiração de referência (Eto) em função de dados diários de temperatura do ar. A RNA, do tipo FeedForward Multilayer Perceptron, foi treinada tomando-se por referência a Eto diária obtida pelo método de Penman-Monteith. Nas camadas intermediárias e de saída foram utilizadas funções de ativação do tipo tan-sigmóide e lineares, respectivamente. Os valores de Eto gerados pela RNA foram comparados com os obtidos pelos métodos de Blanney-Criddle e Hargreaves considerando meses referentes às quatro estações do ano. Em relação aos outros métodos analisados, os resultados obtidos a partir da RNA foram mais próximos ao método padrão Penman-Monteith. Assim, o desempenho da RNA desenvolvida foi satisfatório, podendo-se considerá-la como integrante do conjunto de métodos indiretos para estimativa da evapotranspiração, além de representar uma diminuição dos custos de aquisição de dados para estimativa desta variável. Abstract in english The estimation of evapotranspiration by indirect methods provides synthetic data for planning irrigation systems and application on meteorological and hydrological models, both useful in watershed management. The objective of this study was to develop an Artificial Neural Network (ANN) to estimate t [...] he reference evapotranspiration (Eto) based on daily air temperature data. The ANN model of Feedforward Multilayer Perceptron type, was trained using as a reference the daily Eto obtained by the Penman-Monteith method. In the intermediate and output layers were used activation functions like tan-sigmoid and linear, respectively. Eto values generated by ANN were compared with those obtained by the methods of Blanney-Criddle and Hargreaves considering the months of the four seasons. Comparing to the other analyzed methods, the results obtained from the ANN were closer to the standard Penman-Monteith method. Thus, the performance of the developed ANN was satisfactory, and the ANN model can be considered as one indirect method for estimating evapotranspiration and allows a cost reduction on data acquisition to estimate this variable.
Prediction of Rainfall in India using Artificial Neural Network (ANN) Models
Santosh Kumar Nanda; Debi Prasad Tripathy; Simanta Kumar Nayak; Subhasis Mohapatra
2013-01-01
In this paper, ARIMA(1,1,1) model and Artificial Neural Network (ANN) models like Multi Layer Perceptron (MLP), Functional-link Artificial Neural Network (FLANN) and Legendre Polynomial Equation ( LPE) were used to predict the time series data. MLP, FLANN and LPE gave very accurate results for complex time series model. All the Artificial Neural Network model results matched closely with the ARIMA(1,1,1) model with minimum Absolute Average Percentage Error(AAPE). Comparing the different ANN ...
Abnormal Control Chart Pattern Classification Optimisation Using Multi Layered Perceptron
Directory of Open Access Journals (Sweden)
Mitra Mahdiani
2014-06-01
Full Text Available In today's industry, control charts are widely used to monitor production process. The abnormal patterns of a quality control chart could reveal problems that occur in the process. In the recent years, as an alternative of the traditional process quality management methods, such as Shewhart Statistical Process Control (SPC, Artificial Neural Networks (ANN have been widely used to recognize the abnormal pattern of control charts. Various types of patterns are observed in control charts. Identification of these Control Chart Patterns (CCPs can provide clues to potential quality problems in the manufacturing process. Each type of control chart pattern has its own geometric shape and various related features can represent this shape. Feature-based approaches can facilitate efficient pattern recognition since extracted shape features represent the main characteristics of the patterns in a condensed form. The objective of this study was to evaluate the relative performance of a feature-based CCP recognizer compared with the raw data-based recognizer. The study focused on recognition of six commonly researched CCPs plotted on the Shewhart X-bar chart. The ANN-based CCP recognizer trained using the nine shape features resulted in significantly better performance and generalization compared with the raw data-based recognizer.
Land, Walker H., Jr.; Masters, Timothy D.; Lo, Joseph Y.; McKee, Dan
2001-07-01
A new neural network technology was developed for improving the benign/malignant diagnosis of breast cancer using mammogram findings. A new paradigm, Adaptive Boosting (AB), uses a markedly different theory in solutioning Computational Intelligence (CI) problems. AB, a new machine learning paradigm, focuses on finding weak learning algorithm(s) that initially need to provide slightly better than random performance (i.e., approximately 55%) when processing a mammogram training set. Then, by successive development of additional architectures (using the mammogram training set), the adaptive boosting process improves the performance of the basic Evolutionary Programming derived neural network architectures. The results of these several EP-derived hybrid architectures are then intelligently combined and tested using a similar validation mammogram data set. Optimization focused on improving specificity and positive predictive value at very high sensitivities, where an analysis of the performance of the hybrid would be most meaningful. Using the DUKE mammogram database of 500 biopsy proven samples, on average this hybrid was able to achieve (under statistical 5-fold cross-validation) a specificity of 48.3% and a positive predictive value (PPV) of 51.8% while maintaining 100% sensitivity. At 97% sensitivity, a specificity of 56.6% and a PPV of 55.8% were obtained.
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Santana Isabel
2011-08-01
Full Text Available Abstract Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI, but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.
Scientific Electronic Library Online (English)
Olanrewaju A, Oludolapo; Adisa A, Jimoh; Pule A, Kholopane.
Full Text Available In view of the close association between energy and economic growth, South Africa's aspirations for higher growth, more energy is required; formulating a long-term economic development plan and implementing an energy strategy for a country /industry necessitates establishing the correct relationship [...] between energy and the economy. As insufficient energy or a lack thereof is reported to be a major cause of social and economic poverty, it is very important to select a model to forecast the consumption of energy reasonably accurately. This study presents techniques based on the development of multilayer perceptron (MLP) and radial basis function (RBF) of artificial neural network (ANN) models, for calculating the energy consumption of South Africa's industrial sector between 1993 and 2000. The approach examines the energy consumption in relation to the gross domestic product. The results indicate a strong agreement between model predictions and observed values, since the mean absolute percentage error is below 5%. When performance indices are compared, the RBF-based model is a more accurate predictor than the MLP model.
Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels.
Feng, Qi; Wu, Shengjun; Du, Yun; Xue, Huaiping; Xiao, Fei; Ban, Xuan; Li, Xiaodong
2013-12-01
Fugitive dust deriving from construction sites is a serious local source of particulate matter (PM) that leads to air pollution in cities undergoing rapid urbanization in China. In spite of this fact, no study has yet been published relating to prediction of high levels of PM with diameters neural network models (multilayer perceptron, Elman, and support vector machine) in predicting daily PM10 IAQI one day in advance. To obtain acceptable forecasting accuracy, measured time series data were decomposed into wavelet representations and wavelet coefficients were predicted. Effectiveness of these forecasters were tested using a time series recorded between January 1, 2005, and December 31, 2011, at six monitoring stations situated within the urban area of the city of Wuhan, China. Experimental trials showed that the improved models provided low root mean square error values and mean absolute error values in comparison to the original models. In addition, these improved models resulted in higher values of coefficients of determination and AHPC (the accuracy rate of high PM10 IAQI caused by nearby construction activity) compared to the original models when predicting high PM10 IAQI levels attributable to fugitive dust from nearby construction sites. PMID:24381481
Determining the appropriate amount of anesthetic gas using DWT and EMD combined with neural network.
Co?kun, Mustafa; Gürüler, Hüseyin; Istanbullu, Ayhan; Peker, Musa
2015-01-01
The spectrum of EEG has been studied to predict the depth of anesthesia using variety of signal processing methods up to date. Those standard models have used the full spectrum of EEG signals together with the systolic-diastolic pressure and pulse values. As it is generally agreed today that the brain is in stable state and the delta-theta bands of the EEG spectrum remain active during anesthesia. Considering this background, two questions that motivates this paper. First, determining the amount of gas to be administered is whether feasable from the spectrum of EEG during the maintenance stage of surgical operations. Second, more specifically, the delta-theta bands of the EEG spectrum are whether sufficient alone for this aim. This research aims to answer these two questions together. Discrete wavelet transformation (DWT) and empirical mode decomposition (EMD) were applied to the EEG signals to extract delta-theta bands. The power density spectrum (PSD) values of target bands were presented as inputs to multi-layer perceptron (MLP) neural network (NN), which predicted the gas level. The present study has practical implications in terms of using less data, in an effective way and also saves time as well. PMID:25472730
Artificial Neural Network Based Equation to Estimate Head Loss Along Drip Irrigation Laterals
Directory of Open Access Journals (Sweden)
Acácio Perboni
2014-04-01
Full Text Available This work proposes an equation based on Artificial Neural Network (ANN to estimate head loss along emitting pipes accounting for cylindrical in-line emitters. The following input variables were used to fit the model: total head loss between two consecutive emitters; emitter spacing; internal diameter of the pipe; mean water velocity at uniform pipe sections; and, kinematic viscosity of water. The input data was obtained by experimental means and standardized from 0 to 1. Five replications and six distinct structures of ANNs multilayer perceptron (MLP were used during the training stage performed using the package neuralnet of the software R. A MLP structure consisting of six neurons at input layer, six neurons at hidden layer, and one neuron at output layer was applied for fitting the model. Estimated values by the ANN’s equation were compared to the estimated values by an equation based on dimensional analysis. The ANN’s equation and the equation based on dimensional analysis presented maximum deviations between measured and estimated values of 0.324 kPa and 1.647 kPa, respectively. Therefore the ANN’s equation presented better results than the equation based on dimensional analysis.
A Hybrid Model based on Neural Network and Hybrid Genetic Algorithm for Automotive Price Forecasting
Directory of Open Access Journals (Sweden)
M. Reza Peyghami
2011-06-01
Full Text Available In this paper, we introduce a new intelligent combination method based on Multilayer Perceptron Neural Network (MLP?NN and Hybrid Genetic Algorithm (HGA for automotive price forecasting. The combination of MLPNN and HGA lead us to accelerate convergence to the optimal weights and improve the forecasting performance. In this structure, the Levenberg? Marquardt (LM algorithm is employed for training of the network, and the hybridization of Genetic Algorithm (GA with some local search optimization techniques such as steepest descent (SD method and quasi?Newton methods with DFP and BFGS formula is used to perform HGA. We apply our new hybrid model to forecast the automotive prices in Iran Khodro Company which is the biggest automotive manufacturing in IRAN. Simulation results show the enough reduction in the processing iterations and forecasting error which is mean square error. The results are well promising compared to the cases when we apply MLP?NN or hybridization of MLP?NN and GA, individually.
/ Artificial neural networks (ANN): prediction of sensory measurements from instrumental data
Scientific Electronic Library Online (English)
Naiara Barbosa, Carvalho; Valéria Paula Rodrigues, Minim; Rita de Cássia dos Santos Navarro, Silva; Suzana Maria, Della Lucia; Luis Aantonio, Minim.
2013-12-01
Full Text Available [...] Abstract in english 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 ligh [...] t 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.
An Efficient Weather Forecasting System using a Hybrid Neural Network SOFM–MLP
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I.Kadar Shereef
2010-12-01
Full Text Available Weather prediction is a challenging task for researchers and has drawn a lot of research interest in the recent years. Literature studies have shown that machine learning techniques achieved better performance than traditional statistical methods. Presently multilayer perceptron networks (MLPs are used for prediction of the maximum and the minimum temperatures based on past observations on various atmospheric parameters. To capture the seasonality of atmospheric data, with a view to improving the prediction accuracy, a novel weather forecasting system is presented in this paper. The proposed system is based on a neural architecture that combines a selforganizing feature map (SOFM and MLPs to realize a hybrid network named SOFM–MLP. It is also demonstrated that the use of appropriate features such as temperature gradient can not only reduce the number of features drastically, but also can improve the prediction accuracy. These observations motivated us to use a feature selection MLP (FSMLP instead of MLP, which can select good features online while learning the prediction task. FSMLP is used as a preprocessor to select good features. The combined use of FSMLP and SOFM–MLP provides better result in a network system that uses only very few inputs but can produce good prediction. The proposed system is experimented using the real time data observations and from which it is found that the proposed system predict the temperature with minimum error.
Comparative Study of Artificial Neural Network and ARIMA Models in Predicting Exchange Rate
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karamollah Bagherifard
2012-11-01
Full Text Available Capital market as an organized market has an effective role in mobilizing financial resources due to have growth and economic development of countries and many countries now in the finance firms is responsible for the required credits. In the stock market, shareholders are always seeking the highest efficiency, so the stock price prediction is important for them. Since the stock market is a nonlinear system under conditions of political, economic and psychological, it is difficult to predict the correct stock price. Thus, in the present study artificial intelligence and ARIMA method has been used to predict stock prices. Multilayer Perceptron neural network and radial basis functions are two methods used in this research. Evaluation methods, selection methods and exponential smoothing methods are compared to random walk. The results showed that AI-based methods used in predicting stock performance are more accurate. Between two methods used in artificial intelligence, a method based on radial basis functions is capable to estimate stock prices in the future with higher accuracy.
Artificial Neural Network applied as a methodology of mosquito species identification.
Lorenz, Camila; Ferraudo, Antonio Sergio; Suesdek, Lincoln
2015-12-01
There are about 200 species of mosquitoes (Culicidae) known to be vectors of pathogens that cause diseases in humans. Correct identification of mosquito species is an essential step in the development of effective control strategies for these diseases; recognizing the vectors of pathogens is integral to understanding transmission. Unfortunately, taxonomic identification of mosquitoes is a laborious task, which requires trained experts, and it is jeopardized by the high variability of morphological and molecular characters found within the Culicidae family. In this context, the development of an automatized species identification method would be a valuable and more accessible resource to non-taxonomist and health professionals. In this work, an artificial neural network (ANN) technique was proposed for the identification and classification of 17 species of the genera Anopheles, Aedes, and Culex, based on wing shape characters. We tested the hypothesis that classification using ANN is better than traditional classification by discriminant analysis (DA). Thirty-two wing shape principal components were used as input to a Multilayer Perceptron Classification ANN. The obtained ANN correctly identified species with accuracy rates ranging from 85.70% to 100%, and classified species more efficiently than did the traditional method of multivariate discriminant analysis. The results highlight the power of ANNs to diagnose mosquito species and to partly automatize taxonomic identification. These findings also support the hypothesis that wing venation patterns are species-specific, and thus should be included in taxonomic keys. PMID:26394186
Use of artificial neural networks and geographic objects for classifying remote sensing imagery
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Gholamreza Asadollahfardi
2013-08-01
Full Text Available 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 MLP network was pH, discharge, sulfate, sodium, calcium, chloride, magnesium and bicarbonate, and output was predictive of the SAR. The results showed a correlation coefficient 0.976 between actual and predicted SAR, which means the accuracy of the model is acceptable. The model uses the input parameters to predict the SAR at the same month. The sensitivity analysis indicated the prediction of the SAR was affected by merely pH and calcium. As a whole, the MLP of the ANN may be applicable for prediction of the SAR which is necessary parameter ration for agriculture.
{\\sc CosmoNet}: fast cosmological parameter estimation in non-flat models using neural networks
Auld, T; Hobson, M P
2007-01-01
We present a further development of a method for accelerating the calculation of CMB power spectra, matter power spectra and likelihood functions for use in cosmological Bayesian inference. The algorithm, called {\\sc CosmoNet}, is based on training a multilayer perceptron neural network. We compute CMB power spectra (up to $\\ell=2000$) and matter transfer functions over a hypercube in parameter space encompassing the $4\\sigma$ confidence region of a selection of CMB (WMAP + high resolution experiments) and large scale structure surveys (2dF and SDSS). We work in the framework of a generic 7 parameter non-flat cosmology. Additionally we use {\\sc CosmoNet} to compute the WMAP 3-year, 2dF and SDSS likelihoods over the same region. We find that the average error in the power spectra is typically well below cosmic variance for spectra, and experimental likelihoods calculated to within a fraction of a log unit. We demonstrate that marginalised posteriors generated with {\\sc CosmoNet} spectra agree to within a few p...
Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models
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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.
Medarevi?, Djordje P; Kleinebudde, Peter; Djuriš, Jelena; Djuri?, Zorica; Ibri?, Svetlana
2016-03-01
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. PMID:26065534
Modeling of Soft sensor based on Artificial Neural Network for Galactic Cosmic Rays Application
International Nuclear Information System (INIS)
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
Artificial neural networks for analysis of process states in fluidized bed combustion
Energy Technology Data Exchange (ETDEWEB)
Liukkonen, M.; Heikkinen, M.; Hiltunen, T.; Halikka, E.; Kuivalainen, R.; Hiltunen, Y. [University of Eastern Finland, Kuopio (Finland). Dept. of Environmental Science
2011-01-15
There are several challenges confronting energy production nowadays, such as increasing the efficiency of combustion processes and at the same time reducing harmful emissions. The latter, however, often necessitates process improvement, which requires knowledge of the behavior of the process. It is therefore important to develop and implement novel methods for process diagnostics that can respond to the challenges of modern-day energy plants. In this study the formation of nitrogen oxides (NOx) in a circulating fluidized bed (CFB) boiler is modeled by using artificial neural networks (ANN). In the approach used, the process data are first arranged using self-organizing maps (SOM) and k-means clustering to create subsets representing the separate process states in the boiler, including load increase and load decrease situations and conditions of high or low boiler load. After the determination of these process states, variable selection based on multilayer perceptrons (MLP) is performed to obtain information on the factors affecting the formation of NOx in those states. The results show that this approach provides a useful way of monitoring a combustion process.
Greek long-term energy consumption prediction using artificial neural networks
International Nuclear Information System (INIS)
In this paper artificial neural networks (ANN) are addressed in order the Greek long-term energy consumption to be predicted. The multilayer perceptron model (MLP) has been used for this purpose by testing several possible architectures in order to be selected the one with the best generalizing ability. Actual recorded input and output data that influence long-term energy consumption were used in the training, validation and testing process. The developed ANN model is used for the prediction of 2005-2008, 2010, 2012 and 2015 Greek energy consumption. The produced ANN results for years 2005-2008 were compared with the results produced by a linear regression method, a support vector machine method and with real energy consumption records showing a great accuracy. The proposed approach can be useful in the effective implementation of energy policies, since accurate predictions of energy consumption affect the capital investment, the environmental quality, the revenue analysis, the market research management, while conserve at the same time the supply security. Furthermore it constitutes an accurate tool for the Greek long-term energy consumption prediction problem, which up today has not been faced effectively.
International Nuclear Information System (INIS)
This paper presents an artificial neural network (ANN) approach for annual electricity consumption in high energy consumption industrial sectors. Chemicals, basic metals and non-metal minerals industries are defined as high energy consuming industries. It is claimed that, due to high fluctuations of energy consumption in high energy consumption industries, conventional regression models do not forecast energy consumption correctly and precisely. Although ANNs have been typically used to forecast short term consumptions, this paper shows that it is a more precise approach to forecast annual consumption in such industries. Furthermore, the ANN approach based on a supervised multi-layer perceptron (MLP) is used to show it can estimate the annual consumption with less error. Actual data from high energy consuming (intensive) industries in Iran from 1979 to 2003 is used to illustrate the applicability of the ANN approach. This study shows the advantage of the ANN approach through analysis of variance (ANOVA). Furthermore, the ANN forecast is compared with actual data and the conventional regression model through ANOVA to show its superiority. This is the first study to present an algorithm based on the ANN and ANOVA for forecasting long term electricity consumption in high energy consuming industries
Directory of Open Access Journals (Sweden)
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.
International Nuclear Information System (INIS)
A new analysis technique, called multi-level interval estimation method, is developed for locating damage in structures. In this method, the artificial neural networks (ANN) analysis method is combined with the statistics theory to estimate the range of damage location. The ANN is multilayer perceptron trained by back-propagation. Natural frequencies and modal shape at a few selected points are used as input to identify the location and severity of damage. Considering the large-scale structures which have lots of elements, multi-level interval estimation method is developed to reduce the estimation range of damage location step-by-step. Every step, estimation range of damage location is obtained from the output of ANN by using the method of interval estimation. The next ANN training cases are selected from the estimation range after linear transform, and the output of new ANN estimation range of damage location will gained a reduced estimation range. Two numerical example analyses on 10-bar truss and 100-bar truss are presented to demonstrate the effectiveness of the proposed method.
NEURAL NETWORKS FOR THE SIMULATION OF MICROCLIMATIC PARAMETERS IN DAIRY HOUSES
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Alessandro D'Emilio
2009-06-01
Full Text Available The aim of the present paper is to study natural ventilation in a dairy house by means of a parametric analysis relating wind speed and direction to the air flows through the ridge vent of the building. This analysis was carried out by means of an artificial neural network (ANN which capability in modelling and simulating some climatic parameters inside a dairy house has been validated using the data collected in a trial carried out during summer 2005. The results show that modelling a Generalized feed-forward Multi-Layer Perceptron ANN allowed to obtain satisfactory results in the simulation of air speed and direction and air temperature and humidity inside a dairy house, using as input the values of wind speed and direction and outdoor air temperature and humidity. The adequate accuracy in the simulation of the air motion across the ridge vent allowed to perform a parametric analysis of the ventilation, which provided the values of air speed and direction in function of a fixed range of values of wind speed and direction.
Aspects of artificial neural networks - with applications in high energy physics
International Nuclear Information System (INIS)
Different aspects of artificial neural networks are studied and discussed. They are demonstrated to be powerful general purpose algorithms, applicable to many different problem areas like pattern recognition, function fitting and prediction. Multi-layer perceptron (MPL) models are shown to out perform previous standard approaches on both off-line and on-line analysis tasks in high energy physics, like quark flavour tagging and mass reconstruction, as well as being powerful tools for prediction tasks. It is also demonstrated how a self-organizing network can be employed to extract information from data, for instance to track down origins of unexpected model discrepancies. Furthermore, it is proved that the MPL is more efficient than the learning vector quantization technique on classification problems, by producing smoother discrimination surfaces, and that an MPL network should be trained with a noisy updating schedule if the Hessian is ill-conditioned - A result that is especially important for MPL network with more than just one hidden layer. 81 refs, 6 figs
Lu, W Z; Wang, W J; Wang, X K; Xu, Z B; Leung, A Y T
2003-09-01
As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of air pollutant parameters becomes an important and popular topic in environmental science. Airborne pollution is a serious, and will be a major problem in Hong Kong within the next few years. In Hong Kong, Respirable Suspended Particulate (RSP) and Nitrogen Oxides NOx and NO2 are major air pollutants due to the dominant diesel fuel usage by public transportation and heavy vehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are of significance to public and the city image. The multi-layer perceptron (MLP) neural network is regarded as a reliable and cost-effective method to achieve such tasks. The works presented here involve developing an improved neural network model, which combines the principal component analysis (PCA) technique and the radial basis function (RBF) network, and forecasting the pollutant levels and tendencies based in the recorded data. In the study, the PCA is firstly used to reduce and orthogonalize the original input variables (data), these treated variables are then used as new input vectors in RBF neural network model established for forecasting the pollutant tendencies. Comparing with the general neural network models, the proposed model possesses simpler network architecture, faster training speed, and more satisfactory predicting performance. This improved model is evaluated by using hourly time series of RSP, NOx and NO2 concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP. NOx and NO2 concentrations with the actual data of these pollutants recorded at the monitory station, the effectiveness of the proposed model has been proven. Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and has advantages over the traditional neural network methods. PMID:12952354
Computationally efficient model predictive control algorithms a neural network approach
?awry?czuk, Maciej
2014-01-01
This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: · A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. · Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. · The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). · The MPC algorithms with neural approximation with no on-line linearization. · The MPC algorithms with guaranteed stability and robustness. · Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...
Scientific Electronic Library Online (English)
Hamid Reza, Tavakoli; Omid Lotfi, Omran; Saman Soleimani, Kutanaei; Masoud Falahtabar, shiade.
2014-11-01
Full Text Available The main objective of the present work is to utilize feedforward multi-layer perceptron (MLP) type of artificial neural networks (ANN) to find the combined effect of nano-silica and different fibers (steel, polypropylene, glass) on the toughness, flexural strength and fracture energy of concrete is [...] evaluated.For this purpose, 40 mix plot including 4 series A and B and C and D, which contain, respectively, 0, 2, 4 and 6% weight of cement, nano-silica particles were used as a substitute for cement. Each of series includes three types of fibers (metal: 0.2, 0.3 and 0.5% volume and polypropylene: 0.1, 0.15 and 0.2 % volume and glass 0.15 and 0.2 and 0.3% by volume) were tested. The obtained results from the experimental data are used to train the MLP type artificial neural network. The Results of this study show that fibers conjugate presence and optimal percent of nano-silica improved toughness, flexural strength and fracture energy of concrete of Self-compacting concrete (SCC). Results of this study show that fibers conjugate presence and optimal per-cent of nano-silica improved toughness, toughness, fracture ener-gy and flexural strength of SCC.
Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.
2015-06-01
Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20?s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.
CÃ¡mara, MontaÃ±a; Torrecilla, JosÃ© S; Caceres, Jorge O; SÃ¡nchez Mata, M Cortes; FernÃ¡ndez-Ruiz, Virginia
2010-01-13
In this study a neural network (NN) model was designed to predict lycopene and beta-carotene concentrations in food samples, combined with a simple and fast technique, such as UV-vis spectroscopy. The measurement of the absorbance at 446 and 502 nm of different beta-carotene and lycopene standard mixtures was used to optimize a neural network based on a multilayer perceptron (MLP) (learning and verification process). Then, for validation purposes, the optimized NN has been applied to determine the concentration of both compounds in food samples (fresh tomato, tomato concentrate, tomato sauce, ketchup, tomato juice, watermelon, medlar, green pepper, and carrots), comparing the NN results with the known values of these compounds obtained by analytical techniques (UV-vis and HPLC). It was concluded that when the MLP-NN is used within the range studied, the optimized NN is able to estimate the beta-carotene and lycopene concentrations in food samples with an adequate accuracy, solving the UV-vis interference of beta-carotene and lycopene. PMID:19919099
Directory of Open Access Journals (Sweden)
Rajesh Rai
2010-11-01
Full Text Available In this paper, an attempt is made to apply the principles of artificial neural networks (ANN towards developing a prediction model for surface roughness during the machining of high chromium steel through face milling process. Now a days, hot chromium steel is prominently used in die and mould industry as well as in press tools, helicopter rotor blades, etc... Initially, Taguchi design of experiments was applied while conducting the experiments to reduce the time and cost of experiment. Multilayer perceptron (MLP network using Feed Forward Error Back propagation was chosen as the Neural Network architecture to describe the process model. The experiments were conducted on a C.N.C milling machine using carbide cutters. Pearson correlation coefficient was also calculated to analyze the correlation between the system inputs and selected system output i.e. surface roughness. The results of ANN modeling were substantiated by testing and validation of the resulting surface roughness values and the results have been encouraging. The outputs of Pearson correlation coefficient also showed a strong correlation between the feed per tooth and surface roughness, followed by cutting speed.
Artificial Neural Nets with Interaction of Afferents
Blasio, Gabriel de; Moreno Díaz, Arminda; Moreno Díaz, Roberto
2011-01-01
The aim is to obtain computationally more powerful, neuro physiologically founded, arti?cial neurons and neural nets. Arti?cial Neural Nets (ANN) of the Perceptron type evolved from the original proposal by McCulloch an Pitts classical paper [1]. Essentially, they keep the computing structure of a linear machine followed by a non linear operation. The McCulloch-Pitts formal neuron (which was never considered by the author’s to be models of real neurons) consists of the simplest case of a lin...
International Conference on Artificial Neural Networks (ICANN)
Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics
2015-01-01
The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...
Czech Academy of Sciences Publication Activity Database
Šíma, Ji?í
1995-01-01
Ro?. 8, ?. 2 (1995), s. 261-271. ISSN 0893-6080 R&D Projects: GA ?R GA201/95/0976 Keywords : expert system * knowledge representation * multilayered neural network * back propagation * interval neuron function * incomplete information * explanation Impact factor: 1.262, year: 1995
PLUNKETT, K; Marchman, V
1991-01-01
A three-layer back-propagation network is used to implement a pattern association task in which four types of mapping are learned. These mappings, which are considered analogous to those which characterize the relationship between the stem and past tense forms of English verbs, include arbitrary mappings, identity mappings, vowel changes, and additions of a suffix. The degree of correspondence between parallel distributed processing (PDP) models which learn mappings of this sort (e.g., Rumelh...
Introduction to neural networks
International Nuclear Information System (INIS)
This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix
Energy Technology Data Exchange (ETDEWEB)
Labrador, I.; Carrasco, R.; Martinez, L.
1996-07-01
This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.
International Nuclear Information System (INIS)
This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs
Neural Network Aided Glitch-Burst Discrimination and Glitch Classification
Rampone, Salvatore; Pierro, Vincenzo; Troiano, Luigi; Pinto, Innocenzo M.
2013-11-01
We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts (glitches), in the data of a single detector. The further classification of glitches into typical sets is explored. In order to provide a proof of concept, we use the core-collapse supernova waveform catalog produced by H. Dimmelmeier and co-Workers, and the data base of glitches observed in laser interferometer gravitational wave observatory (LIGO) data maintained by P. Saulson and co-Workers to construct datasets of (windowed) transient waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian) noise with different signal-to-noise ratios (SNR). Principal component analysis (PCA) is next implemented for reducing data dimensionality, yielding results consistent with, and extending those in the literature. Then, a multilayer perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset, and used to classify the transients as glitch or burst. A Self-Organizing Map (SOM) architecture is finally used to classify the glitches. The glitch/burst discrimination and glitch classification abilities are gauged in terms of the related truth tables. Preliminary results suggest that the approach is effective and robust throughout the SNR range of practical interest. Perspective applications pertain both to distributed (network, multisensor) detection of GWBs, where some intelligence at the single node level can be introduced, and instrument diagnostics/optimization, where spurious transients can be identified, classified and hopefully traced back to their entry points.
Scientific Electronic Library Online (English)
Vitor Hugo, Ferreira; Alexandre Pinto Alves da, Silva.
2011-12-01
Full Text Available Após 1991, a literatura sobre previsão de carga passou a ser dominada por propostas baseadas em modelos neurais. Entretanto, um empecilho na aplicação destes modelos reside na possibilidade do ajuste excessivo dos dados, i.e, overfitting. O excesso de não-linearidade disponibilizado pelos modelos ne [...] urais de previsão de carga, que depende da representação do espaço de entrada, vem sendo ajustado de maneira heurística. Modelos autônomos incluindo técnicas automáticas e acopladas para seleção de entradas e controle de complexidade dos modelos foram propostos recentemente para previsão de carga em curto prazo. Entretanto, estas técnicas necessitam da especificação do conjunto inicial de entradas que será processado pelo modelo visando determinar aquelas mais relevantes. Este trabalho explora a teoria do caos como ferramenta de análise não-linear de séries temporais na definição automática do conjunto de atrasos de uma dada série de carga a serem utilizados como entradas de modelos neurais autônomos. Neste trabalho, inferência Bayesiana aplicada a perceptrons de múltiplas camadas e máquinas de vetores relevantes são utilizadas no desenvolvimento de modelos neurais autônomos. Abstract in english After 1991, the literature on load forecasting has been dominated by neural network based proposals. However, one major risk in using neural models is the possibility of excessive training, i.e., data overfitting. The extent of nonlinearity provided by neural network based load forecasters, which de [...] pends on the input space representation, has been adjusted using heuristic procedures. The empirical nature of these procedures makes their application cumbersome and time consuming. Autonomous modeling including automatic input selection and model complexity control has been proposed recently for short-term load forecasting. However, these techniques require the specification of an initial input set that will be processed by the model in order to select the most relevant variables. This paper explores chaos theory as a tool from non-linear time series analysis to automatic select the lags of the load series data that will be used by the neural models. In this paper, Bayesian inference applied to multi-layered perceptrons and relevance vector machines are used in the development of autonomous neural models.
A neural network construction method for surrogate modeling of physics-based analysis
Sung, Woong Je
In this thesis existing methodologies related to the developmental methods of neural networks have been surveyed and their approaches to network sizing and structuring are carefully observed. This literature review covers the constructive methods, the pruning methods, and the evolutionary methods and questions about the basic assumption intrinsic to the conventional neural network learning paradigm, which is primarily devoted to optimization of connection weights (or synaptic strengths) for the pre-determined connection structure of the network. The main research hypothesis governing this thesis is that, without breaking a prevailing dichotomy between weights and connectivity of the network during learning phase, the efficient design of a task-specific neural network is hard to achieve because, as long as connectivity and weights are searched by separate means, a structural optimization of the neural network requires either repetitive re-training procedures or computationally expensive topological meta-search cycles. The main contribution of this thesis is designing and testing a novel learning mechanism which efficiently learns not only weight parameters but also connection structure from a given training data set, and positioning this learning mechanism within the surrogate modeling practice. In this work, a simple and straightforward extension to the conventional error Back-Propagation (BP) algorithm has been formulated to enable a simultaneous learning for both connectivity and weights of the Generalized Multilayer Perceptron (GMLP) in supervised learning tasks. A particular objective is to achieve a task-specific network having reasonable generalization performance with a minimal training time. The dichotomy between architectural design and weight optimization is reconciled by a mechanism establishing a new connection for a neuron pair which has potentially higher error-gradient than one of the existing connections. Interpreting an instance of the absence of connection as a zero-weight connection, the potential contribution to training error reduction of any present or absent connection can readily be evaluated using the BP algorithm. Instead of being broken, the connections that contribute less remain frozen with constant weight values optimized to that point but they are excluded from further weight optimization until reselected. In this way, a selective weight optimization is executed only for the dynamically maintained pool of high gradient connections. By searching the rapidly changing weights and concentrating optimization resources on them, the learning process is accelerated without either a significant increase in computational cost or a need for re-training. This results in a more task-adapted network connection structure. Combined with another important criterion for the division of a neuron which adds a new computational unit to a network, a highly fitted network can be grown out of the minimal random structure. This particular learning strategy can belong to a more broad class of the variable connectivity learning scheme and the devised algorithm has been named Optimal Brain Growth (OBG). The OBG algorithm has been tested on two canonical problems; a regression analysis using the Complicated Interaction Regression Function and a classification of the Two-Spiral Problem. A comparative study with conventional Multilayer Perceptrons (MLPs) consisting of single- and double-hidden layers shows that OBG is less sensitive to random initial conditions and generalizes better with only a minimal increase in computational time. This partially proves that a variable connectivity learning scheme has great potential to enhance computational efficiency and reduce efforts to select proper network architecture. To investigate the applicability of the OBG to more practical surrogate modeling tasks, the geometry-to-pressure mapping of a particular class of airfoils in the transonic flow regime has been sought using both the conventional MLP networks with pre-defined architecture and the OBG-developed networks started from
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. PMID:25892634
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.
International Nuclear Information System (INIS)
A technique for level measurement in pressure vessels was developed using thermal probes with internal cooling and artificial neural networks (ANN's). This new concept of thermal probes was experimentally tested in an experimental facility (BETSNI) with two test sections, ST1 and ST2. Two different thermal probes were designed and constructed: concentric tubes probe and U tube probe. A data acquisition system (DAS) was assembled to record the experimental data during the tests. Steady state and transient level tests were carried out and the experimental data obtained were used as learning and recall data sets in the ANN's program RETRO-05 that simulate a multilayer perceptron with backpropagation. The results of the analysis show that the technique can be applied for level measurements in pressure vessel. The technique is applied for a less input temperature data than the initially designed to the probes. The technique is robust and can be used in case of lack of some temperature data. Experimental data available in literature from electrically heated thermal probe were also used in the ANN's analysis producing good results. The results of the ANN's analysis show that the technique can be improved and applied to level measurements in pressure vessels. (author)
Directory of Open Access Journals (Sweden)
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.
DEFF Research Database (Denmark)
S. Nadimi, Esmaeil; Nyholm JØrgensen, Rasmus
2012-01-01
Animal welfare is an issue of great importance in modern food production systems. Because animal behavior provides reliable information about animal health and welfare, recent research has aimed at designing monitoring systems capable of measuring behavioral parameters and transforming them into their corresponding behavioral modes. However, network unreliability and high-energy consumption have limited the applicability of those systems. In this study, a 2.4-GHz ZigBee-based mobile ad hoc wireless sensor network (MANET) that is able to overcome those problems is presented. The designed MANET showed high communication reliability, low energy consumption and low packet loss rate (14.8%) due to the deployment of modern communication protocols (e.g. multi-hop communication and handshaking protocol). The measured behavioral parameters were transformed into the corresponding behavioral modes using a multilayer perceptron (MLP)-based artificial neural network (ANN). The best performance of the ANN in terms of the mean squared error (MSE) and the convergence speed was achieved when it was initialized and trained using the Nguyen–Widrow and Levenberg–Marquardt back-propagation algorithms, respectively. The success rate of behavior classification into five classes (i.e. grazing, lying down, walking, standing and others) was 76.2% (?mean=1.06)(?mean=1.06) on average. The results of this study showed an important improvement regarding the performance of the designed MANET and behavior classification compared to the results of other similar studies.
Lee, Su Jeong; Ahn, Myoung-Hwan; Lee, Yeonjin
2016-02-01
Atmospheric instability information derived from satellites plays an important role in short-term weather forecasting, especially the forecasting of severe convective storms. For the next generation of weather satellites for Korea's multi-purpose geostationary satellite program, a new imaging instrument has been developed. Although this imaging instrument is not designed to perform full sounding missions and its capability is limited, its multi-spectral infrared channels provide information on vertical sounding. To take full advantage of the observation data from the much improved spatiotemporal resolution of the imager, the feasibility of an artificial neural network approach for the derivation of the atmospheric instability is investigated. The multi-layer perceptron model with a feed-forward and back-propagation training algorithm shows quite a sensitive response to the selection of the training dataset and model architecture. Through an extensive performance test with a carefully selected training dataset of 7197 independent profiles, the model architectures are selected to be 12, 5000, and 0.3 for the number of hidden nodes, number of epochs, and learning rate, respectively. The selected model gives a mean absolute error, RMSE, and correlation coefficient of 330 J kg-1, 420 J kg-1, and 0.9, respectively. The feasibility is further demonstrated via application of the model to real observation data from a similar instrument that has comparable observation channels with the planned imager.
Li, Wei; Zhang, Yan; Cui, Lijuan; Zhang, Manyin; Wang, Yifei
2015-08-01
A horizontal subsurface flow constructed wetland (HSSF-CW) was designed to improve the water quality of an artificial lake in Beijing Wildlife Rescue and Rehabilitation Center, Beijing, China. Artificial neural networks (ANNs), including multilayer perceptron (MLP) and radial basis function (RBF), were used to model the removal of total phosphorus (TP). Four variables were selected as the input parameters based on the principal component analysis: the influent TP concentration, water temperature, flow rate, and porosity. In order to improve model accuracy, alternative ANNs were developed by incorporating meteorological variables, including precipitation, air humidity, evapotranspiration, solar heat flux, and barometric pressure. A genetic algorithm and cross-validation were used to find the optimal network architectures for the ANNs. Comparison of the observed data and the model predictions indicated that, with careful variable selection, ANNs appeared to be an efficient and robust tool for predicting TP removal in the HSSF-CW. Comparison of the accuracy and efficiency of MLP and RBF for predicting TP removal showed that the RBF with additional meteorological variables produced the most accurate results, indicating a high potentiality for modeling TP removal in the HSSF-CW. PMID:25903184
Directory of Open Access Journals (Sweden)
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.
Monthly evaporation forecasting using artificial neural networks and support vector machines
Tezel, Gulay; Buyukyildiz, Meral
2015-02-01
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 ?-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, ?-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 ?-SVR had similar results. The ANNs and ?-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.
Golubovi?, Jelena; Birkemeyer, Claudia; Proti?, Ana; Otaševi?, Biljana; Ze?evi?, Mira
2016-03-18
Quantitative structure-property relationship (QSPR) methods are based on the hypothesis that changes in the molecular structure are reflected in changes in the observed property of the molecule. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. For the first time a quantitative structure-response relationship in electrospray ionization-mass spectrometry (ESI-MS) by means of artificial neural networks (ANN) on the group of angiotensin II receptor antagonists - sartans has been established. The investigated descriptors correspond to different properties of the analytes: polarity (logP), ionizability (pKa), surface area (solvent excluded volume) and number of proton acceptors. The influence of the instrumental parameters: methanol content in mobile phase, mobile phase pH and flow rate was also examined. Best performance showed a multilayer perceptron network with the architecture 6-3-3-1, trained with backpropagation algorithm. It showed high prediction ability on the previously unseen (test) data set with a coefficient of determination of 0.994. High prediction ability of the model would enable prediction of ESI-MS responsiveness under different conditions. This is particularly important in the method development phase. Also, prediction of responsiveness can be important in case of gradient-elution LC-MS and LC-MS/MS methods in which instrumental conditions are varied during time. Polarity, chargeability and surface area all appeared to be crucial for electrospray ionization whereby signal intensity appeared to be the result of a simultaneous influence of the molecular descriptors and their interactions. Percentage of organic phase in the mobile phase showed a positive, while flow rate showed a negative impact on signal intensity. PMID:26884139
Reconstruction of sub-surface archaeological remains from magnetic data using neural computing.
Bescoby, D. J.; Cawley, G. C.; Chroston, P. N.
2003-04-01
The remains of a former Roman colonial settlement, once part of the classical city of Butrint in southern Albania have been the subject of a high resolution magnetic survey using a caesium-vapour magnetometer. The survey revealed the surviving remains of an extensive planned settlement and a number of outlying buildings, today buried beneath over 0.5 m of alluvial deposits. The aim of the current research is to derive a sub-surface model from the magnetic survey measurements, allowing an enhanced archaeological interpretation of the data. Neural computing techniques are used to perform the non-linear mapping between magnetic data and corresponding sub-surface model parameters. The adoption of neural computing paradigms potentially holds several advantages over other modelling techniques, allowing fast solutions for complex data, while having a high tolerance to noise. A multi-layer perceptron network with a feed-forward architecture is trained to estimate the shape and burial depth of wall foundations using a series of representative models as training data. Parameters used to forward model the training data sets are derived from a number of trial trench excavations targeted over features identified by the magnetic survey. The training of the network was optimized by first applying it to synthetic test data of known source parameters. Pre-processing of the network input data, including the use of a rotationally invariant transform, enhanced network performance and the efficiency of the training data. The approach provides good results when applied to real magnetic data, accurately predicting the depths and layout of wall foundations within the former settlement, verified by subsequent excavation. The resulting sub-surface model is derived from the averaged outputs of a ‘committee’ of five networks, trained with individualized training sets. Fuzzy logic inference has also been used to combine individual network outputs through correlation with data from a second geophysical technique, allowing the integration of data from a separate series of measurements.
ANNAM. An artificial neural net attraction model to analyze market shares.
Hruschka, Harald
1999-01-01
The marketing literature so far only considers attraction models with strict functional forms. Greater exibility can be achieved by the neural net based approach introduced which assesses brands' attraction values by means of a perceptron with one hidden layer. Using log-ratio transformed market shares as dependent variables stochastic gradient descent followed by a quasi-Newton method estimates parameters. For store-level data the neural net model performs better and implies a price response...
Transformation of Neural State Space Models into LFT Models for Robust Control Design
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2000-01-01
This paper considers the extraction of linear state space models and uncertainty models from neural networks trained as state estimators with direct application to robust control. A new method for writing a neural state space model in a linear fractional transformation form in a non-conservative way is proposed, and it is demonstrated how a standard robust control law can be designed for a system described by means of a multi layer perceptron.
An Efficient Weather Forecasting System using a Hybrid Neural Network SOFM–MLP
I. Kadar Shereef; S Santhosh Baboo
2010-01-01
Weather prediction is a challenging task for researchers and has drawn a lot of research interest in the recent years. Literature studies have shown that machine learning techniques achieved better performance than traditional statistical methods. Presently multilayer perceptron networks (MLPs) are used for prediction of the maximum and the minimum temperatures based on past observations on various atmospheric parameters. To capture the seasonality of atmospheric data, with a view to improvin...
Learning by random walks in the weight space of the Ising perceptron
International Nuclear Information System (INIS)
Several variants of a stochastic local search process for constructing the synaptic weights of an Ising perceptron are studied. In this process, binary patterns are sequentially presented to the Ising perceptron and are then learned as the synaptic weight configuration is modified through a chain of single- or double-weight flips within the compatible weight configuration space of the earlier learned patterns. This process is able to reach a storage capacity of ??0.63 for pattern length N = 101 and ??0.41 for N = 1001. If in addition a relearning process is exploited, the learning performance is further improved to a storage capacity of ??0.80 for N = 101 and ??0.42 for N = 1001. We found that, for a given learning task, the solutions constructed by the random walk learning process are separated by a typical Hamming distance, which decreases with the constraint density ? of the learning task; at a fixed value of ?, the width of the Hamming distance distribution decreases with N
Learning by random walks in the weight space of the Ising perceptron
Huang, Haiping; Zhou, Haijun
2010-08-01
Several variants of a stochastic local search process for constructing the synaptic weights of an Ising perceptron are studied. In this process, binary patterns are sequentially presented to the Ising perceptron and are then learned as the synaptic weight configuration is modified through a chain of single- or double-weight flips within the compatible weight configuration space of the earlier learned patterns. This process is able to reach a storage capacity of ??0.63 for pattern length N = 101 and ??0.41 for N = 1001. If in addition a relearning process is exploited, the learning performance is further improved to a storage capacity of ??0.80 for N = 101 and ??0.42 for N = 1001. We found that, for a given learning task, the solutions constructed by the random walk learning process are separated by a typical Hamming distance, which decreases with the constraint density ? of the learning task; at a fixed value of ?, the width of the Hamming distance distribution decreases with N.
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 coeï¬ƒcients 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.
Neural network analysis of W UMa eclipsing binaries
Zeraatgari, F. Z.; Abedi, A.; Farshad, M.; Ebadian, M.; Riazi, N.
2015-04-01
We try five different artificial neural models, four models based on PNN (Perceptron Neural Network), and one using GRNN (Generalized Regression Neural Network) as tools for the automated light curve analysis of W UMa-type eclipsing binary systems. These algorithms, which are inspired by the Rucinski method, are designed and trained using MATLAB 7.6. A total of 17,820 generated contact binary light curves are first analyzed using a truncated cosine series with 11 coefficients and the most significant coefficients are applied as inputs of the neural models. The required sample light curves are systematically generated, using the WD2007 program (Wilson and Devinney 2007). The trained neural models are then applied to estimate the geometrical parameters of seven W UMa-type systems. The efficiency of different neural network models are then evaluated and compared to find the most efficient one.
Directory of Open Access Journals (Sweden)
Vitor Hugo Ferreira
2011-12-01
Full Text Available After 1991, the literature on load forecasting has been dominated by neural network based proposals. However, one major risk in using neural models is the possibility of excessive training, i.e., data overfitting. The extent of nonlinearity provided by neural network based load forecasters, which depends on the input space representation, has been adjusted using heuristic procedures. The empirical nature of these procedures makes their application cumbersome and time consuming. Autonomous modeling including automatic input selection and model complexity control has been proposed recently for short-term load forecasting. However, these techniques require the specification of an initial input set that will be processed by the model in order to select the most relevant variables. This paper explores chaos theory as a tool from non-linear time series analysis to automatic select the lags of the load series data that will be used by the neural models. In this paper, Bayesian inference applied to multi-layered perceptrons and relevance vector machines are used in the development of autonomous neural models.ApÃ³s 1991, a literatura sobre previsÃ£o de carga passou a ser dominada por propostas baseadas em modelos neurais. Entretanto, um empecilho na aplicaÃ§Ã£o destes modelos reside na possibilidade do ajuste excessivo dos dados, i.e, overfitting. O excesso de nÃ£o-linearidade disponibilizado pelos modelos neurais de previsÃ£o de carga, que depende da representaÃ§Ã£o do espaÃ§o de entrada, vem sendo ajustado de maneira heurÃstica. Modelos autÃ´nomos incluindo tÃ©cnicas automÃ¡ticas e acopladas para seleÃ§Ã£o de entradas e controle de complexidade dos modelos foram propostos recentemente para previsÃ£o de carga em curto prazo. Entretanto, estas tÃ©cnicas necessitam da especificaÃ§Ã£o do conjunto inicial de entradas que serÃ¡ processado pelo modelo visando determinar aquelas mais relevantes. Este trabalho explora a teoria do caos como ferramenta de anÃ¡lise nÃ£o-linear de sÃ©ries temporais na definiÃ§Ã£o automÃ¡tica do conjunto de atrasos de uma dada sÃ©rie de carga a serem utilizados como entradas de modelos neurais autÃ´nomos. Neste trabalho, inferÃªncia Bayesiana aplicada a perceptrons de mÃºltiplas camadas e mÃ¡quinas de vetores relevantes sÃ£o utilizadas no desenvolvimento de modelos neurais autÃ´nomos.
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.
Directory of Open Access Journals (Sweden)
Ivana ÄurÄ‘eviÄ‡ BabiÄ‡
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.
Directory of Open Access Journals (Sweden)
Behniafar Ali
2013-01-01
Full Text Available The electric marine instruments are newly inserted in the trade and industry, for which the existence of an equipped and reliable power system is necessitated. One of the features of such a power system is that it cannot have an earth system causing the protection relays not to be able to detect the single line to ground short circuit fault. While on the other hand, the occurrence of another similar fault at the same time can lead to the double line fault and thereby the tripping of relays and shortening of vital loads. This in turn endangers the personals' security and causes the loss of military plans. From the above considerations, it is inferred that detecting the single line to ground fault in the marine instruments is of a special importance. In this way, this paper intends to detect the single line to ground fault in the power systems of the marine instruments using the wavelet transform and Multi-Layer Perceptron (MLP neural network. In the numerical analysis, several different types of short circuit faults are simulated on several marine power systems and the proposed approach is applied to detect the single line to ground fault. The results are of a high quality and preciseness and perfectly demonstrate the effectiveness of the proposed approach.
Kumari, Amrita; Das, Suchandan Kumar; Srivastava, Prem Kumar
2015-07-01
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.
International Nuclear Information System (INIS)
Highlights: â–º Solar radiation estimation based on Gene Expression Programming is unexplored. â–º This approach is evaluated for the first time in this study. â–º Other artificial intelligence models (ANN and ANFIS) are also included in the study. â–º New alternatives for solar radiation estimation based on temperatures are provided. - Abstract: Surface incoming solar radiation is a key variable for many agricultural, meteorological and solar energy conversion related applications. In absence of the required meteorological sensors for the detection of global solar radiation it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). A comparison was also made among these techniques and traditional temperature based global solar radiation estimation equations. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the modelsâ€™ performances. An ANN (a four-input multilayer perceptron with 10 neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ mâˆ’2 dâˆ’1 of RMSE). The ability of GEP approach to model global solar radiation based on daily atmospheric variables was found to be satisfactory.
International Nuclear Information System (INIS)
Fall prevention lacks easy, quantitative and wearable methods for the classification of fall-risk (FR). Efforts must be thus devoted to the choice of an ad hoc classifier both to reduce the size of the sample used to train the classifier and to improve performances. A new methodology that uses a neural network (NN) and a wearable device are hereby proposed for this purpose. The NN uses kinematic parameters assessed by a wearable device with accelerometers and rate gyroscopes during a posturography protocol. The training of the NN was based on the Mahalanobis distance and was carried out on two groups of 30 elderly subjects with varying fall-risk Tinetti scores. The validation was done on two groups of 100 subjects with different fall-risk Tinetti scores and showed that, both in terms of specificity and sensitivity, the NN performed better than other classifiers (naive Bayes, Bayes net, multilayer perceptron, support vector machines, statistical classifiers). In particular, (i) the proposed NN methodology improved the specificity and sensitivity by a mean of 3% when compared to the statistical classifier based on the Mahalanobis distance (SCMD) described in Giansanti (2006 Physiol. Meas. 27 1081â€“90); (ii) the assessed specificity was 97%, the assessed sensitivity was 98% and the area under receiver operator characteristics was 0.965. (note)
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M. Hayatzadeh
2015-08-01
Full Text Available Since the development of surface water control needs accurate access to flow behavior of sediment rates, the lack of sediment measurement stations, the novelty of most stations and the lack of statistics on the deposit make it difficult to properly evaluate and simulate the flow behavior and their sediments. In a watershed, the morphological characteristics and sediment load of flow affect each other. It is, thus, important to know about the extent of this relationship to manage and control the flow in downstream areas. In the present study, using artificial neural networks and sediment rating regression methods based on the data from 136 events and also morphological parameters, we have attempted to predict the sediment load of Bagh Abbas basin. In the first step, we used flow data to predict the sediment load of both methods, and then basin morphological characteristics such as the compactness factor and form factor were added to the models. The results of this study showed that by using neural networks of Multilayer Perceptron (MLP type with Levenberg – Marquardt algorithm and the stimulation function of tangent Sigmoid with two hidden layers and four neurons in each layer, we can predict suspended sediment discharge rate with a sufficient accuracy. Accuracy of the results obtained from the ANN method was higher than the accuracy of rating curve method. In the evaluation of NGANN & GANN network methods and SRC & MARS regression methods, correlation coefficients were respectively calculated as 0.94, 0.93, 0.767, 0.766, and root mean square errors (RMSE, 0.45, 0.49, 2.3 and 2.3. Nash coefficient (NS was calculated respectively as 0.71, 0.58, 0.27 and 0.23. Therefore, the most efficient method among the four models is artificial neural network combined with morphological data (GANN. Furthermore, the findings of the study show that adding geomorphological parameters to sediment rating has little effect on the model performance.
Learning by random walks in the weight space of the Ising perceptron
Huang, Haiping
2010-01-01
The weight space of the Ising perceptron is explored by a random walk process where single weight flips are performed until the new presented pattern is learned. In this learning protocol, patterns are added sequentially and previous learned patterns (constraints) should be kept satisfied. Random walks are carried out until no solutions can be found. By this protocol, we are able to evaluate the overlap distribution of different solutions found on the same learning instance, and we show that solutions are far apart in Hamming distance even at small loading, implying that well-separated clusters form in the weight space. Adding the constraint that the stability of each learned pattern should be maximized before another new pattern is presented, the evolving fraction of frozen weights can be computed and shows that the simple random walk process will get trapped by the exponentially many suboptimal states. However, we suggest an additional rule by which a finite energy barrier involving only the barely learned ...
Generalizing with perceptrons in case of structured phase- and pattern-spaces
Dirscherl, G; Krey, U
1998-01-01
We investigate the influence of different kinds of structure on the learning behaviour of a perceptron performing a classification task defined by a teacher rule. The underlying pattern distribution is permitted to have spatial correlations. The prior distribution for the teacher coupling vectors itself is assumed to be nonuniform. Thus classification tasks of quite different difficulty are included. As learning algorithms we discuss Hebbian learning, Gibbs learning, and Bayesian learning with different priors, using methods from statistics and the replica formalism. We find that the Hebb rule is quite sensitive to the structure of the actual learning problem, failing asymptotically in most cases. Contrarily, the behaviour of the more sophisticated methods of Gibbs and Bayes learning is influenced by the spatial correlations only in an intermediate regime of $\\alpha$, where $\\alpha$ specifies the size of the training set. Concerning the Bayesian case we show, how enhanced prior knowledge improves the performa...
International Nuclear Information System (INIS)
A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from Haar measures. This assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross-correlation matrix of the pattern matrix. Links to existing methods for analysis of perceptron learning and Gaussian linear vector channels and an application to a simple but nontrivial problem are also shown
Feng, Xiao; Li, Qi; Zhu, Yajie; Hou, Junxiong; Jin, Lingyan; Wang, Jingjie
2015-04-01
In the paper a novel hybrid model combining air mass trajectory analysis and wavelet transformation to improve the artificial neural network (ANN) forecast accuracy of daily average concentrations of PM2.5 two days in advance is presented. The model was developed from 13 different air pollution monitoring stations in Beijing, Tianjin, and Hebei province (Jing-Jin-Ji area). The air mass trajectory was used to recognize distinct corridors for transport of "dirty" air and "clean" air to selected stations. With each corridor, a triangular station net was constructed based on air mass trajectories and the distances between neighboring sites. Wind speed and direction were also considered as parameters in calculating this trajectory based air pollution indicator value. Moreover, the original time series of PM2.5 concentration was decomposed by wavelet transformation into a few sub-series with lower variability. The prediction strategy applied to each of them and then summed up the individual prediction results. Daily meteorological forecast variables as well as the respective pollutant predictors were used as input to a multi-layer perceptron (MLP) type of back-propagation neural network. The experimental verification of the proposed model was conducted over a period of more than one year (between September 2013 and October 2014). It is found that the trajectory based geographic model and wavelet transformation can be effective tools to improve the PM2.5 forecasting accuracy. The root mean squared error (RMSE) of the hybrid model can be reduced, on the average, by up to 40 percent. Particularly, the high PM2.5 days are almost anticipated by using wavelet decomposition and the detection rate (DR) for a given alert threshold of hybrid model can reach 90% on average. This approach shows the potential to be applied in other countries' air quality forecasting systems.
Directory of Open Access Journals (Sweden)
M.R. Mustafa
2014-05-01
Full Text Available Estimation of suspended sediments in rivers using soft computing techniques has been extensively performed around the world since 1990’s. However, accuracy in the results was always found to be highly desired and a profound crucial task. This study presents a thorough comparison between the performances of best basis function of Radial Basis Functions (RBF and the best training algorithm in Multilayer Perceptron (MLP neural networks for prediction of suspended sediments in Pari River, Perak, Malaysia. Time series data of water discharge and suspended sediments was used to develop MLP and RBF models. A comparison between six basis functions was performed to identify the most appropriate and best basis function for the selected time series of the river’s data. The performance of the models was compared using several statistical measures including coefficient of determination, coefficient of efficiency and mean absolute error. The performance of the best RBF function was compared with the previously identified best training algorithm of MLP neural networks. The results showed that comparison of various basis functions is always advantageous to achieve the most appropriate basis function for the accurate prediction of the time series data. The results also showed that the performances of both particular RBF and MLP models were close to each other and capable to capture the exact pattern of the sediment data in the river. However, the RBF model showed some inconsistency while predicting the time series data. Furthermore, RBF modeling required more investigation to choose appropriate value for the predefined parameters as compared to MLP modeling.
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.
Scientific Electronic Library Online (English)
Ernesto, Gómez Vargas; Nelson, Obregón Neira; Virgilio, Socarras Quintero.
2010-07-01
Full Text Available Este artículo muestra los resultados obtenidos en la exploración de la bondad de la implementación del modelo neurodifuso ANFIS y de las redes neuronales para la predicción de caudales medios mensuales en la cuenca del Rio Bogotá en la ciudad de Villapinzón. Se desarrolla e implementa el modelo ANFI [...] S y se evalúa el comportamiento de seis modelos, al variar el número de entradas, y el número y tipo de conjuntos difusos (funciones de pertenencia), que son los parámetros fundamentales del modelo ANFIS. Los resultados se comparan con los obtenidos con las redes neuronales perceptrón multicapa. Abstract in english This paper shows the results in the exploration of the benefits of the implementation of neuro-fuzzy AN-FIS model and neural networks for the prediction of monthly mean flows in the basin of Bogota River in Villapinzón. The ANFIS model is developed, implemented and the performance of six models is e [...] valuated bychanging entries number, number and type of fuzzy sets (membership functions), which are the basic parameters of the ANFIS model. The results are compared with those obtained with multilayer perceptron neural networks.
System Identification, Prediction, Simulation and Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...... a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent...
Multilayer Insulation Material Guidelines
Finckenor, M. M.; Dooling, D.
1999-01-01
Multilayer Insulation Material Guidelines provides data on multilayer insulation materials used by previous spacecraft such as Spacelab and the Long-Duration Exposure Facility and outlines other concerns. The data presented in the document are presented for information only. They can be used as guidelines for multilayer insulation design for future spacecraft provided the thermal requirements of each new design and the environmental effects on these materials are taken into account.
Scientific Electronic Library Online (English)
J. D, Velásquez; C. J, Franco.
2012-06-01
Full Text Available Muitas séries temporais com tendência e sazonalidade são sucesso modelado e previsto pelo modelo airline de Box e Jenkins, no entanto, este modelo negligencia a presença de não-linearidade dos dados. Neste trabalho, propomos uma nova versão não-linear do modelo airline, por isso, substituir o compon [...] ente linear das medias moviles por um perceptron multicamadas. O modelo proposto é utilizado para previsão de duas séries temporais de referência; descobrimos que o modelo proposto é capaz de prever a série de tempo com mais precisão que outros métodos tradicionais. Abstract in spanish Muchas series de tiempo con tendencia y ciclos estacionales son exitosamente modeladas y pronosticadas usando el modelo airline de Box y Jenkins; sin embargo, la presencia de no linealidades en los datos son despreciadas por este modelo. En este artículo, se propone una nueva versión no lineal del m [...] odelo airline; para esto, se reemplaza la componente lineal de promedios móviles por un perceptrón multicapa. El modelo propuesto es usado para pronosticar dos series de tiempo benchmark; se encontró que el modelo propuesto es capaz de pronosticar las series de tiempo con mayor precisión que otras aproximaciones tradicionales. Abstract in english Many time series with trend and seasonal pattern are successfully modeled and forecasted by the airline model of Box and Jenkins; however, this model neglects the presence of nonlinearity on data. In this paper, we propose a new nonlinear version of the airline model; for this, we replace the moving [...] average linear component by a multilayer perceptron neural network. The proposed model is used for forecasting two benchmark time series; we found that the proposed model is able to forecast the time series with more accuracy that other traditional approaches.
Directory of Open Access Journals (Sweden)
Ed Pinheiro Lima
2003-04-01
Full Text Available As capacidades de interpolação de redes perceptron multicamada (MLP foram utilizadas para resolver um sistema de equações diferencias ordinárias que modela um reator não-adiabático com leito fixo e dispersão axial. As metodologias descritas neste artigo seguem as propostas por Lagaris et al. (1998, 2000, estendidas para modelos com condições de contorno mistas e pelo uso do método da penalidade para converter o problema de otimização original de restrito para irrestrito no treinamento das redes MLP. Os resultados são compatíveis com aqueles apresentados em Luize e Biscaia (1991, que foram obtidos com técnicas numéricas já consagradas, como elementos finitos e colocação ortogonal. O método de neuro-interpolação adotado neste artigo é de fácil manuseio se comparado com os métodos clássicos para solução numérica de equações diferenciais, particularmente para sistemas diferenciais não-lineares, e define uma aproximação global, na forma analítica, para a solução de problemas.The interpolation capabilities of multilayer perceptron networks (MLP were used to solve a system of ordinary differential equations that models an axial dispersed non-adiabatic fixed bed reactor. The methodologies described in this paper follow the first ones proposed by Lagaris et al. (1998, 2000, but enlarge them to differential models with mix boundary conditions and by the use of the penalty method to convert the original constrained to unconstrained optimization problem in training the MLP networks. The results are in agreement on those in Luize e Biscaia (1991, which were obtained by well-established numerical techniques as finite element and orthogonal collocation methods. The neural interpolation method used in this paper is easier to handle than the classical methods for numerical solution of differential equations, particularly for non-linear differential systems, and defines a global approximation, in analytic form, for problems solution.
Directory of Open Access Journals (Sweden)
Sidney S. Zanetti
2008-04-01
Full Text Available Propor uma rede neural artificial (RNA para estimar a evapotranspiração de referência (ETo em função das coordenadas de posição geográfica e da temperatura do ar no Estado do Rio de Janeiro, motivou a realização do presente estudo. Os dados utilizados no treinamento da rede foram obtidos de 17 séries históricas de elementos climáticos localizadas nesse Estado. A ETo diária calculada pelo método de Penman-Monteith (FAO-56 foi utilizada como referência para treinar as redes. As RNAs, do tipo perceptron de múltiplas camadas, foram treinadas para estimar a ETo em função da latitude, longitude, altitude, temperatura média do ar, amplitude térmica diária e dia do ano. Após o treinamento com várias configurações de rede, selecionou-se a que apresentou o melhor desempenho, a qual é composta de apenas uma camada intermediária (com vinte neurônios e função de ativação do tipo sigmóide logística e uma camada de saída (com um neurônio e função de ativação linear. Pelos resultados obtidos conclui-se que, levando-se em consideração apenas as coordenadas de posição geográfica e a temperatura do ar, pode-se estimar a ETo (valores diários em 17 localidades do Estado do Rio de Janeiro usando uma RNA.This work was performed with the aim of proposing an artificial neural network (ANN to estimate the reference evapotranspiration (ETo as a function of geographic position coordinates and air temperature in the State of Rio de Janeiro. Data used for the network training were collected from 17 historical time series of climatic elements located in the State of Rio de Janeiro. The daily ETo calculated by Penman-Monteith (FAO-56 method was used as a reference for network training. ANNs of multilayer perceptron type were trained to estimate ETo as a function of latitude, longitude, altitude, mean air temperature, thermal daily amplitude and day of the year. After training with different network configurations, the one showing best performance was selected, and was composed by only one intermediary layer (with twenty neurons and sigmoid logistic activation function and one output layer (with one neuron and linear activation function. According to the results obtained it can be concluded that, considering only geographical positioning coordinates and air temperature, it is possible to estimate daily ETo in 17 places of Rio de Janeiro State by using an ANN.
Directory of Open Access Journals (Sweden)
N. Baghdadi
2012-06-01
Full Text Available The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on multi-layer perceptron (MLP neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases using or not using a-priori knowledge on soil parameters. The inversion approach was then validated using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils improves the soil moisture estimates, whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters ?_{1} and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm^{3} cm^{?3} and surface roughness (root mean square surface height lower or higher than 1.0 cm. Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 cm^{3} cm^{?3} without a-priori information on soil parameters and 0.065 cm^{3} cm^{?3} (RMSE applying a-priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm. Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with an RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.
Baghdadi, N.; Cresson, R.; El Hajj, M.; Ludwig, R.; La Jeunesse, I.
2012-06-01
The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on multi-layer perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases using or not using a-priori knowledge on soil parameters. The inversion approach was then validated using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates, whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters ?1 and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm3 cm-3) and surface roughness (root mean square surface height lower or higher than 1.0 cm). Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 cm3 cm-3 without a-priori information on soil parameters and 0.065 cm3 cm-3 (RMSE) applying a-priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm). Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with an RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.
Can artificial neural networks be used to predict the origin of ozone episodes?
Energy Technology Data Exchange (ETDEWEB)
Fontes, T., E-mail: trfontes@ua.pt [University Fernando Pessoa, Global Change, Energy, Environment and Bioengineering Center (CIAGEB), Praça 9 de Abril, 349, 4249-004 Porto (Portugal); University of Aveiro, Department of Mechanical Engineering/Centre for Mechanical Technology and Automation, Campus Universitário de Santiago, 3810-193 Aveiro (Portugal); Silva, L.M. [University of Aveiro, Department of Mathematics, Campus Universitário de Santiago, 3810-193 Aveiro (Portugal); INEB — Instituto de Engenharia Biomédica, Rua do Campo Alegre, 823, 4150-180 Porto (Portugal); Silva, M.P.; Barros, N. [University Fernando Pessoa, Global Change, Energy, Environment and Bioengineering Center (CIAGEB), Praça 9 de Abril, 349, 4249-004 Porto (Portugal); Carvalho, A.C. [New University of Lisbon, Faculty of Sciences and Technology/Center for Environmental and Sustainability Research (CENSE), Quinta da Torre, 2829-516 Caparica (Portugal)
2014-08-01
Tropospheric ozone is a secondary pollutant having a negative impact on health and environment. To control and minimize such impact the European Community established regulations to promote a clean air all over Europe. However, when an episode is related with natural mechanisms as Stratosphere–Troposphere Exchanges (STE), the benefits of an action plan to minimize precursor emissions are inefficient. Therefore, this work aims to develop a tool to identify the sources of ozone episodes in order to minimize misclassification and thus avoid the implementation of inappropriate air quality plans. For this purpose, an artificial neural network model – the Multilayer Perceptron – is used as a binary classifier of the source of an ozone episode. Long data series, between 2001 and 2010, considering the ozone precursors, {sup 7}Be activity and meteorological conditions were used. With this model, 2–7% of a mean error was achieved, which is considered as a good generalization. Accuracy measures for imbalanced data are also discussed. The MCC values show a good performance of the model (0.65–0.92). Precision and F{sub 1}-measure indicate that the model specifies a little better the rare class. Thus, the results demonstrate that such a tool can be used to help authorities in the management of ozone, namely when its thresholds are exceeded due natural causes, as the above mentioned STE. Therefore, the resources used to implement an action plan to minimize ozone precursors could be better managed avoiding the implementation of inappropriate measures. - Highlights: • ANN can classify the origin of an O{sub 3} episode with a mean error around 2-7%. • The best classification is obtained when a simpler input combination is used. • ANN can help authorities to foster O{sub 3} action plans to control exceedances.
Application of neural networks to measurement methods based on radiation interactions with matter
International Nuclear Information System (INIS)
The possibility of improving by neuronal techniques the preparation and interpretation of nuclear measurements was investigated. A general methodology was developed and applied to various problems in this field. Whatever the problem to be treated, to solve it comes to determine the relation which binds the inputs to the outputs. Neural networks based on supervised training, like the multilayer Perceptron, have the capability to calculate any relation between a set of input and output data. On the other hand, the training phase is often a long and delicate operation whose difficulties grow with the size of the network: it is thus interesting to reduce it by introducing knowledge a priori and/or by reducing the number of inputs in order to extract the relevant information. If the correlations between the inputs are linear, the Principal Components Analysis (PCA) and its neuronal equivalents make it possible to obtain by orthogonal projection a reduced number of input components while preserving the maximum of initial information. If the correlations are nonlinear, the Curvilinear Components Analysis (CCA) allows, by a unsupervised training, to carry out a nonlinear projection of the inputs in a space of reduced size. Besides, it is noticed that when the dimension of the input space is equal to the intrinsic dimension of the problem, this last is practically solved by CCA. We propose a general method which consists in characterizing as well as possible the problem by its inputs and then to extract and classify the information contained in those by projection in a space of reduced size. Association between the projected data and the problem outputs is then carried out by a supervised training network. Certain results having to be provided with their associated uncertainty, a statistical method based on the bootstrap algorithm is proposed. Potential applications other that those treated are considered. (author)
A Neural-Network-Based Nonlinear Adaptive State-Observer for Pressurized Water Reactors
Directory of Open Access Journals (Sweden)
Zhe Dong
2013-10-01
Full Text Available Although there have been some severe nuclear accidents such as Three Mile Island (USA, Chernobyl (Ukraine and Fukushima (Japan, nuclear fission energy is still a source of clean energy that can substitute for fossil fuels in a centralized way and in a great amount with commercial availability and economic competitiveness. Since the pressurized water reactor (PWR is the most widely used nuclear fission reactor, its safe, stable and efficient operation is meaningful to the current rebirth of the nuclear fission energy industry. Power-level regulation is an important technique which can deeply affect the operation stability and efficiency of PWRs. Compared with the classical power-level controllers, the advanced power-level regulators could strengthen both the closed-loop stability and control performance by feeding back the internal state-variables. However, not all of the internal state variables of a PWR can be obtained directly by measurements. To implement advanced PWR power-level control law, it is necessary to develop a state-observer to reconstruct the unmeasurable state-variables. Since a PWR is naturally a complex nonlinear system with parameters varying with power-level, fuel burnup, xenon isotope production, control rod worth and etc., it is meaningful to design a nonlinear observer for the PWR with adaptability to system uncertainties. Due to this and the strong learning capability of the multi-layer perceptron (MLP neural network, an MLP-based nonlinear adaptive observer is given for PWRs. Based upon Lyapunov stability theory, it is proved theoretically that this newly-built observer can provide bounded and convergent state-observation. This observer is then applied to the state-observation of a special PWR, i.e., the nuclear heating reactor (NHR, and numerical simulation results not only verify its feasibility but also give the relationship between the observation performance and observer parameters.
International Nuclear Information System (INIS)
This paper presents an application of Artificial Neural Networks (ANNs) to predict daily solar radiation. We look at the Multi-Layer Perceptron (MLP) network which is the most used of ANNs architectures. In previous studies, we have developed an ad-hoc time series preprocessing and optimized a MLP with endogenous inputs in order to forecast the solar radiation on a horizontal surface. We propose in this paper to study the contribution of exogenous meteorological data (multivariate method) as time series to our optimized MLP and compare with different forecasting methods: a naive forecaster (persistence), ARIMA reference predictor, an ANN with preprocessing using only endogenous inputs (univariate method) and an ANN with preprocessing using endogenous and exogenous inputs. The use of exogenous data generates an nRMSE decrease between 0.5% and 1% for two stations during 2006 and 2007 (Corsica Island, France). The prediction results are also relevant for the concrete case of a tilted PV wall (1.175 kWp). The addition of endogenous and exogenous data allows a 1% decrease of the nRMSE over a 6 months-cloudy period for the power production. While the use of exogenous data shows an interest in winter, endogenous data as inputs on a preprocessed ANN seem sufficient in summer. -- Research highlights: â†’ Use of exogenous data as ANN inputs to forecast horizontal daily global irradiation data. â†’ New methodology allowing to choice the adequate exogenous data - a systematic method comparing endogenous and exogenous data. â†’ Different referenced mathematical predictors allows to conclude about the pertinence of the proposed methodology.
Modeling of global horizontal irradiance in the United Arab Emirates with artificial neural networks
International Nuclear Information System (INIS)
This paper employs ANN (Artificial Neural Network) models to estimate GHI (global horizontal irradiance) for three major cities in the UAE (United Arab Emirates), namely Abu Dhabi, Dubai and Al-Ain. City data are then used to develop a comprehensive global GHI model for other nearby locations in the UAE. The ANN models use MLP (Multi-Layer Perceptron) and RBF (Radial Basis Function) techniques with comprehensive training algorithms, architectures, and different combinations of inputs. The UAE models are tested and validated against individual city models and data available from the UAE Solar Atlas with good agreement as attested by the computed statistical error parameters. The optimal ANN model is MLP-based and requires four mean daily weather parameters; namely, maximum temperature, wind speed, sunshine hours, and relative humidity. The computed statistical error parameters for the optimal MLP-ANN model in relation to the measured three-cities mean data (referred to as UAE data) are MBE (mean bias error)Â =Â âˆ’0.0003Â kWh/m2, RMSEÂ =Â 0.179Â kWh/m2, R2Â =Â 99%, NSE (Nash-Sutcliffe model Efficiency coefficient)Â =Â 99%, and t-statisticÂ =Â 0.005 at 5% significance level. Results prove the suitability of the ANN models for estimating the monthly mean daily GHI in different locations of the UAE. - Highlights: â€¢ ANN prediction models for the GHI (global horizontal irradiance) in the UAE. â€¢ Models used to estimate the potential of global solar radiation for UAE cities. â€¢ Data from the UAE Solar Atlas are used to validate developed ANN models. â€¢ ANN models are more efficient than regression models in predicting GHI
Can artificial neural networks be used to predict the origin of ozone episodes?
International Nuclear Information System (INIS)
Tropospheric ozone is a secondary pollutant having a negative impact on health and environment. To control and minimize such impact the European Community established regulations to promote a clean air all over Europe. However, when an episode is related with natural mechanisms as Stratosphereâ€“Troposphere Exchanges (STE), the benefits of an action plan to minimize precursor emissions are inefficient. Therefore, this work aims to develop a tool to identify the sources of ozone episodes in order to minimize misclassification and thus avoid the implementation of inappropriate air quality plans. For this purpose, an artificial neural network model â€“ the Multilayer Perceptron â€“ is used as a binary classifier of the source of an ozone episode. Long data series, between 2001 and 2010, considering the ozone precursors, 7Be activity and meteorological conditions were used. With this model, 2â€“7% of a mean error was achieved, which is considered as a good generalization. Accuracy measures for imbalanced data are also discussed. The MCC values show a good performance of the model (0.65â€“0.92). Precision and F1-measure indicate that the model specifies a little better the rare class. Thus, the results demonstrate that such a tool can be used to help authorities in the management of ozone, namely when its thresholds are exceeded due natural causes, as the above mentioned STE. Therefore, the resources used to implement an action plan to minimize ozone precursors could be better managed avoiding the implementation of inappropriate measures. - Highlights: â€¢ ANN can classify the origin of an O3 episode with a mean error around 2-7%. â€¢ The best classification is obtained when a simpler input combination is used. â€¢ ANN can help authorities to foster O3 action plans to control exceedances
Forecasting of preprocessed daily solar radiation time series using neural networks
Energy Technology Data Exchange (ETDEWEB)
Paoli, Christophe; Muselli, Marc; Nivet, Marie-Laure [University of Corsica, CNRS UMR SPE, Corte (France); Voyant, Cyril [University of Corsica, CNRS UMR SPE, Corte (France); Hospital of Castelluccio, Radiotherapy Unit, Ajaccio (France)
2010-12-15
In this paper, we present an application of Artificial Neural Networks (ANNs) in the renewable energy domain. We particularly look at the Multi-Layer Perceptron (MLP) network which has been the most used of ANNs architectures both in the renewable energy domain and in the time series forecasting. We have used a MLP and an ad hoc time series pre-processing to develop a methodology for the daily prediction of global solar radiation on a horizontal surface. First results are promising with nRMSE {proportional_to} 21% and RMSE {proportional_to} 3.59 MJ/m{sup 2}. The optimized MLP presents predictions similar to or even better than conventional and reference methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors. Moreover we found that the data pre-processing approach proposed can reduce significantly forecasting errors of about 6% compared to conventional prediction methods such as Markov chains or Bayesian inference. The simulator proposed has been obtained using 19 years of available data from the meteorological station of Ajaccio (Corsica Island, France, 41 55'N, 8 44'E, 4 m above mean sea level). The predicted whole methodology has been validated on a 1.175 kWc mono-Si PV power grid. Six prediction methods (ANN, clear sky model, combination..) allow to predict the best daily DC PV power production at horizon d + 1. The cumulated DC PV energy on a 6-months period shows a great agreement between simulated and measured data (R{sup 2} > 0.99 and nRMSE < 2%). (author)
A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran
International Nuclear Information System (INIS)
This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on artificial neural network (ANN), computer simulation and design of experiments using stochastic procedures. First, an ANN approach is illustrated based on supervised multi-layer perceptron (MLP) network for the electrical consumption forecasting. The chosen model, therefore, can be compared to that of estimated by time series model. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The simulated-based ANN model is then developed. Therefore, there are four treatments to be considered in analysis of variance (ANOVA), which are actual data, time series, ANN and simulated-based ANN. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being statistically equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan method (DMRT) of paired comparison is used to select the optimum model which could be time series, ANN or simulated-based ANN. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best fitted ANN model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that ANN always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to the proposed algorithm
Wind speed spatial estimation for energy planning in Sicily: A neural kriging application
Energy Technology Data Exchange (ETDEWEB)
Cellura, M.; Marvuglia, A. [Dipartimento di Ricerche Energetiche ed Ambientali (DREAM), Universita degli Studi di Palermo, Viale delle Scienze, 90128 Palermo (Italy); Cirrincione, G. [ISSIA-CNR, Institute on Intelligent Systems for the Automation, Section of Palermo, via Dante12, Palermo (Italy); Miraoui, A. [Universite de Technologie de Belfort-Montbeliard (UTBM), Belfort (France)
2008-06-15
One of the first steps for the exploitation of any energy source is necessarily represented by its estimation and mapping at the aim of identifying the most suitable areas in terms of energy potential. In the field of renewable energies this is often a very difficult task, because the energy source is in this case characterized by relevant variations over space and time. This implies that any temporal, but also spatial, estimation model has to be able to incorporate this spatial and temporal variability. The paper deals with the spatial estimation of the wind fields in Sicily (Italy) by following a data-driven approach. Starting from the results of a preliminary study, a novel technique resulting from the integration of neural and geostatistical techniques was developed in order to obtain the wind speed maps for the region at 10 and 50 meters above the ground level. The mean values of the theoretical Weibull distribution function describing the wind regime at each of the available measurement sites were used to train a multi-layer perceptron (MLP) whose goal is to compute the most of the wind spatial trends. Other pieces of information about the territory (altitude, land coverage) were also used as inputs of the network and organized into a geographic information system (GIS) environment. The remaining de-trended linear means have been computed by using a universal kriging (UK) estimator. The results of these steps were then summed up and it was thus possible to obtain a map of the estimated wind fields. (author)
Scientific Electronic Library Online (English)
Juan D., Velásquez; Sarah, Gutiérrez; Carlos J., Franco.
2013-06-01
Full Text Available La habilidad para obtener pronósticos precisos de la volatilidad es un importante problema para el analista financiero. En este artículo, se usa el modelo DAN2, un perceptrón multicapa y un modelo ARCH para pronosticar la varianza condicional mensual de una acción. Los resultados muestran que el mod [...] elo DAN2 es más preciso para pronosticar las varianzas dentro-de-la-muestra y fuera-de-la-muestra que los otros modelos considerados para el conjunto de datos utilizado. Así, el valor de esta red neuronal como herramienta predictiva es demostrado. Abstract in english The ability to obtain accurate volatility forecasts is an important issue for the financial analyst. In this paper, we use the DAN2 model, a multilayer perceptron and an ARCH model to predict the monthly conditional variance of stock prices. The results show that DAN2 model is more accurate for pred [...] icting in-sample and out-of-sample variance that the other considered models for the used dataset. Thus, the value of this neural network as a predictive tool is demonstrated.
Directory of Open Access Journals (Sweden)
J D Velásquez
2012-06-01
Full Text Available Many time series with trend and seasonal pattern are successfully modeled and forecasted by the airline model of Box and Jenkins; however, this model neglects the presence of nonlinearity on data. In this paper, we propose a new nonlinear version of the airline model; for this, we replace the moving average linear component by a multilayer perceptron neural network. The proposedmodel is used for forecasting two benchmark time series; we found that theproposed model is able to forecast the time series with more accuracy that other traditional approaches.Muchas series de tiempo con tendencia y ciclos estacionales son exitosamente modeladas y pronosticadas usando el modelo airline de Box y Jenkins; sin embargo, la presencia de no linealidades en los datos son despreciadas por este modelo. En este artículo, se propone una nueva versión no lineal del modelo airline; para esto, se reemplaza la componente lineal de promedios móviles por un perceptrón multicapa. El modelo propuesto es usado para pronosticar dos series de tiempo benchmark; se encontró que el modelo propuesto es capaz de pronosticar las series de tiempo con mayor precisión que otras aproximaciones tradicionales.
Directory of Open Access Journals (Sweden)
F. GUNE?
2014-04-01
Full Text Available In this work, a novel multi-objective design optimization procedure is presented for the Minkowski Reflectarray RAs using a complete 3-D CST Microwave Studio MWS-based Multilayer Perceptron Neural Network MLP NN model including the substrate constant ?r with a hybrid Genetic GA and Nelder-Mead NM algorithm. The MLP NN model provides an accurate and fast model and establishes the reflection phase of a unit Minkowski RA element as a continuous function within the input domain including the substrate 1 ? ?r ? 6; 0.5mm ? h ? 3mm in the frequency between 8GHz ? f ? 12GHz. This design procedure enables a designer to obtain not only the most optimum Minkowski RA design all throughout the X- band, at the same time the optimum Minkowski RAs on the selected substrates. Moreover a design of a fully optimized X-band 15×15 Minkowski RA antenna is given as a worked example with together the tolerance analysis and its performance is also compared with those of the optimized RAs on the selected traditional substrates. Finally it may be concluded that the presented robust and systematic multi-objective design procedure is conveniently applied to the Microstrip Reflectarray RAs constructed from the advanced patches.
The neural network Z vertex trigger for the Belle II detector
Energy Technology Data Exchange (ETDEWEB)
Skambraks, Sebastian; Abudinen, Fernando [Max-Planck-Institut fuer Physik (Werner-Heisenberg-Institut), Foehringer Ring 6 80805 Muenchen (Germany)
2013-07-01
A novel approach for track triggering is currently studied for the Belle II detector: neural networks are used to predict the event vertex in z direction, using only information from the central drift chamber. The lack in accuracy of classical online vertex reconstruction motivates new studies for the z vertex trigger. Since neural networks are general function approximators, they are well suited for problems where the model is not known a priori. Several methods were investigated, but our studies for single tracks in geometrically restricted areas of the detector have proven the multi layer perceptron to produce the most accurate results, even in the presence of background. This encourages the use of a set of multi layer perceptrons to cover the entire detector. Additionally, the methods presented may lead to online event reconstruction, for Belle II as well as for other running or future detectors.
Directory of Open Access Journals (Sweden)
Tomas Ayala-Silva
2006-01-01
Full Text Available A fast identification of insufficiency of nutrients using spectral features would be a useful instrument in farming and in other nutrient demanding agricultural systems such as those proposed for long period space missions. A Multilayer Perceptron (MLP neural network and backpropagation algorithm was used to differentiate between normal leaves of wheat (Triticum aestivum L. and those deficient in nitrogen, phosphorus, (K and (Ca using hyperspectral data. The network consisted of three layers with spectral reflectance of the leaves in wavelengths from 401 to 770 nm as the input layer and the nutrient concentrations as the output layer. Based upon the values of actual nutrient concentrations (mg L-1, plants were classified as either deficient or standard. Wheat plants were grown for .100 days under both hydroponic conditions in the greenhouse and vermiculite media in a growth chamber using Hoagland`s complete nutrient solution with selected minerals eliminated to induce specific nutrient deficiencies. Check plants received complete nutrient solutions. The MLP model was trained and tested successfully within 1000 epochs as the MSE of the sample-training curve approached zero. The backpropagation algorithm functioned well with the following accuracies for the classification model: N 90.9, P 100, K 90 and Ca 100%. Using the regression model, the following accuracies were obtained: N 93.0, P 87.2, K 91.9 and Ca 97.4%. This affirms the potential of using spectral data coupled with either a classification or regression neural network models to quickly categorize leaves deficient in these four major minerals so that remedial applications of those nutrients can be made before the yield is drastically affected.
Representation of Functional Data in Neural Networks
Rossi, Fabrice; Conan-Guez, Brieuc; Verleysen, Michel
2005-01-01
Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in the analysis methods. This paper shows how to extend the Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models to functional data inputs, in particular when the latter are known through lists of input-output pairs. Various possibilities for functional processing are discussed, including the projection on smooth bases, Functional Principal Component Analysis, functional centering and reduction, and the use of differential operators. It is shown how to incorporate these functional processing into the RBFN and MLP models. The functional approach is illustrated on a benchmark of spectrometric data ana...
GenSo-EWS: a novel neural-fuzzy based early warning system for predicting bank failures.
Tung, W L; Quek, C; Cheng, P
2004-05-01
Bank failure prediction is an important issue for the regulators of the banking industries. The collapse and failure of a bank could trigger an adverse financial repercussion and generate negative impacts such as a massive bail out cost for the failing bank and loss of confidence from the investors and depositors. Very often, bank failures are due to financial distress. Hence, it is desirable to have an early warning system (EWS) that identifies potential bank failure or high-risk banks through the traits of financial distress. Various traditional statistical models have been employed to study bank failures [J Finance 1 (1975) 21; J Banking Finance 1 (1977) 249; J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073]. However, these models do not have the capability to identify the characteristics of financial distress and thus function as black boxes. This paper proposes the use of a new neural fuzzy system [Foundations of neuro-fuzzy systems, 1997], namely the Generic Self-organising Fuzzy Neural Network (GenSoFNN) [IEEE Trans Neural Networks 13 (2002c) 1075] based on the compositional rule of inference (CRI) [Commun ACM 37 (1975) 77], as an alternative to predict banking failure. The CRI based GenSoFNN neural fuzzy network, henceforth denoted as GenSoFNN-CRI(S), functions as an EWS and is able to identify the inherent traits of financial distress based on financial covariates (features) derived from publicly available financial statements. The interaction between the selected features is captured in the form of highly intuitive IF-THEN fuzzy rules. Such easily comprehensible rules provide insights into the possible characteristics of financial distress and form the knowledge base for a highly desired EWS that aids bank regulation. The performance of the GenSoFNN-CRI(S) network is subsequently benchmarked against that of the Cox's proportional hazards model [J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073], the multi-layered perceptron (MLP) and the modified cerebellar model articulation controller (MCMAC) [IEEE Trans Syst Man Cybern: Part B 30 (2000) 491] in predicting bank failures based on a population of 3635 US banks observed over a 21 years period. Three sets of experiments are performed-bank failure classification based on the last available financial record and prediction using financial records one and two years prior to the last available financial statements. The performance of the GenSoFNN-CRI(S) network as a bank failure classification and EWS is encouraging. PMID:15109685
Hruschka, Harald
2000-01-01
Attraction models are very popular in marketing research for studying the effects of marketing instruments on market shares. However, so far the marketing literature only considers attraction models with certain functional forms that exclude threshold or saturation effects on attraction values. We can achieve greater exibility by using the neural net based approach introduced here. This approach assesses brands' attraction values by means of a perceptron with one hidden layer. The approach us...
Deepthi, Dasika Ratna; Krishna, G. R. Aditya; Eswaran, K.
2007-01-01
This paper proposes an unsupervised learning technique by using Multi-layer Mirroring Neural Network and Forgy's clustering algorithm. Multi-layer Mirroring Neural Network is a neural network that can be trained with generalized data inputs (different categories of image patterns) to perform non-linear dimensionality reduction and the resultant low-dimensional code is used for unsupervised pattern classification using Forgy's algorithm. By adapting the non-linear activation ...
Accurate Wavelet Neural Network for Efficient Controlling of an Active Magnetic Bearing System
Directory of Open Access Journals (Sweden)
Youssef Harkouss
2010-01-01
Full Text Available Problem statement: The synthesis of a command by the neural network has an excellent advantage over the classical one such as PID. This study presented a fast and accurate Wavelet Neural Network (WNN approach for efficient controlling of an Active Magnetic Bearing (AMB system. Approach: The proposed approach combined neural network with the wavelet theory. Wavelet theory may be exploited in deriving a good initialization for the neural network and thus improved convergence of the learning algorithm. Results: We tested two control systems based on three types of neural controllers: Multiplayer Perceptron (MLP controller, RBF Neural Network (RBFNN controller and WNN controller. The simulation results show that the proposed WNN controller provides better performance comparing with standard PID controller, MLP and RBFNN controllers. Conclusion: The proposed WNN approach was shown to be useful in controlling nonlinear dynamic mechanical system, such as the AMB system used in this study.
Control of a Uniform Step Asymmetrical 9-Level Inverter Based on Artificial Neural Network Strategy
Directory of Open Access Journals (Sweden)
Rachid Taleb
2009-12-01
Full Text Available A neural implementation of a harmonic elimination strategy for the control auniform step asymmetrical 9-level inverter is proposed and described in this paper. AMulti-Layer Perceptrons (MLP neural network is used to approximate the mappingbetween the modulation rate and the required switching angles. After learning, the neuralnetwork generates the appropriate switching angles for the inverter. This leads to a lowcomputational-cost neural controller which is therefore well suited for real-timeapplications. This neural approach is compared to the well-known Multi-Carrier Pulse-Width Modulation (MCPWM. Simulation results demonstrate the technical advantages ofthe neural implementation of the harmonic elimination strategy over the conventionalmethod for the control of an uniform step asymmetrical 9-level inverter. The approach isused to supply an asynchronous machine and results show that the neural method ensures ahighest quality torque by efficiently canceling the harmonics generated by the inverter.
Scientific Electronic Library Online (English)
Luiz Moreira, Coelho Junior; José Luiz Pereira de, Rezende; André Luiz França, Batista; Adriano Ribeiro de, Mendonça; Wilian Soares, Lacerda.
2013-06-01
Full Text Available A energia é um importante fator de crescimento econômico e vital para a estabilidade de uma nação. O carvão vegetal é um recurso energético renovável, um dos insumos básicos responsáveis pelo desenvolvimento das indústrias de base florestal no Brasil. Objetivou-se, neste artigo, fazer a prognose par [...] a o ano de 2007 da série de preços do carvão vegetal, utilizando as Redes Neurais Artificiais. Foi utilizada a RNA perceptron de camadas múltiplas, feed-forward, cujos resultados são próximos da realidade. Os principais resultados encontrados foram: os preços reais do carvão vegetal foram declinantes no período de 1975 a 2000 e crescentes a partir do início do século XXI; a arquitetura da Rede Neural Artificial que realizou melhor previsão foi a com duas camadas escondidas; a taxa de aprendizagem mais eficiente foi de 0,99 e 600 ciclos, que representou treinamento da RNA mais satisfatório e mais preciso. A previsão, usando a RNA, se mostrou mais precisa quando comparada pelo erro quadrático médio de previsão de outros estudos para a série de preços de carvão vegetal em Minas Gerais. Abstract in english Energy is an important factor of economic growth and is critical to the stability of a nation. Charcoal is a renewable energy resource and is a fundamental input to the development of the Brazilian forest-based industry. The objective of this study is to provide a prognosis of the charcoal price ser [...] ies for the year 2007 by using Artificial Neural Networks. A feedforward multilayer perceptron ANN was used, the results of which are close to reality. The main findings are that: real prices of charcoal dropped between 1975 and 2000 and rose from the early 21st century; the ANN with two hidden layers was the architecture making the best prediction; the most effective learning rate was 0.99 and 600 cycles, representing the most satisfactory and accurate ANN training. Prediction using ANN was found to be more accurate when compared by the mean squared error to other studies modeling charcoal price series in Minas Gerais state.
Using Probabilistic Neural Networks for Handwritten Digit Recognition
Directory of Open Access Journals (Sweden)
Abdelkader Benyettou
2011-01-01
Full Text Available Artificial neural networks are well known in the field of pattern recognition and machine learning. Multi-layer neural networks are usually used as universal neural classifiers even though probabilistic neural networks represent a special type of artificial neural networks and have been designed to be used mainly in classification problems. In this article a study has been conducted to train a probabilistic neural network to recognize handwritten digits taken from the MINST database for handwritten digits. Results presented in this paper show good performance and generalization capacity of the proposed network for a real-world big database and no deep tuning of the parameters is required.
Directory of Open Access Journals (Sweden)
R Noori
2009-03-01
Full Text Available "nBackground: Municipal solid waste (MSW is the natural result of human activities. MSW generation modeling is of prime importance in designing and programming municipal solid waste management system. This study tests the short-term prediction of waste generation by artificial neural network (ANN and principal component-regression analysis."nMethods: Two forecasting techniques are presented in this paper for prediction of waste generation (WG. One of them, multivariate linear regression (MLR, is based on principal component analysis (PCA. The other technique is ANN model. For ANN, a feed-forward multi-layer perceptron was considered the best choice for this study. However, in this research after removing the problem of multicolinearity of independent variables by PCA, an appropriate model (PCA-MLR was developed for predicting WG."nResults: Correlation coefficient (R and average absolute relative error (AARE in ANN model obtained as equal to 0.837 and 4.4% respectively. In comparison whit PCA-MLR model (R= 0.445, MARE= 6.6%, ANN model has a better results. However, threshold statistic error is done for the both models in the testing stage that the maximum absolute relative error (ARE for 50% of prediction is 3.7% in ANN model but it is 6.2% for PCA-MLR model. Also we can say that the maximum ARE for 90% of prediction in testing step of ANN model is about 8.6% but it is 10.5% for PCA-MLR model."nConclusion: The ANN model has better results in comparison with the PCA-MLR model therefore this model is selected for prediction of WG in Tehran.
Directory of Open Access Journals (Sweden)
FÃ¡bio ConceiÃ§Ã£o de Menezes
2009-12-01
Full Text Available A coagulaÃ§Ã£o Ã© uma etapa de tratamento da Ã¡gua, e para tal sÃ£o realizados ensaios de teste de jarro que permitem determinar a dose necessÃ¡ria dos agentes coagulante e quÃmico de ajuste de pH no processo de coagulaÃ§Ã£o. Contudo, esses ensaios demoram a ser executados, nÃ£o respondendo em tempo real Ã s mudanÃ§as da qualidade da Ã¡gua bruta. Para superar tal limitaÃ§Ã£o, redes neurais artificiais multicamadas foram construÃdas (e seus pesos sinÃ¡pticos ajustados, validadas e testadas para predizer a dosagem do hidrÃ³xido de sÃ³dio e do sulfato de alumÃnio - utilizados como agentes quÃmico de ajuste de pH e coagulante, respectivamente. Os resultados dos modelos obtidos sÃ£o compatÃveis com os dados experimentais tendo em vista que as incertezas das estimativas estÃ£o na mesma ordem de grandeza das faixas indicadas pelos ensaios realizados de testes de jarro ao longo de quase seis anos.Coagulation is a stage in water treatment and, for this, jar tests are performed, which allows determining the optimal coagulant and alkalizer doses in coagulation process. However, these tests are time-consuming and do not enable real-time responses to changes in raw water quality. To overcome these limitations, artificial multilayer perceptron neural networks were built, trained, validated and tested to predict the aluminum and sodium hydroxide doses - used as coagulant and alkalizer, respectively. The results of these models are encouraging to consider that the estimated uncertainties have the same order of the variation limits magnitude indicated by the jar tests for almost a six-year period.
Khan, Zaheer Ullah; Hayat, Maqsood; Khan, Muazzam Ali
2015-01-21
Enzyme catalysis is one of the most essential and striking processes among of all the complex processes that have evolved in living organisms. Enzymes are biological catalysts, which play a significant role in industrial applications as well as in medical areas, due to profound specificity, selectivity and catalytic efficiency. Refining catalytic efficiency of enzymes has become the most challenging job of enzyme engineering, into acidic and alkaline. Discrimination of acidic and alkaline enzymes through experimental approaches is difficult, sometimes impossible due to lack of established structures. Therefore, it is highly desirable to develop a computational model for discriminating acidic and alkaline enzymes from primary sequences. In this study, we have developed a robust, accurate and high throughput computational model using two discrete sample representation methods Pseudo amino acid composition (PseAAC) and split amino acid composition. Various classification algorithms including probabilistic neural network (PNN), K-nearest neighbor, decision tree, multi-layer perceptron and support vector machine are applied to predict acidic and alkaline with high accuracy. 10-fold cross validation test and several statistical measures namely, accuracy, F-measure, and area under ROC are used to evaluate the performance of the proposed model. The performance of the model is examined using two benchmark datasets to demonstrate the effectiveness of the model. The empirical results show that the performance of PNN in conjunction with PseAAC is quite promising compared to existing approaches in the literature so for. It has achieved 96.3% accuracy on dataset1 and 99.2% on dataset2. It is ascertained that the proposed model might be useful for basic research and drug related application areas. PMID:25452135
Some applications of neural networks in microwave modeling
Directory of Open Access Journals (Sweden)
Milovanovi? Bratislav D.
2003-01-01
Full Text Available This paper presents some applications of neural networks in the microwave modeling. The applications are related to modeling of either passive or active structures and devices. Modeling is performed using not only simple multilayer perception network (MLP but also advanced knowledge based neural network (KBNN structures.
Uezu, T
2001-01-01
In a previous letter, we studied learning from stochastic examples by perceptrons with Ising weights in the framework of statistical mechanics. Under the one-step replica symmetry breaking ansatz, the behaviours of learning curves were classified according to some local property of the rules by which examples were drawn. Further, the conditions for the existence of the Perfect Learning together with other behaviors of the learning curves were given. In this paper, we give the detailed derivation about these results and further argument about the Perfect Learning together with extensive numerical calculations.
A comparison of artificial neural networks used for river forecasting
Dawson, C W; Wilby, R. L.
1999-01-01
This paper compares the performance of two artificial neural network (ANN) models ? the multi layer perceptron (MLP) and the radial basis function network (RBF) ? with a stepwise multiple linear regression model (SWMLR) and zero order forecasts (ZOF) of river flow. All models were trained using 15 minute rainfall-runoff data for the River Mole, a flood-prone tributary of the River Thames, UK. The models were then used to forecast river flows with a 6 hour lead time and 15 minute resolution, g...
Prediction of hydrocyclone performance using artificial neural networks
Scientific Electronic Library Online (English)
M., Karimi; A., Dehghani; A., Nezamalhosseini; Sh., Talebi.
2010-05-01
Full Text Available Artificial neural networks (ANNs) have found their applications in the modelling of unit operations of mineral processing plants. In this research, laboratory-scale tests were conducted, using a three-inch diameter Mozley hydrocyclone. Main parameters included pressure drop at inlet, solid per cent, [...] vortex and apex diameter were adjusted. The corrected cut size (d50c) and the flow rates of underflow and overflow were determined. Multi layers perceptron (MLP) feed forward network architectures were designed to predict the responses. The results showed a good correlation between experimental and network output, for corrected cut size and flow rates.