Backpropagation neural networks: pattern recognition
Studenikin, Oleg
2005-01-01
In this Master’s degree work artificial neural networks and back propagation learning algorithm for human faces and pattern recognition are analyzed. In the second part of work artificial neural networks and their architecture and structures models are analyzed. In the third part of article the backpropagation procedure and procedures theoretical learning principle are analyzed. In the fourth part different kinds of ANN methods and patterns extracting methods in recognition, learning and ...
Neural network construction via back-propagation
International Nuclear Information System (INIS)
Burwick, T.T.
1994-06-01
A method is presented that combines back-propagation with multi-layer neural network construction. Back-propagation is used not only to adjust the weights but also the signal functions. Going from one network to an equivalent one that has additional linear units, the non-linearity of these units and thus their effective presence is then introduced via back-propagation (weight-splitting). The back-propagated error causes the network to include new units in order to minimize the error function. We also show how this formalism allows to escape local minima
Training Deep Spiking Neural Networks Using Backpropagation.
Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael
2016-01-01
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.
Tropical Timber Identification using Backpropagation Neural Network
Siregar, B.; Andayani, U.; Fatihah, N.; Hakim, L.; Fahmi, F.
2017-01-01
Each and every type of wood has different characteristics. Identifying the type of wood properly is important, especially for industries that need to know the type of timber specifically. However, it requires expertise in identifying the type of wood and only limited experts available. In addition, the manual identification even by experts is rather inefficient because it requires a lot of time and possibility of human errors. To overcome these problems, a digital image based method to identify the type of timber automatically is needed. In this study, backpropagation neural network is used as artificial intelligence component. Several stages were developed: a microscope image acquisition, pre-processing, feature extraction using gray level co-occurrence matrix and normalization of data extraction using decimal scaling features. The results showed that the proposed method was able to identify the timber with an accuracy of 94%.
Prediction of tides using back-propagation neural networks
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.
Prediction of tides is very much essential for human activities and to reduce the construction cost in marine environment. This paper presents an application of the artificial neural network with back-propagation procedures for accurate prediction...
Analysis Resilient Algorithm on Artificial Neural Network Backpropagation
Saputra, Widodo; Tulus; Zarlis, Muhammad; Widia Sembiring, Rahmat; Hartama, Dedy
2017-12-01
Prediction required by decision makers to anticipate future planning. Artificial Neural Network (ANN) Backpropagation is one of method. This method however still has weakness, for long training time. This is a reason to improve a method to accelerate the training. One of Artificial Neural Network (ANN) Backpropagation method is a resilient method. Resilient method of changing weights and bias network with direct adaptation process of weighting based on local gradient information from every learning iteration. Predicting data result of Istanbul Stock Exchange training getting better. Mean Square Error (MSE) value is getting smaller and increasing accuracy.
Kontrol Kecepatan Motor Induksi menggunakan Algoritma Backpropagation Neural Network
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MUHAMMAD RUSWANDI DJALAL
2017-07-01
Full Text Available ABSTRAKBanyak strategi kontrol berbasis kecerdasan buatan telah diusulkan dalam penelitian seperti Fuzzy Logic dan Artificial Neural Network (ANN. Tujuan dari penelitian ini adalah untuk mendesain sebuah kontrol agar kecepatan motor induksi dapat diatur sesuai kebutuhan serta membandingkan kinerja motor induksi tanpa kontrol dan dengan kontrol. Dalam penelitian ini diusulkan sebuah metode artificial neural network untuk mengontrol kecepatan motor induksi tiga fasa. Kecepatan referensi motor diatur pada kecepatan 140 rad/s, 150 rad/s, dan 130 rad/s. Perubahan kecepatan diatur pada setiap interval 0.3 detik dan waktu simulasi maksimum adalah 0,9 detik. Kasus 1 tanpa kontrol, menunjukkan respon torka dan kecepatan dari motor induksi tiga fasa tanpa kontrol. Meskipun kecepatan motor induksi tiga fasa diatur berubah pada setiap 0,3 detik tidak akan mempengaruhi torka. Selain itu, motor induksi tiga fasa tanpa kontrol memiliki kinerja yang buruk dikarenakan kecepatan motor induksi tidak dapat diatur sesuai dengan kebutuhan. Kasus 2 dengan control backpropagation neural network, meskipun kecepatan motor induksi tiga fasa berubah pada setiap 0.3 detik tidak akan mempengaruhi torsi. Selain itu, kontrol backpropagation neural network memiliki kinerja yang baik dikarenakan kecepatan motor induksi dapat diatur sesuai dengan kebutuhan.Kata kunci: Backpropagation Neural Network (BPNN, NN Training, NN Testing, Motor.ABSTRACTMany artificial intelligence-based control strategies have been proposed in research such as Fuzzy Logic and Artificial Neural Network (ANN. The purpose of this research was design a control for the induction motor speed that could be adjusted as needed and compare the performance of induction motor without control and with control. In this research, it was proposed an artificial neural network method to control the speed of three-phase induction motors. The reference speed of motor was set at the rate of 140 rad / s, 150 rad / s, and 130
LVQ and backpropagation neural networks applied to NASA SSME data
Doniere, Timothy F.; Dhawan, Atam P.
1993-01-01
Feedfoward neural networks with backpropagation learning have been used as function approximators for modeling the space shuttle main engine (SSME) sensor signals. The modeling of these sensor signals is aimed at the development of a sensor fault detection system that can be used during ground test firings. The generalization capability of a neural network based function approximator depends on the training vectors which in this application may be derived from a number of SSME ground test-firings. This yields a large number of training vectors. Large training sets can cause the time required to train the network to be very large. Also, the network may not be able to generalize for large training sets. To reduce the size of the training sets, the SSME test-firing data is reduced using the learning vector quantization (LVQ) based technique. Different compression ratios were used to obtain compressed data in training the neural network model. The performance of the neural model trained using reduced sets of training patterns is presented and compared with the performance of the model trained using complete data. The LVQ can also be used as a function approximator. The performance of the LVQ as a function approximator using reduced training sets is presented and compared with the performance of the backpropagation network.
Ding, Weifu; Zhang, Jiangshe; Leung, Yee
2016-10-01
In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained based on sparse response back-propagation in which only a small number of neurons respond to the specified stimulus simultaneously and provide a high convergence rate for the trained network, in addition to low energy consumption and greater generalization. Our method is evaluated on Hong Kong air monitoring station data and corresponding meteorological variables for which five air quality parameters were gathered at four monitoring stations in Hong Kong over 4 years (2012-2015). Our results show that our training method has more advantages in terms of the precision of the prediction, effectiveness, and generalization of traditional linear regression algorithms when compared with a feedforward artificial neural network trained using traditional back-propagation.
An approach to the interpretation of backpropagation neural network models in QSAR studies.
Baskin, I I; Ait, A O; Halberstam, N M; Palyulin, V A; Zefirov, N S
2002-03-01
An approach to the interpretation of backpropagation neural network models for quantitative structure-activity and structure-property relationships (QSAR/QSPR) studies is proposed. The method is based on analyzing the first and second moments of distribution of the values of the first and the second partial derivatives of neural network outputs with respect to inputs calculated at data points. The use of such statistics makes it possible not only to obtain actually the same characteristics as for the case of traditional "interpretable" statistical methods, such as the linear regression analysis, but also to reveal important additional information regarding the non-linear character of QSAR/QSPR relationships. The approach is illustrated by an example of interpreting a backpropagation neural network model for predicting position of the long-wave absorption band of cyane dyes.
Wanto, Anjar; Zarlis, Muhammad; Sawaluddin; Hartama, Dedy
2017-12-01
Backpropagation is a good artificial neural network algorithm used to predict, one of which is to predict the rate of Consumer Price Index (CPI) based on the foodstuff sector. While conjugate gradient fletcher reeves is a suitable optimization method when juxtaposed with backpropagation method, because this method can shorten iteration without reducing the quality of training and testing result. Consumer Price Index (CPI) data that will be predicted to come from the Central Statistics Agency (BPS) Pematangsiantar. The results of this study will be expected to contribute to the government in making policies to improve economic growth. In this study, the data obtained will be processed by conducting training and testing with artificial neural network backpropagation by using parameter learning rate 0,01 and target error minimum that is 0.001-0,09. The training network is built with binary and bipolar sigmoid activation functions. After the results with backpropagation are obtained, it will then be optimized using the conjugate gradient fletcher reeves method by conducting the same training and testing based on 5 predefined network architectures. The result, the method used can increase the speed and accuracy result.
IMPLEMENTASI BACKPROPAGATION NEURAL NETWORK DALAM PRAKIRAAN CUACA DI DAERAH BALI SELATAN
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I MADE DWI UDAYANA PUTRA
2016-11-01
Full Text Available Weather information has an important role in human life in various fields, such as agriculture, marine, and aviation. The accurate weather forecasts are needed in order to improve the performance of various fields. In this study, use artificial neural network method with backpropagation learning algorithm to create a model of weather forecasting in the area of ??South Bali. The aim of this study is to determine the effect of the number of neurons in the hidden layer and to determine the level of accuracy of the method of artificial neural network with backpropagation learning algorithm in weather forecast models. Weather forecast models in this study use input of the factors that influence the weather, namely air temperature, dew point, wind speed, visibility, and barometric pressure.The results of testing the network with a different number of neurons in the hidden layer of artificial neural network method with backpropagation learning algorithms show that the increase in the number of neurons in the hidden layer is not directly proportional to the value of the accuracy of the weather forecasts, the increase in the number of neurons in the hidden layer does not necessarily increase or decrease value accuracy of weather forecasts we obtain the best accuracy rate of 51.6129% on a network model with three neurons in the hidden layer.
Witoonchart, Peerajak; Chongstitvatana, Prabhas
2017-08-01
In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Ikhthison Mekongga
2014-02-01
Full Text Available The need for bandwidth has been increasing recently. This is because the development of internet infrastructure is also increasing so that we need an economic and efficient provider system. This can be achieved through good planning and a proper system. The prediction of the bandwidth consumption is one of the factors that support the planning for an efficient internet service provider system. Bandwidth consumption is predicted using ANN. ANN is an information processing system which has similar characteristics as the biologic al neural network. ANN is chosen to predict the consumption of the bandwidth because ANN has good approachability to non-linearity. The variable used in ANN is the historical load data. A bandwidth consumption information system was built using neural networks with a backpropagation algorithm to make the use of bandwidth more efficient in the future both in the rental rate of the bandwidth and in the usage of the bandwidth.Keywords: Forecasting, Bandwidth, Backpropagation
Application of artificial neural networks with backpropagation technique in the financial data
Jaiswal, Jitendra Kumar; Das, Raja
2017-11-01
The propensity of applying neural networks has been proliferated in multiple disciplines for research activities since the past recent decades because of its powerful control with regulatory parameters for pattern recognition and classification. It is also being widely applied for forecasting in the numerous divisions. Since financial data have been readily available due to the involvement of computers and computing systems in the stock market premises throughout the world, researchers have also developed numerous techniques and algorithms to analyze the data from this sector. In this paper we have applied neural network with backpropagation technique to find the data pattern from finance section and prediction for stock values as well.
Energy Technology Data Exchange (ETDEWEB)
Kerr, John Patrick [Iowa State Univ., Ames, IA (United States)
1992-01-01
The objective of this study was to determine the feasibility of using an Artificial Neural Network (ANN), in particular a backpropagation ANN, to improve the speed and quality of the reconstruction of three-dimensional SPECT (single photon emission computed tomography) images. In addition, since the processing elements (PE)s in each layer of an ANN are independent of each other, the speed and efficiency of the neural network architecture could be better optimized by implementing the ANN on a massively parallel computer. The specific goals of this research were: to implement a fully interconnected backpropagation neural network on a serial computer and a SIMD parallel computer, to identify any reduction in the time required to train these networks on the parallel machine versus the serial machine, to determine if these neural networks can learn to recognize SPECT data by training them on a section of an actual SPECT image, and to determine from the knowledge obtained in this research if full SPECT image reconstruction by an ANN implemented on a parallel computer is feasible both in time required to train the network, and in quality of the images reconstructed.
A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification
Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun
2016-01-01
Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value. PMID:27905520
A modified backpropagation algorithm for training neural networks on data with error bars
International Nuclear Information System (INIS)
Gernoth, K.A.; Clark, J.W.
1994-08-01
A method is proposed for training multilayer feedforward neural networks on data contaminated with noise. Specifically, we consider the case that the artificial neural system is required to learn a physical mapping when the available values of the target variable are subject to experimental uncertainties, but are characterized by error bars. The proposed method, based on maximum likelihood criterion for parameter estimation, involves simple modifications of the on-line backpropagation learning algorithm. These include incorporation of the error-bar assignments in a pattern-specific learning rate, together with epochal updating of a new measure of model accuracy that replaces the usual mean-square error. The extended backpropagation algorithm is successfully tested on two problems relevant to the modelling of atomic-mass systematics by neural networks. Provided the underlying mapping is reasonably smooth, neural nets trained with the new procedure are able to learn the true function to a good approximation even in the presence of high levels of Gaussian noise. (author). 26 refs, 2 figs, 5 tabs
Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network
International Nuclear Information System (INIS)
Zeng, Yu-Rong; Zeng, Yi; Choi, Beomjin; Wang, Lin
2017-01-01
Reliable energy consumption forecasting can provide effective decision-making support for planning development strategies to energy enterprises and for establishing national energy policies. Accordingly, the present study aims to apply a hybrid intelligent approach named ADE–BPNN, the back-propagation neural network (BPNN) model supported by an adaptive differential evolution algorithm, to estimate energy consumption. Most often, energy consumption is influenced by socioeconomic factors. The proposed hybrid model incorporates gross domestic product, population, import, and export data as inputs. An improved differential evolution with adaptive mutation and crossover is utilized to find appropriate global initial connection weights and thresholds to enhance the forecasting performance of the BPNN. A comparative example and two extended examples are utilized to validate the applicability and accuracy of the proposed ADE–BPNN model. Errors of the test data sets indicate that the ADE–BPNN model can effectively predict energy consumption compared with the traditional back-propagation neural network model and other popular existing models. Moreover, mean impact value based analysis is conducted for electrical energy consumption in U.S. and total energy consumption forecasting in China to quantitatively explore the relative importance of each input variable for the improvement of effective energy consumption prediction. - Highlights: • Enhanced back-propagation neural network (ADE-BPNN) for energy consumption forecasting. • ADE-BPNN outperforms the current best models for two comparative cases. • Mean impact value approach explores socio-economic factors' relative importance. • ADE-BPNN's adjusted goodness-of-fit is 99.2% for China's energy consumption forecasting.
Wutsqa, D. U.; Marwah, M.
2017-06-01
In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.
A new backpropagation learning algorithm for layered neural networks with nondifferentiable units.
Oohori, Takahumi; Naganuma, Hidenori; Watanabe, Kazuhisa
2007-05-01
We propose a digital version of the backpropagation algorithm (DBP) for three-layered neural networks with nondifferentiable binary units. This approach feeds teacher signals to both the middle and output layers, whereas with a simple perceptron, they are given only to the output layer. The additional teacher signals enable the DBP to update the coupling weights not only between the middle and output layers but also between the input and middle layers. A neural network based on DBP learning is fast and easy to implement in hardware. Simulation results for several linearly nonseparable problems such as XOR demonstrate that the DBP performs favorably when compared to the conventional approaches. Furthermore, in large-scale networks, simulation results indicate that the DBP provides high performance.
Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network
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Kindie Biredagn Nahato
2015-01-01
Full Text Available The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets.
Facial Expression Recognition By Using Fisherface Methode With Backpropagation Neural Network
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Zaenal Abidin
2011-01-01
Full Text Available Abstract— In daily lives, especially in interpersonal communication, face often used for expression. Facial expressions give information about the emotional state of the person. A facial expression is one of the behavioral characteristics. The components of a basic facial expression analysis system are face detection, face data extraction, and facial expression recognition. Fisherface method with backpropagation artificial neural network approach can be used for facial expression recognition. This method consists of two-stage process, namely PCA and LDA. PCA is used to reduce the dimension, while the LDA is used for features extraction of facial expressions. The system was tested with 2 databases namely JAFFE database and MUG database. The system correctly classified the expression with accuracy of 86.85%, and false positive 25 for image type I of JAFFE, for image type II of JAFFE 89.20% and false positive 15, for type III of JAFFE 87.79%, and false positive for 16. The image of MUG are 98.09%, and false positive 5. Keywords— facial expression, fisherface method, PCA, LDA, backpropagation neural network.
Ueda, Michihito; Nishitani, Yu; Kaneko, Yukihiro; Omote, Atsushi
2014-01-01
To realize an analog artificial neural network hardware, the circuit element for synapse function is important because the number of synapse elements is much larger than that of neuron elements. One of the candidates for this synapse element is a ferroelectric memristor. This device functions as a voltage controllable variable resistor, which can be applied to a synapse weight. However, its conductance shows hysteresis characteristics and dispersion to the input voltage. Therefore, the conductance values vary according to the history of the height and the width of the applied pulse voltage. Due to the difficulty of controlling the accurate conductance, it is not easy to apply the back-propagation learning algorithm to the neural network hardware having memristor synapses. To solve this problem, we proposed and simulated a learning operation procedure as follows. Employing a weight perturbation technique, we derived the error change. When the error reduced, the next pulse voltage was updated according to the back-propagation learning algorithm. If the error increased the amplitude of the next voltage pulse was set in such way as to cause similar memristor conductance but in the opposite voltage scanning direction. By this operation, we could eliminate the hysteresis and confirmed that the simulation of the learning operation converged. We also adopted conductance dispersion numerically in the simulation. We examined the probability that the error decreased to a designated value within a predetermined loop number. The ferroelectric has the characteristics that the magnitude of polarization does not become smaller when voltages having the same polarity are applied. These characteristics greatly improved the probability even if the learning rate was small, if the magnitude of the dispersion is adequate. Because the dispersion of analog circuit elements is inevitable, this learning operation procedure is useful for analog neural network hardware.
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Michihito Ueda
Full Text Available To realize an analog artificial neural network hardware, the circuit element for synapse function is important because the number of synapse elements is much larger than that of neuron elements. One of the candidates for this synapse element is a ferroelectric memristor. This device functions as a voltage controllable variable resistor, which can be applied to a synapse weight. However, its conductance shows hysteresis characteristics and dispersion to the input voltage. Therefore, the conductance values vary according to the history of the height and the width of the applied pulse voltage. Due to the difficulty of controlling the accurate conductance, it is not easy to apply the back-propagation learning algorithm to the neural network hardware having memristor synapses. To solve this problem, we proposed and simulated a learning operation procedure as follows. Employing a weight perturbation technique, we derived the error change. When the error reduced, the next pulse voltage was updated according to the back-propagation learning algorithm. If the error increased the amplitude of the next voltage pulse was set in such way as to cause similar memristor conductance but in the opposite voltage scanning direction. By this operation, we could eliminate the hysteresis and confirmed that the simulation of the learning operation converged. We also adopted conductance dispersion numerically in the simulation. We examined the probability that the error decreased to a designated value within a predetermined loop number. The ferroelectric has the characteristics that the magnitude of polarization does not become smaller when voltages having the same polarity are applied. These characteristics greatly improved the probability even if the learning rate was small, if the magnitude of the dispersion is adequate. Because the dispersion of analog circuit elements is inevitable, this learning operation procedure is useful for analog neural network hardware.
An intelligent switch with back-propagation neural network based hybrid power system
Perdana, R. H. Y.; Fibriana, F.
2018-03-01
The consumption of conventional energy such as fossil fuels plays the critical role in the global warming issues. The carbon dioxide, methane, nitrous oxide, etc. could lead the greenhouse effects and change the climate pattern. In fact, 77% of the electrical energy is generated from fossil fuels combustion. Therefore, it is necessary to use the renewable energy sources for reducing the conventional energy consumption regarding electricity generation. This paper presents an intelligent switch to combine both energy resources, i.e., the solar panels as the renewable energy with the conventional energy from the State Electricity Enterprise (PLN). The artificial intelligence technology with the back-propagation neural network was designed to control the flow of energy that is distributed dynamically based on renewable energy generation. By the continuous monitoring on each load and source, the dynamic pattern of the intelligent switch was better than the conventional switching method. The first experimental results for 60 W solar panels showed the standard deviation of the trial at 0.7 and standard deviation of the experiment at 0.28. The second operation for a 900 W of solar panel obtained the standard deviation of the trial at 0.05 and 0.18 for the standard deviation of the experiment. Moreover, the accuracy reached 83% using this method. By the combination of the back-propagation neural network with the observation of energy usage of the load using wireless sensor network, each load can be evenly distributed and will impact on the reduction of conventional energy usage.
The Performance of EEG-P300 Classification using Backpropagation Neural Networks
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Arjon Turnip
2013-12-01
Full Text Available Electroencephalogram (EEG recordings signal provide an important function of brain-computer communication, but the accuracy of their classification is very limited in unforeseeable signal variations relating to artifacts. In this paper, we propose a classification method entailing time-series EEG-P300 signals using backpropagation neural networks to predict the qualitative properties of a subject’s mental tasks by extracting useful information from the highly multivariate non-invasive recordings of brain activity. To test the improvement in the EEG-P300 classification performance (i.e., classification accuracy and transfer rate with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA. Finally, the result of the experiment showed that the average of the classification accuracy was 97% and the maximum improvement of the average transfer rate is 42.4%, indicating the considerable potential of the using of EEG-P300 for the continuous classification of mental tasks.
Izhari, F.; Dhany, H. W.; Zarlis, M.; Sutarman
2018-03-01
A good age in optimizing aspects of development is at the age of 4-6 years, namely with psychomotor development. Psychomotor is broader, more difficult to monitor but has a meaningful value for the child's life because it directly affects his behavior and deeds. Therefore, there is a problem to predict the child's ability level based on psychomotor. This analysis uses backpropagation method analysis with artificial neural network to predict the ability of the child on the psychomotor aspect by generating predictions of the child's ability on psychomotor and testing there is a mean squared error (MSE) value at the end of the training of 0.001. There are 30% of children aged 4-6 years have a good level of psychomotor ability, excellent, less good, and good enough.
International Nuclear Information System (INIS)
Vasconcelos, W.L.; Shigaki, Y.; Tolentino, E.
2009-01-01
In this work it was analyzed the residual performance of Portland cement concretes, when cold after heat-treated up to 600 deg C. Granite-gneiss was used in the three concrete mix proportions as the coarse aggregate, and river sand with finesses modulus of 2.7 as the fine aggregate. Ultrasonic pulse tests were performed on all the specimens and ultrasonic dynamic modulus were obtained. An artificial neural network of the backpropagation type was trained to evaluate and apply models in predicting residual properties of Portland cement concretes. The input layer for both models consists of an external layer input vector of the temperature. The hidden layer has two processing units with hyperbolic tangent sigmoid transfer functions (tansig for short), and the output layer contains one processing unit that represents the network's output (ultrasonic pulse velocity or modulus of elasticity) for each input vector. The training phase of the network converged for reasonable results after 5.000 epochs approximately, resulting in mean squared errors less than 0.02 for the normalized data. The neural network developed for modeling residual properties of Portland cement concretes was shown to be efficient in both the training phase and the test. From the results reasonable predictions could be made for the ultrasonic pulse velocity or dynamic modulus of elasticity by using temperature. (author)
Khuriati, Ainie; Setiabudi, Wahyu; Nur, Muhammad; Istadi, Istadi
2015-12-01
Backpropgation neural network was trained to predict of combustible fraction heating value of MSW from the physical composition. Waste-to-Energy (WtE) is a viable option for municipal solid waste (MSW) management. The influence of the heating value of municipal solid waste (MSW) is very important on the implementation of WtE systems. As MSW is heterogeneous material, direct heating value measurements are often not feasible. In this study an empirical model was developed to describe the heating value of the combustible fraction of municipal solid waste as a function of its physical composition of MSW using backpropagation neural network. Sampling process was carried out at Jatibarang landfill. The weight of each sorting sample taken from each discharged MSW vehicle load is 100 kg. The MSW physical components were grouped into paper wastes, absorbent hygiene product waste, styrofoam waste, HD plastic waste, plastic waste, rubber waste, textile waste, wood waste, yard wastes, kitchen waste, coco waste, and miscellaneous combustible waste. Network was trained by 24 datasets with 1200, 769, and 210 epochs. The results of this analysis showed that the correlation from the physical composition is better than multiple regression method .
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Wei Li
2018-01-01
Full Text Available Nutrient removal in tidal flow constructed wetlands (TF-CW is a complex series of nonlinear multi-parameter interactions. We simulated three tidal flow systems and a continuous vertical flow system filled with synthetic wastewater and compared the influent and effluent concentrations to examine (1 nutrient removal in artificial TF-CWs, and (2 the ability of a backpropagation (BP artificial neural network to predict nutrient removal. The nutrient removal rates were higher under tidal flow when the idle/reaction time was two, and reached 90 ± 3%, 99 ± 1%, and 58 ± 13% for total nitrogen (TN, ammonium nitrogen (NH4+-N, and total phosphorus (TP, respectively. The main influences on nutrient removal for each scenario were identified by redundancy analysis and were input into the model to train and verify the pollutant effluent concentrations. Comparison of the actual and model-predicted effluent concentrations showed that the model predictions were good. The predicted and actual values were correlated and the margin of error was small. The BP neural network fitted best to TP, with an R2 of 0.90. The R2 values of TN, NH4+-N, and nitrate nitrogen (NO3−-N were 0.67, 0.73, and 0.69, respectively.
The application of backpropagation neural network method to estimate the sediment loads
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Ari Gunawan Taufik
2017-01-01
Full Text Available Nearly all formulations of conventional sediment load estimation method were developed based on a review of laboratory data or data field. This approach is generally limited by local so it is only suitable for a particular river typology. From previous studies, the amount of sediment load tends to be non-linear with respect to the hydraulic parameters and parameter that accompanies sediment. The dominant parameter is turbulence, whereas turbulence flow velocity vector direction of x, y and z. They were affected by water bodies in 3D morphology of the cross section of the vertical and horizontal. This study is conducted to address the non-linear nature of the hydraulic parameter data and sediment parameter against sediment load data by applying the artificial neural network (ANN method. The method used is the backpropagation neural network (BPNN schema. This scheme used for projecting the sediment load from the hydraulic parameter data and sediment parameters that used in the conventional estimation of sediment load. The results showed that the BPNN model performs reasonably well on the conventional calculation, indicated by the stability of correlation coefficient (R and the mean square error (MSE.
Syahputra, M. F.; Amalia, C.; Rahmat, R. F.; Abdullah, D.; Napitupulu, D.; Setiawan, M. I.; Albra, W.; Nurdin; Andayani, U.
2018-03-01
Hypertension or high blood pressure can cause damage of blood vessels in the retina of eye called hypertensive retinopathy (HR). In the event Hypertension, it will cause swelling blood vessels and a decrese in retina performance. To detect HR in patients body, it is usually performed through physical examination of opthalmoscope which is still conducted manually by an ophthalmologist. Certainly, in such a manual manner, takes a ong time for a doctor to detetct HR on aa patient based on retina fundus iamge. To overcome ths problem, a method is needed to identify the image of retinal fundus automatically. In this research, backpropagation neural network was used as a method for retinal fundus identification. The steps performed prior to identification were pre-processing (green channel, contrast limited adapative histogram qualization (CLAHE), morphological close, background exclusion, thresholding and connected component analysis), feature extraction using zoning. The results show that the proposed method is able to identify retinal fundus with an accuracy of 95% with maximum epoch of 1500.
Li, Liu; Liqing, Huo; Hongru, Lu; Feng, Zhang; Chongxun, Zheng; Pokhrel, Shami; Jie, Zhang
2011-01-01
To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE) in newborns based on artificial neural networks and to determine its feasibility. Based on published research as well as preliminary studies in our laboratory, multiple noninvasive indicators with high sensitivity and specificity were selected for the early diagnosis of HIE and employed in the present study, which incorporates fuzzy logic with artificial neural networks. The analysis of the diagnostic results from the fuzzy neural network experiments with 140 cases of HIE showed a correct recognition rate of 100% in all training samples and a correct recognition rate of 95% in all the test samples, indicating a misdiagnosis rate of 5%. A preliminary model using fuzzy backpropagation neural networks based on a composite index of clinical indicators was established and its accuracy for the early diagnosis of HIE was validated. Therefore, this method provides a convenient tool for the early clinical diagnosis of HIE.
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Yi-jun Liu
2015-12-01
Full Text Available Childhood nephrotic syndrome is a chronic disease harmful to growth of children. Scientific and accurate prediction of negative conversion days for children with nephrotic syndrome offers potential benefits for treatment of patients and helps achieve better cure effect. In this study, the improved backpropagation neural network with momentum is used for prediction. Momentum speeds up convergence and maintains the generalization performance of the neural network, and therefore overcomes weaknesses of the standard backpropagation algorithm. The three-tier network structure is constructed. Eight indicators including age, lgG, lgA and lgM, etc. are selected for network inputs. The scientific computing software of MATLAB and its neural network tools are used to create model and predict. The training sample of twenty-eight cases is used to train the neural network. The test sample of six typical cases belonging to six different age groups respectively is used to test the predictive model. The low mean absolute error of predictive results is achieved at 0.83. The experimental results of the small-size sample show that the proposed approach is to some degree applicable for the prediction of negative conversion days of childhood nephrotic syndrome.
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Nugroho Nugroho
2012-01-01
Full Text Available The research on image identification has been conducted to identify the type of beef. The research is aimed to compare the performance of artificial neural network of backpropagation and general regression neural network model in identifying the type of meat. Image management is processed by counting R, G and B value in every meat image, and normalization process is then carried out by obtaining R, G, and B index value which is then converted from RGB model to HSI model to obtain the value of hue, saturation and intensity. The resulting value of image processing will be used as input parameter of training and validation programs. The performance of G RNN model is more accurate than the backpropagation with accuracy ratio by 51%.Keyword: Identification; Backpropagation; GRNN
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Bahman O. Taha
2015-06-01
Full Text Available The reinforced concrete with fiber reinforced polymer (FRP bars (carbon, aramid, basalt and glass is used in places where a high ratio of strength to weight is required and corrosion is not acceptable. Behavior of structural members using (FRP bars is hard to be modeled using traditional methods because of the high non-linearity relationship among factors influencing the strength of structural members. Back-propagation neural network is a very effective method for modeling such complicated relationships. In this paper, back-propagation neural network is used for modeling the flexural behavior of beams reinforced with (FRP bars. 101 samples of beams reinforced with fiber bars were collected from literatures. Five important factors are taken in consideration for predicting the strength of beams. Two models of Multilayer Perceptron (MLP are created, first with single-hidden layer and the second with two-hidden layers. The two-hidden layer model showed better accuracy ratio than the single-hidden layer model. Parametric study has been done for two-hidden layer model only. Equations are derived to be used instead of the model and the importance of input factors is determined. Results showed that the neural network is successful in modeling the behavior of concrete beams reinforced with different types of (FRP bars.
Xin, Meiting; Li, Bing; Yan, Xiao; Chen, Lei; Wei, Xiang
2018-02-01
A robust coarse-to-fine registration method based on the backpropagation (BP) neural network and shift window technology is proposed in this study. Specifically, there are three steps: coarse alignment between the model data and measured data, data simplification based on the BP neural network and point reservation in the contour region of point clouds, and fine registration with the reweighted iterative closest point algorithm. In the process of rough alignment, the initial rotation matrix and the translation vector between the two datasets are obtained. After performing subsequent simplification operations, the number of points can be reduced greatly. Therefore, the time and space complexity of the accurate registration can be significantly reduced. The experimental results show that the proposed method improves the computational efficiency without loss of accuracy.
International Nuclear Information System (INIS)
Sun, S.P.; Yi, D.Q.; Jiang, Y.; Wu, C.P.; Zang, B.; Li, Y.
2011-01-01
Research highlights: → An ANN was built to predict the formation enthalpies of Al 2 X-type intermetallics. → The values predicted by the ANN agree with experiments well to typically within 10%. → The method comparison suggests that our ANN method is superior to Miedema's model. → Some trends of formation enthalpies for Al 2 X-type intermetallics were observed. - Abstract: A back-propagation artificial neural network (ANN) was established to predict the formation enthalpies of Al 2 X-type intermetallics as a function of some physical parameters. These physical parameters include the electronegativity difference, the electron density difference, the atomic size difference, and the electron-atom ratio (e/a). The values calculated by the ANN method agree with experiments well to typically within 10%, indicating that the well-trained back-propagation (BP) neural network is feasible, and can precisely predict the formation enthalpies of Al 2 X-type intermetallics. The method comparison based on the predicted formation enthalpies suggests that our ANN method is superior to Miedema's model. Some trends of formation enthalpies for Al 2 X-type intermetallics were also observed from the ANN.
Vairaprakash Gurusamy *1 & K.Nandhini2
2017-01-01
A Neural Network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain.Back propagation was created by generalizing the Widrow-Hoff learning rule to multiple-layer networks and nonlinear differentiable transfer functions. The term back pro...
Li, Jian; Gu, Jun-zhong; Mao, Sheng-hua; Xiao, Wen-jia; Jin, Hui-ming; Zheng, Ya-xu; Wang, Yong-ming; Hu, Jia-yu
2013-12-01
To establish BP artificial neural network predicting model regarding the daily cases of infectious diarrhea in Shanghai. Data regarding both the incidence of infectious diarrhea from 2005 to 2008 in Shanghai and meteorological factors including temperature, relative humidity, rainfall, atmospheric pressure, duration of sunshine and wind speed within the same periods were collected and analyzed with the MatLab R2012b software. Meteorological factors that were correlated with infectious diarrhea were screened by Spearman correlation analysis. Principal component analysis (PCA) was used to remove the multi-colinearities between meteorological factors. Back-Propagation (BP) neural network was employed to establish related prediction models regarding the daily infectious diarrhea incidence, using artificial neural networks toolbox. The established models were evaluated through the fitting, predicting and forecasting processes. Data from Spearman correlation analysis indicated that the incidence of infectious diarrhea had a highly positive correlation with factors as daily maximum temperature, minimum temperature, average temperature, minimum relative humidity and average relative humidity in the previous two days (P neural network model were established under the input of 4 meteorological principal components, extracted by PCA and used for training and prediction. Then appeared to be 4.7811, 6.8921,0.7918,0.8418 and 5.8163, 7.8062,0.7202,0.8180, respectively. The rate on mean error regarding the predictive value to actual incidence in 2008 was 5.30% and the forecasting precision reached 95.63% . Temperature and air pressure showed important impact on the incidence of infectious diarrhea. The BP neural network model had the advantages of low simulation forecasting errors and high forecasting hit rate that could ideally predict and forecast the effects on the incidence of infectious diarrhea.
Chang, C L; Liu, H C
2015-09-01
The trophic state index, and in particular, the Carlson Trophic State Index (CTSI), is critical for evaluating reservoir water quality. Despite its common use in evaluating static water quality, the reliability of the CTSI may decrease when water turbidity is high. Therefore, this study examines the reliability of the CTSI and uses the Back-Propagation Neural Network (BPNN) model to create a new trophic state index. Fuzzy theory, rather than binary logic, is implemented to classify the trophic status into its three grades. The results show that compared to the CTSI with traditional classification, the new index with fuzzy classification can improve trophic status evaluation with high water turbidity. A reliable trophic state index can correctly describe reservoir water quality and allow relevant agencies to address proper water quality management strategies for a reservoir system.
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Shojaee Safar Ali
2014-01-01
Full Text Available In this study, feasibility of a back-propagated artificial neural network to correlate the binary density of ionic liquids (ILs mixtures containing water as the common solvent has been investigated. To verify the optimized parameters of the neural network, total of 1668 data were collected and divided into two different subsets. The first subsets consisted of more than two-third (1251 data points of data bank was used to find the optimum parameters including weights and biases, number of neurons (7 neurons, transfer functions in hidden and output layer which were tansig and purelin, respectively. In addition, the correlative capability of network was examined using testing subset (417 data points not considered during the training stage. The overall obtained results revealed that the proposed network is accurate enough to correlate the binary density of the ionic liquids mixtures with average absolute relative deviation (AARD % and average relative deviation (ARD % of 1.56% and -0.04 %, respectively. Finally, the correlative capability of the proposed ANN model was compared with one of the available correlations proposed by Rodríguez and Brennecke.
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Shi Liang Zhang
2015-01-01
Full Text Available This paper proposes a model to extract feature information quickly and accurately identifying what cannot be achieved through traditional methods of remote sensing image classification. First, process the selected Landsat-8 remote sensing data, including radiometric calibration, geometric correction, optimal band combination, and image cropping. Add the processed remote sensing image to the normalized geographic auxiliary information, digital elevation model (DEM, and normalized difference vegetation index (NDVI, working together to build a neural network that consists of three levels based on the structure of back-propagation neural and extended delta bar delta (BPN-EDBD algorithm, determining the parameters of the neural network to constitute a good classification model. Then determine classification and standards via field surveys and related geographic information; select training samples BPN-EDBD for algorithm learning and training and, if necessary, revise and improve its parameters using the BPN-EDBD classification algorithm to classify the remote sensing image after pretreatment and DEM data and NDVI as input parameters and output classification results, and run accuracy assessment. Finally, compare with traditional supervised classification algorithms, while adding different auxiliary geographic information to compare classification results to study the advantages and disadvantages of BPN-EDBD classification algorithm.
New backpropagation algorithm with type-2 fuzzy weights for neural networks
Gaxiola, Fernando; Valdez, Fevrier
2016-01-01
In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights. The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ô=17) and Dow-Jones time series, and recognition of person with iris bi...
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Lei Si
2014-01-01
Full Text Available Classification is an important theme in data mining. Rough sets and neural networks are the most common techniques applied in data mining problems. In order to extract useful knowledge and classify ambiguous patterns effectively, this paper presented a hybrid algorithm based on the integration of rough sets and BP neural network to construct a novel classification system. The attribution values were discretized through PSO algorithm firstly to establish a decision table. The attribution reduction algorithm and rules extraction method based on rough sets were proposed, and the flowchart of proposed approach was designed. Finally, a prototype system was developed and some simulation examples were carried out. Simulation results indicated that the proposed approach was feasible and accurate and was outperforming others.
Chen, Yan; Cai, Kezhou; Tu, Zehui; Nie, Wen; Ji, Tuo; Hu, Bing; Chen, Conggui; Jiang, Shaotong
2017-11-29
Benzo[a]pyrene (BaP), a potent mutagen and carcinogen, is reported to be present in processed meat products and, in particular, in smoked meat. However, few methods exist for predictive determination of the BaP content of smoked meats such as sausage. In this study, an artificial neural network (ANN) model based on the back-propagation (BP) algorithm was used to predict the BaP content of smoked sausage. The results showed that the BP network based on the Levenberg-Marquardt algorithm was the best suited for creating a nonlinear map between the input and output parameters. The optimal network structure was 3-7-1 and the learning rate was 0.6. This BP-ANN model allowed for accurate predictions, with the correlation coefficients (R) for the experimentally determined training, validation, test and global data sets being 0.94, 0.96, 0.95 and 0.95 respectively. The validation performance was 0.013, suggesting that the proposed BP-ANN may be used to predictively detect the BaP content of smoked meat products. An effective predictive model was constructed for estimation of the BaP content of smoked sausage using ANN modeling techniques, which shows potential to predict the BaP content in smoked sausage. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.
Forecasting Urban Air Quality via a Back-Propagation Neural Network and a Selection Sample Rule
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Yonghong Liu
2015-07-01
Full Text Available In this paper, based on a sample selection rule and a Back Propagation (BP neural network, a new model of forecasting daily SO2, NO2, and PM10 concentration in seven sites of Guangzhou was developed using data from January 2006 to April 2012. A meteorological similarity principle was applied in the development of the sample selection rule. The key meteorological factors influencing SO2, NO2, and PM10 daily concentrations as well as weight matrices and threshold matrices were determined. A basic model was then developed based on the improved BP neural network. Improving the basic model, identification of the factor variation consistency was added in the rule, and seven sets of sensitivity experiments in one of the seven sites were conducted to obtain the selected model. A comparison of the basic model from May 2011 to April 2012 in one site showed that the selected model for PM10 displayed better forecasting performance, with Mean Absolute Percentage Error (MAPE values decreasing by 4% and R2 values increasing from 0.53 to 0.68. Evaluations conducted at the six other sites revealed a similar performance. On the whole, the analysis showed that the models presented here could provide local authorities with reliable and precise predictions and alarms about air quality if used at an operational scale.
Computer vision system for egg volume prediction using backpropagation neural network
Siswantoro, J.; Hilman, M. Y.; Widiasri, M.
2017-11-01
Volume is one of considered aspects in egg sorting process. A rapid and accurate volume measurement method is needed to develop an egg sorting system. Computer vision system (CVS) provides a promising solution for volume measurement problem. Artificial neural network (ANN) has been used to predict the volume of egg in several CVSs. However, volume prediction from ANN could have less accuracy due to inappropriate input features or inappropriate ANN structure. This paper proposes a CVS for predicting the volume of egg using ANN. The CVS acquired an image of egg from top view and then processed the image to extract its 1D and 2 D size features. The features were used as input for ANN in predicting the volume of egg. The experiment results show that the proposed CSV can predict the volume of egg with a good accuracy and less computation time.
Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong
2013-01-01
Background The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Conclusion/Significance Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control. PMID:23556015
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Nader Salari
Full Text Available Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that
Salari, Nader; Shohaimi, Shamarina; Najafi, Farid; Nallappan, Meenakshii; Karishnarajah, Isthrinayagy
2014-01-01
Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the
Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong
2013-01-01
The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control.
Abdollahi, Yadollah; Sairi, Nor Asrina; Said, Suhana Binti Mohd; Abouzari-lotf, Ebrahim; Zakaria, Azmi; Sabri, Mohd Faizul Bin Mohd; Islam, Aminul; Alias, Yatimah
2015-11-05
It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend's components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R-squared (R(2)) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R(2) was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303-323 K, x[gua], 0-0.033, x[MDAE], 0.3-0.4, and x[H2O], 0.7-1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x[gua]>temperature>x[MDEA]>x[H2O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO2 capture in separation technologies that is able to industries scale up. Copyright © 2015 Elsevier B.V. All rights reserved.
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.
=UTF-8 54 / JOURNAL OF WATERWAY, PORT, COASTAL, AND OCEAN ENGINEERING / JANUARY/FEBRUARY 2001 BACK-PROPAGATION NEURAL NETWORK IN TIDAL-LEVEL FORECASTING a Discussion by Arun Kumar 3 and Vijay K. Minocha 4 The authors have presented... and the output is not required to be defined a priori. The ANN model, due to its flexible structure, if not applied with greater caution, may overfit at the training stage and may yield poorer results during the prediction stage. The efforts of the authors...
Sheridan, C.; O'Farrell, M.; Lewis, E.; Lyons, W. B.; Flanagan, C.; Jackman, N.
2006-02-01
This paper reports on two methods of classifying the spectral data from an optical fibre based sensor system as used in the food industry. The first method uses a feed-forward back-propagation artificial neural network while the second method involves using Kohonen self-organizing maps. The sensor monitors the food colour online as the food cooks by examining the reflected light from both the surface and the core of the product. The combination of using principal component analysis and back-propagation neural networks has been successfully investigated previously. In this paper, results obtained using this method are compared with results obtained using a self-organizing map trained on the principal components. The principal components used to train both classifiers are ordered in a 'colourscale'—a scale developed to allow several products of similar colour to be tested using a single network that had been trained using the colourscale. The results presented show that both classifiers perform well, and that any differences that arise occur at the boundaries of the classes.
Bahadir, Elif
2016-01-01
The purpose of this study is to examine a neural network based approach to predict achievement in graduate education for Elementary Mathematics prospective teachers. With the help of this study, it can be possible to make an effective prediction regarding the students' achievement in graduate education with Artificial Neural Networks (ANN). Two…
Yu, Hao; Rossi, Giammarco; Braglia, Andrea; Perrone, Guido
2016-08-10
The paper presents the development of a tool based on a back-propagation artificial neural network to assist in the accurate positioning of the lenses used to collimate the beam from semiconductor laser diodes along the so-called fast axis. After training using a Gaussian beam ray-equivalent model, the network is capable of indicating the tilt, decenter, and defocus of such lenses from the measured field distribution, so the operator can determine the errors with respect to the actual lens position and optimize the diode assembly procedure. An experimental validation using a typical configuration exploited in multi-emitter diode module assembly and fast axis collimating lenses with different focal lengths and numerical apertures is reported.
Vrettaros, John; Vouros, George; Drigas, Athanasios S.
This article studies the expediency of using neural networks technology and the development of back-propagation networks (BPN) models for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the developed neural models is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the percentage measurement of the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process.
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Harjoko Agus
2018-01-01
Full Text Available Acute Myeloid Leukemia (AML is a type of cancer which attacks white blood cells from myeloid. AML has eight subtypes, namely: M0, M1, M2, M3, M4, M5, M6, and M7. AML subtypes M1, M2 and M3 are affected by the same type of cells, myeloblast, making it needs more detailed analysis to distinguish. To overcome these obstacles, this research is applying digital image processing with Active Contour Without Edge (ACWE and Momentum Backpropagation artificial neural network for AML subtypes M1, M2 and M3 classification based on the type of the cell. Six features required as training parameters from every cell obtained by using feature extraction. The features are: cell area, perimeter, circularity, nucleus ratio, mean and standard deviation. The results show that ACWE can be used for segmenting white blood cells with 83.789% success percentage of 876 total cell objects. The whole AML slides had been identified according to the cell types predicted number through training with momentum backpropagation. Five times testing calibration with the best parameter generated averages value of 84.754% precision, 75.887% sensitivity, 95.090% specificity and 93.569% accuracy.
International Nuclear Information System (INIS)
Cen Haiyan; Bao Yidan; He Yong
2006-01-01
Visible and near-infrared reflectance (visible-NIR) spectroscopy is applied to discriminate different varieties of bayberry juices. The discrimination of visible-NIR spectra from samples is a matter of pattern recognition. By partial least squares (PLS), the spectrum is reduced to certain factors, which are then taken as the input of the backpropagation neural network (BPNN). Through training and prediction, three different varieties of bayberry juice are classified based on the output of the BPNN. In addition, a mathematical model is built and the algorithm is optimized. With proper parameters in the training set,100% accuracy is obtained by the BPNN. Thus it is concluded that the PLS analysis combined with the BPNN is an alternative for pattern recognition based on visible and NIR spectroscopy
International Nuclear Information System (INIS)
Doh, Jaeh Yeok; Lee, Jong Soo; Lee, Seung Uk
2016-01-01
In this study, a Back-propagation neural network (BPN) is employed to conduct an approximation of a true stress-strain curve using the load-displacement experimental data of DP590, a high-strength material used in automobile bodies and chassis. The optimized interconnection weights are obtained with hidden layers and output layers of the BPN through intelligent learning and training of the experimental data; by using these weights, a mathematical model of the material's behavior is suggested through this feed-forward neural network. Generally, the material properties from the tensile test cannot be acquired until the fracture regions, since it is difficult to measure the cross-section area of a specimen after diffusion necking. For this reason, the plastic properties of the true stress-strain are extrapolated using the weighted-average method after diffusion necking. The accuracies of BPN-based meta-models for predicting material properties are validated in terms of the Root mean square error (RMSE). By applying the approximate material properties, the reliable finite element solution can be obtained to realize the different shapes of the finite element models. Furthermore, the sensitivity analysis of the approximate meta-model is performed using the first-order approximate derivatives of the BPN and is compared with the results of the finite difference method. In addition, we predict the tension velocity's effect on the material property through a first-order sensitivity analysis.
Energy Technology Data Exchange (ETDEWEB)
Doh, Jaeh Yeok; Lee, Jong Soo [Yonsei University, Seoul (Korea, Republic of); Lee, Seung Uk [Gyeongbuk Hybrid Technology Institute, Yeongcheon (Korea, Republic of)
2016-03-15
In this study, a Back-propagation neural network (BPN) is employed to conduct an approximation of a true stress-strain curve using the load-displacement experimental data of DP590, a high-strength material used in automobile bodies and chassis. The optimized interconnection weights are obtained with hidden layers and output layers of the BPN through intelligent learning and training of the experimental data; by using these weights, a mathematical model of the material's behavior is suggested through this feed-forward neural network. Generally, the material properties from the tensile test cannot be acquired until the fracture regions, since it is difficult to measure the cross-section area of a specimen after diffusion necking. For this reason, the plastic properties of the true stress-strain are extrapolated using the weighted-average method after diffusion necking. The accuracies of BPN-based meta-models for predicting material properties are validated in terms of the Root mean square error (RMSE). By applying the approximate material properties, the reliable finite element solution can be obtained to realize the different shapes of the finite element models. Furthermore, the sensitivity analysis of the approximate meta-model is performed using the first-order approximate derivatives of the BPN and is compared with the results of the finite difference method. In addition, we predict the tension velocity's effect on the material property through a first-order sensitivity analysis.
3-D inversion of borehole-to-surface electrical data using a back-propagation neural network
Ho, Trong Long
2009-08-01
The "fluid-flow tomography", an advanced technique for geoelectrical survey based on the conventional mise-à-la-masse measurement, has been developed by Exploration Geophysics Laboratory at the Kyushu University. This technique is proposed to monitor fluid-flow behavior during water injection and production in a geothermal field. However data processing of this technique is very costly. In this light, this paper will discuss the solution to cost reduction by applying a neural network in the data processing. A case study in the Takigami geothermal field in Japan will be used to illustrate this. The achieved neural network in this case study is three-layered and feed-forward. The most successful learning algorithm in this network is the Resilient Propagation (RPROP). Consequently, the study advances the pragmatism of the "fluid-flow tomography" technique which can be widely used for geothermal fields. Accuracy of the solution is then verified by using root mean square (RMS) misfit error as an indicator.
Fauziah; Wibowo, E. P.; Madenda, S.; Hustinawati
2018-03-01
Capturing and recording motion in human is mostly done with the aim for sports, health, animation films, criminality, and robotic applications. In this study combined background subtraction and back propagation neural network. This purpose to produce, find similarity movement. The acquisition process using 8 MP resolution camera MP4 format, duration 48 seconds, 30frame/rate. video extracted produced 1444 pieces and results hand motion identification process. Phase of image processing performed is segmentation process, feature extraction, identification. Segmentation using bakground subtraction, extracted feature basically used to distinguish between one object to another object. Feature extraction performed by using motion based morfology analysis based on 7 invariant moment producing four different classes motion: no object, hand down, hand-to-side and hands-up. Identification process used to recognize of hand movement using seven inputs. Testing and training with a variety of parameters tested, it appears that architecture provides the highest accuracy in one hundred hidden neural network. The architecture is used propagate the input value of the system implementation process into the user interface. The result of the identification of the type of the human movement has been clone to produce the highest acuracy of 98.5447%. The training process is done to get the best results.
Deconvolution using a neural network
Energy Technology Data Exchange (ETDEWEB)
Lehman, S.K.
1990-11-15
Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.
Directory of Open Access Journals (Sweden)
Zhilong Wang
2014-01-01
Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.
Yu, Jiong; Pan, Qiaoling; Yang, Jinfeng; Zhu, Chengxing; Jin, Linfeng; Hao, Guangshu; Shi, Xiaowei; Cao, Hongcui; Lin, Feiyan
2017-06-19
BACKGROUND The complete blood count (CBC) is the most common examination used to monitor overall health in clinical practice. Whether there is a relationship between CBC indexes and alanine transaminase (ALT) and aspartate aminotransferase (AST) has been unclear. MATERIAL AND METHODS In this study, 572 normal-weight and 346 overweight Chinese subjects were recruited. The relationship between CBC indexes with ALT and AST were analyzed by Pearson and Spearman correlations according to their sex, then we conducted colinearity diagnostics and multiple linear regression (MLR) analysis. A prediction model was developed by a back-propagation artificial neural network (BP-ANN). RESULTS ALT was related to 4 CBC indexes in the male normal-weight group and 3 CBC indexes in the female group. In the overweight group, ALT had a similar relationship with the normal group, but there was only 1 index related with AST in the normal-weight group and male overweight groups. The ALT regression models were developed in normal-weight and overweight people, which had better correlation coefficient (R>0.3). After training 1000 epochs, the BP-ANN models of ALT achieved higher correlations than MLR models in normal-weight and overweight people. CONCLUSIONS ALT is a more suitable index than AST for developing a regression model. ALT can be predicted by CBC indexes in normal-weight and overweight individuals based on a BP-ANN model, which was better than MLR analysis.
Directory of Open Access Journals (Sweden)
Gang Yang
2017-09-01
Full Text Available The solubility data of compounds in supercritical fluids and the correlation between the experimental solubility data and predicted solubility data are crucial to the development of supercritical technologies. In the present work, the solubility data of silymarin (SM in both pure supercritical carbon dioxide (SCCO2 and SCCO2 with added cosolvent was measured at temperatures ranging from 308 to 338 K and pressures from 8 to 22 MPa. The experimental data were fit with three semi-empirical density-based models (Chrastil, Bartle and Mendez-Santiago and Teja models and a back-propagation artificial neural networks (BPANN model. Interaction parameters for the models were obtained and the percentage of average absolute relative deviation (AARD% in each calculation was determined. The correlation results were in good agreement with the experimental data. A comparison among the four models revealed that the experimental solubility data were more fit with the BPANN model with AARDs ranging from 1.14% to 2.15% for silymarin in pure SCCO2 and with added cosolvent. The results provide fundamental data for designing the extraction of SM or the preparation of its particle using SCCO2 techniques.
Program Helps Simulate Neural Networks
Villarreal, James; Mcintire, Gary
1993-01-01
Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.
Chin Kim On; Teo Kein Yau; Rayner Alfred; Jason Teo; Patricia Anthony; Wang Cheng
2016-01-01
In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car pla...
Song, Xianzhi; Peng, Chi; Li, Gensheng; He, Zhenguo; Wang, Haizhu
2016-01-01
Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells.
Directory of Open Access Journals (Sweden)
Orlando Lastres Danguillecourt
2012-03-01
Full Text Available Este trabajo presenta los resultados preliminares de la configuración de una red neuronal artificial (ANN, de tipo alimentación hacia adelante con el método de entrenamiento de retro-propagación para pronosticar la velocidad de viento en la región del Istmo de Tehuantepec, Oaxaca, México. La base de datos utilizada abarca los años comprendidos entre Junio 2008- Noviembre 2011, y fue suministrada por una estación meteorológica ubicada en la Universidad del Istmo campus Tehuantepec. Los experimentos se realizaron utilizando las siguientes variables: velocidad del viento, presión, temperatura y fecha. Al mismo tiempo se hicieron siete pruebas combinando estas variables, comparando su error cuadrático medio (MSE y el coeficiente de correlación r, con los datos de predicción y experimentales. En esta investigación, se propone una ANN de dos capas ocultas, para un pronóstico de 48 horas.This paper presents the preliminary results of setting up an artificial neural network (ANN of the feed forward type with the backpropagation training method for forecast wind speed in the region in the Isthmus of Tehuantepec, Oaxaca, Mexico. The database used covers the years from June 2008 - November 2011, and was supplied by a meteorological station located at the Isthmus University campus Tehuantepec. The experiments were done using the following variables: wind speed, pressure, temperature and date. At the same time were done seven tests combining these variables, comparing their mean square error (MSE and coefficient correlation r, with data the predicting and experimental. In this research, is proposed a ANN of two hidden layers, for a forecast of 48 hours.
Padgett, Mary L.; Desai, Utpal; Roppel, T.A.; White, Charles R.
1993-01-01
A design procedure is suggested for neural networks which accommodates the inclusion of such knowledge-based systems techniques as fuzzy logic and pairwise comparisons. The use of these procedures in the design of applications combines qualitative and quantitative factors with empirical data to yield a model with justifiable design and parameter selection procedures. The procedure is especially relevant to areas of back-propagation neural network design which are highly responsive to the use of precisely recorded expert knowledge.
Multigradient for Neural Networks for Equalizers
Directory of Open Access Journals (Sweden)
Chulhee Lee
2003-06-01
Full Text Available Recently, a new training algorithm, multigradient, has been published for neural networks and it is reported that the multigradient outperforms the backpropagation when neural networks are used as a classifier. When neural networks are used as an equalizer in communications, they can be viewed as a classifier. In this paper, we apply the multigradient algorithm to train the neural networks that are used as equalizers. Experiments show that the neural networks trained using the multigradient noticeably outperforms the neural networks trained by the backpropagation.
Dynamic training algorithm for dynamic neural networks
International Nuclear Information System (INIS)
Tan, Y.; Van Cauwenberghe, A.; Liu, Z.
1996-01-01
The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper
Parameter estimation of an aeroelastic aircraft using neural networks
Indian Academy of Sciences (India)
The neurons within a network are arranged in an input layer, one or more hidden or processing layers, and an output layer. Figure 1b shows a typical backpropagation neural network. The name `backpropagation' comes from the training method employed during the learning (training) process ± backpropagation of error.
Pelicano, Christian Mark; Rapadas, Nick; Cagatan, Gerard; Magdaluyo, Eduardo
2017-12-01
Herein, the crystallite size and band gap energy of zinc oxide (ZnO) quantum dots were predicted using artificial neural network (ANN). Three input factors including reagent ratio, growth time, and growth temperature were examined with respect to crystallite size and band gap energy as response factors. The generated results from neural network model were then compared with the experimental results. Experimental crystallite size and band gap energy of ZnO quantum dots were measured from TEM images and absorbance spectra, respectively. The Levenberg-Marquardt (LM) algorithm was used as the learning algorithm for the ANN model. The performance of the ANN model was then assessed through mean square error (MSE) and regression values. Based on the results, the ANN modelling results are in good agreement with the experimental data.
Ehret, Anita; Hochstuhl, David; Gianola, Daniel; Thaller, Georg
2015-03-31
Recently, artificial neural networks (ANN) have been proposed as promising machines for marker-based genomic predictions of complex traits in animal and plant breeding. ANN are universal approximators of complex functions, that can capture cryptic relationships between SNPs (single nucleotide polymorphisms) and phenotypic values without the need of explicitly defining a genetic model. This concept is attractive for high-dimensional and noisy data, especially when the genetic architecture of the trait is unknown. However, the properties of ANN for the prediction of future outcomes of genomic selection using real data are not well characterized and, due to high computational costs, using whole-genome marker sets is difficult. We examined different non-linear network architectures, as well as several genomic covariate structures as network inputs in order to assess their ability to predict milk traits in three dairy cattle data sets using large-scale SNP data. For training, a regularized back propagation algorithm was used. The average correlation between the observed and predicted phenotypes in a 20 times 5-fold cross-validation was used to assess predictive ability. A linear network model served as benchmark. Predictive abilities of different ANN models varied markedly, whereas differences between data sets were small. Dimension reduction methods enhanced prediction performance in all data sets, while at the same time computational cost decreased. For the Holstein-Friesian bull data set, an ANN with 10 neurons in the hidden layer achieved a predictive correlation of r=0.47 for milk yield when the entire marker matrix was used. Predictive ability increased when the genomic relationship matrix (r=0.64) was used as input and was best (r=0.67) when principal component scores of the marker genotypes were used. Similar results were found for the other traits in all data sets. Artificial neural networks are powerful machines for non-linear genome-enabled predictions in
Wave transmission prediction of multilayer floating breakwater using neural network
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Patil, S.G.; Hegde, A.V.
. Among many neural network architectures, the three layers feed forward error backpropagation neural network (BNN) is the most commonly used representing the input nodes as first layer, hidden nodes as second layer and output nodes as third layer...
Isolated Speech Recognition Using Artificial Neural Networks
National Research Council Canada - National Science Library
Polur, Prasad
2001-01-01
.... A small size vocabulary containing the words YES and NO is chosen. Spectral features using cepstral analysis are extracted per frame and imported to a feedforward neural network which uses a backpropagation with momentum training algorithm...
Energy Technology Data Exchange (ETDEWEB)
Toda-Caraballo, I.; Garcia-Mateo, C.; Capdevila, C.
2010-07-01
At the beginning of the decade of the nineties, the industrial interest for TRIP steels leads to a significant increase of the investigation and application in this field. In this work, the flexibility of neural networks for the modelling of complex properties is used to tackle the problem of determining the retained austenite content in TRIP-steel. Applying a combination of two learning algorithms (backpropagation and creeping-random-search) for the neural network, a model has been created that enables the prediction of retained austenite in low-Si / low-Al multiphase steels as a function of processing parameters. (Author). 34 refs.
Carrasco, Manuel; Garde, Andres; Murillo, Pilar; Serrano, Luis
2005-06-01
In this paper a novel design and implementation of a VLSI Analogue Neural Net based on Multi-Layer Perceptron (MLP) with on-chip Back Propagation (BP) learning algorithm suitable for the resolution of classification problems is described. In order to implement a general and programmable analogue architecture, the design has been carried out in a hierarchical way. In this way the net has been divided in synapsis-blocks and neuron-blocks providing an easy method for the analysis. These blocks basically consist on simple cells, which are mainly, the activation functions (NAF), derivatives (DNAF), multipliers and weight update circuits. The analogue design is based on current-mode translinear techniques using MOS transistors working in the weak inversion region in order to reduce both the voltage supply and the power consumption. Moreover, with the purpose of minimizing the noise, offset and distortion of even order, the topologies are fully-differential and balanced. The circuit, named ANNE (Analogue Neural NEt), has been prototyped and characterized as a proof of concept on CMOS AMI-0.5A technology occupying a total area of 2.7mm2. The chip includes two versions of neural nets with on-chip BP learning algorithm, which are respectively a 2-1 and a 2-2-1 implementations. The proposed nets have been experimentally tested using supply voltages from 2.5V to 1.8V, which is suitable for single cell lithium-ion battery supply applications. Experimental results of both implementations included in ANNE exhibit a good performance on solving classification problems. These results have been compared with other proposed Analogue VLSI implementations of Neural Nets published in the literature demonstrating that our proposal is very efficient in terms of occupied area and power consumption.
Introduction to neural networks
International Nuclear Information System (INIS)
Pavlopoulos, P.
1996-01-01
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
Nonlinear Compensation with Modified Adaptive Digital Backpropagation in Flexigrid Networks
DEFF Research Database (Denmark)
Porto da Silva, Edson; Asif, Rameez; Larsen, Knud J.
2015-01-01
We present a modified version of adaptive digital backpropagation based on EVM metric, and numerically access its performance in a flexigrid WDM scenario.......We present a modified version of adaptive digital backpropagation based on EVM metric, and numerically access its performance in a flexigrid WDM scenario....
Artificial Neural Networks for Beginners
Gershenson, Carlos
2003-01-01
The scope of this teaching package is to make a brief induction to Artificial Neural Networks (ANNs) for people who have no previous knowledge of them. We first make a brief introduction to models of networks, for then describing in general terms ANNs. As an application, we explain the backpropagation algorithm, since it is widely used and many other algorithms are derived from it. The user should know algebra and the handling of functions and vectors. Differential calculus is recommendable, ...
Predictive accuracy of backpropagation neural network ...
Indian Academy of Sciences (India)
river basin, Chile; Global Planet. Change 47 212–220. Mohan S 1991 Intercomparison of evapotranspiration esti- mates; Hydrol. Sci. J. 36(5) 447–460. Nandagiri L and Kovoor G 2006 Performance evaluation of reference evapotranspiration equations across a range of. Indian climates; J. Irrig. Drain. Eng. 132(3) 238–249.
Predictive accuracy of backpropagation neural network ...
Indian Academy of Sciences (India)
incorporated into the BP model for high accuracy management purpose of irrigation water, which relies on accurate values of ET ref. 1. Introduction. A water balance computer simulation model appli- cation for irrigation planning in Burkina Faso required a reliable estimation of evapotranspira- tion to improve the water use ...
Predictive accuracy of backpropagation neural network ...
Indian Academy of Sciences (India)
, Burkina Faso, Africa. African Policy Center, United Nations Economic Commission for Africa (UNECA), Addis-Ababa, Ethiopia. Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung, Taiwan, ...
International Nuclear Information System (INIS)
Denby, Bruce; Lindsey, Clark; Lyons, Louis
1992-01-01
The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive
Neural Network Training on Human Body Core Temperature Data
National Research Council Canada - National Science Library
Sanders, Peter
1999-01-01
A multi-layer Adaptive Linear Element neural network computer program was trained with back-propagation on physiological response data from nine subjects walking on a treadmill in two simulated tropical environments...
International Nuclear Information System (INIS)
Smith, Patrick I.
2003-01-01
Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing
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...
Artificial Neural Networks to Detect Risk of Type 2 Diabetes | Baha ...
African Journals Online (AJOL)
A multilayer feedforward architecture with backpropagation algorithm was designed using Neural Network Toolbox of Matlab. The network was trained using batch mode backpropagation with gradient descent and momentum. Best performed network identified during the training was 2 hidden layers of 6 and 3 neurons, ...
Neural network signature verification using Haar wavelet and Fourier transforms
McCormack, Daniel K. R.; Brown, B. M.; Pedersen, John F.
1993-08-01
This paper discusses the use of neural network's for handwritten signature verification using the Fourier and Haar wavelet transforms as methods of encoding signature images. Results will be presented that discuss a neural network's ability to generalize to unseen signatures using wavelet encoded training data. These results will be discussed with reference to both Backpropagation networks and Cascade-Correlation networks. Backpropagation and Cascade- Correlation networks are used to compare and contrast the generalization ability of Haar wavelet and Fourier transform encoded signature data.
Antenna analysis using neural networks
Smith, William T.
1992-01-01
Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern
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.
Training product unit neural networks with genetic algorithms
Janson, D. J.; Frenzel, J. F.; Thelen, D. C.
1991-01-01
The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.
Neural Networks Applied to Thermal Damage Classification in Grinding Process
Spadotto, Marcelo M.; Aguiar, Paulo Roberto de; Sousa, Carlos C. P.; Bianchi, Eduardo C.
2008-01-01
The utilization of neural network of type multi-layer perceptron using the back-propagation algorithm guaranteed very good results. Tests carried out in order to optimize the learning capacity of neural networks were of utmost importance in the training phase, where the optimum values for the number of neurons of the hidden layer, learning rate and momentum for each structure were determined. Once the architecture of the neural network was established with those optimum values, the mean squar...
Neural Networks Applied to Optimal Flight Control
McKelvey, Tomas
1992-01-01
This paper presents a method for developing control laws for nonlinear systems based on an optimal control formulation. Due to the nonlinearities of the system, no analytical solution exists. The method proposed here uses the 'black box' structure of a neural network to model a feedback control law. The network is trained with the back-propagation learning method by using examples of optimal control produced with a differential dynamic programming technique. Two different optimal control prob...
Simplified Learning Scheme For Analog Neural Network
Eberhardt, Silvio P.
1991-01-01
Synaptic connections adjusted one at a time in small increments. Simplified gradient-descent learning scheme for electronic neural-network processor less efficient than better-known back-propagation scheme, but offers two advantages: easily implemented in circuitry because data-access circuitry separated from learning circuitry; and independence of data-access circuitry makes possible to implement feedforward as well as feedback networks, including those of multiple-attractor type. Important in such applications as recognition of patterns.
PEAK TRACKING WITH A NEURAL NETWORK FOR SPECTRAL RECOGNITION
COENEGRACHT, PMJ; METTING, HJ; VANLOO, EM; SNOEIJER, GJ; DOORNBOS, DA
1993-01-01
A peak tracking method based on a simulated feed-forward neural network with back-propagation is presented. The network uses the normalized UV spectra and peak areas measured in one chromatogram for peak recognition. It suffices to train the network with only one set of spectra recorded in one
Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?
Baltacıoğlu, A. K.; Öztürk, B.; Civalek, Ö.; Akgöz, B.
2010-01-01
In the present study, an artificial neural network (ANN) application is introduced for estimation of damage level of reinforced concrete structures. Back-propagation learning algorithm is adopted. A typical neural network architecture is proposed and some conclusions are presented. Applicability of artificial neural network (ANN) for the assessment of earthquake related damage is investigated
Energy Technology Data Exchange (ETDEWEB)
Rosas Ortiz, German
2000-01-01
Fault detection and diagnosis on transmission systems is an interesting area of investigation to Artificial Intelligence (AI) based systems. Neurocomputing is one of fastest growing areas of research in the fields of AI and pattern recognition. This work explores the possible suitability of pattern recognition approach of neural networks for fault detection and classification on power systems. The conventional detection techniques in modern relays are based in digital processing of signals and it need some time (around 1 cycle) to send a tripping signal, also they are likely to make incorrect decisions if the signals are noisy. It's desirable to develop a fast, accurate and robust approach that perform accurately for changing system conditions (like load variations and fault resistance). The aim of this work is to develop a novel technique based on Artificial Neural Networks (ANN), which explores the suitability of a pattern classification approach for fault detection and diagnosis. The suggested approach is based in the fact that when a fault occurs, a change in the system impedance take place and, as a consequence changes in amplitude and phase of line voltage and current signals take place. The ANN-based fault discriminator is trained to detect this changes as indicators of the instant of fault inception. This detector uses instantaneous values of these signals to make decisions. Suitability of using neural network as pattern classifiers for transmission systems fault diagnosis is described in detail a neural network design and simulation environment for real-time is presented. Results showing the performance of this approach are presented and indicate that it is fast, secure and exact enough, and it can be used in high speed fault detection and classification schemes. [Spanish] El diagnostico y la deteccion de fallas en sistemas de transmision es una area de interes en investigacion para sistemas basados en Inteligencia Artificial (IA). El calculo neuronal
Tests of track segment and vertex finding with neural networks
Energy Technology Data Exchange (ETDEWEB)
Denby, B.; Lessner, E. (Fermi National Accelerator Lab., Batavia, IL (USA)); Lindsey, C.S. (Iowa State Univ. of Science and Technology, Ames, IA (USA))
1990-04-01
Feed forward neural networks have been trained, using back-propagation, to find the slopes of simulated track segments in a straw chamber and to find the vertex of tracks from both simulated and real events in a more conventional drift chamber geometry. Network architectures, training, and performance are presented. 12 refs., 7 figs.
Recognition of decays of charged tracks with neural network techniques
International Nuclear Information System (INIS)
Stimpfl-Abele, G.
1991-01-01
We developed neural-network learning techniques for the recognition of decays of charged tracks using a feed-forward network with error back-propagation. Two completely different methods are described in detail and their efficiencies for several NN architectures are compared with conventional methods. Excellent results are obtained. (orig.)
Introduction to neural networks
James, Frederick E
1994-02-02
1. Introduction and overview of Artificial Neural Networks. 2,3. The Feed-forward Network as an inverse Problem, and results on the computational complexity of network training. 4.Physics applications of neural networks.
Optimal control learning with artificial neural networks
International Nuclear Information System (INIS)
Martinez, J.M.; Parey, C.; Houkari, M.
1993-01-01
This paper shows neural networks capabilities in optimal control applications of non linear dynamic systems. Our method is issued of a classical method concerning the direct research of the optimal control using gradient techniques. We show that neural approach and backpropagation paradigm are able to solve efficiently equations relative to necessary conditions for an optimizing solution. We have taken into account the known capabilities of multi layered networks in approximation functions. And for dynamic systems, we have generalized the indirect learning of inverse model adaptive architecture that is capable to define an optimal control in relation to a temporal criterion. (orig.)
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
Neural networks for function approximation in nonlinear control
Linse, Dennis J.; Stengel, Robert F.
1990-01-01
Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.
Baruch, Ieroham; Mariaca-Gaspar, Carlos; Barrera-Cortes, Josefina
2008-01-01
The chapter proposes a new Kalman filter closed loop topology of recurrent neural network for identification and modeling of an unknown hydrocarbon degradation process carried out in a biopile system and a rotating drum. The proposed KF RNN contained a recurrent neural plant model, a recurrent neural output plant filter and posses global and local feedbacks. The learning algorithm is a modified version of the dynamic Backpropagation one derived using the adjoint KF RNN topology by means of th...
A comparison of neural network architectures for the prediction of MRR in EDM
Jena, A. R.; Das, Raja
2017-11-01
The aim of the research work is to predict the material removal rate of a work-piece in electrical discharge machining (EDM). Here, an effort has been made to predict the material removal rate through back-propagation neural network (BPN) and radial basis function neural network (RBFN) for a work-piece of AISI D2 steel. The input parameters for the architecture are discharge-current (Ip), pulse-duration (Ton), and duty-cycle (τ) taken for consideration to obtained the output for material removal rate of the work-piece. In the architecture, it has been observed that radial basis function neural network is comparatively faster than back-propagation neural network but logically back-propagation neural network results more real value. Therefore BPN may consider as a better process in this architecture for consistent prediction to save time and money for conducting experiments.
Reconstruction of periodic signals using neural networks
Directory of Open Access Journals (Sweden)
José Danilo Rairán Antolines
2014-01-01
Full Text Available In this paper, we reconstruct a periodic signal by using two neural networks. The first network is trained to approximate the period of a signal, and the second network estimates the corresponding coefficients of the signal's Fourier expansion. The reconstruction strategy consists in minimizing the mean-square error via backpro-pagation algorithms over a single neuron with a sine transfer function. Additionally, this paper presents mathematical proof about the quality of the approximation as well as a first modification of the algorithm, which requires less data to reach the same estimation; thus making the algorithm suitable for real-time implementations.
Computationally Efficient Neural Network Intrusion Security Awareness
Energy Technology Data Exchange (ETDEWEB)
Todd Vollmer; Milos Manic
2009-08-01
An enhanced version of an algorithm to provide anomaly based intrusion detection alerts for cyber security state awareness is detailed. A unique aspect is the training of an error back-propagation neural network with intrusion detection rule features to provide a recognition basis. Network packet details are subsequently provided to the trained network to produce a classification. This leverages rule knowledge sets to produce classifications for anomaly based systems. Several test cases executed on ICMP protocol revealed a 60% identification rate of true positives. This rate matched the previous work, but 70% less memory was used and the run time was reduced to less than 1 second from 37 seconds.
Conjugate descent formulation of backpropagation error in ...
African Journals Online (AJOL)
The feedforward neural network architecture uses backpropagation learning to determine optimal weights between dierent interconnected layers. This learning procedure uses a gradient descent technique applied to a sum-of-squares error function for the given input-output pattern. It employs an iterative procedure to ...
Back propagation and Monte Carlo algorithms for neural network computations
International Nuclear Information System (INIS)
Junczys, R.; Wit, R.
1996-01-01
Results of teaching procedures for neural network for two different algorithms are presented. The first one is based on the well known back-propagation technique, the second is an adopted version of the Monte Carlo global minimum seeking method. Combination of these two, different in nature, approaches provides promising results. (author) nature, approaches provides promising results. (author)
Energy Technology Data Exchange (ETDEWEB)
Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)
1996-12-31
The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.
Deep learning in neural networks: an overview.
Schmidhuber, Jürgen
2015-01-01
In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Membership generation using multilayer neural network
Kim, Jaeseok
1992-01-01
There has been intensive research in neural network applications to pattern recognition problems. Particularly, the back-propagation network has attracted many researchers because of its outstanding performance in pattern recognition applications. In this section, we describe a new method to generate membership functions from training data using a multilayer neural network. The basic idea behind the approach is as follows. The output values of a sigmoid activation function of a neuron bear remarkable resemblance to membership values. Therefore, we can regard the sigmoid activation values as the membership values in fuzzy set theory. Thus, in order to generate class membership values, we first train a suitable multilayer network using a training algorithm such as the back-propagation algorithm. After the training procedure converges, the resulting network can be treated as a membership generation network, where the inputs are feature values and the outputs are membership values in the different classes. This method allows fairly complex membership functions to be generated because the network is highly nonlinear in general. Also, it is to be noted that the membership functions are generated from a classification point of view. For pattern recognition applications, this is highly desirable, although the membership values may not be indicative of the degree of typicality of a feature value in a particular class.
artificial neural network (ann)
African Journals Online (AJOL)
2004-08-18
Aug 18, 2004 ... forecasting models and artificial intelligence techniques and have become one of the major research fields (Kher and Joshin, 2003). (a) Artificial Neural Network and Electrical Load. Prediction. Neural network analysis is an Artificial Intelligence. (AI) approach to mathematical modeling. Neural. Networks ...
Design of neural networks for classification of remotely sensed imagery
Chettri, Samir R.; Cromp, Robert F.; Birmingham, Mark
1992-01-01
Classification accuracies of a backpropagation neural network are discussed and compared with a maximum likelihood classifier (MLC) with multivariate normal class models. We have found that, because of its nonparametric nature, the neural network outperforms the MLC in this area. In addition, we discuss techniques for constructing optimal neural nets on parallel hardware like the MasPar MP-1 currently at GSFC. Other important discussions are centered around training and classification times of the two methods, and sensitivity to the training data. Finally, we discuss future work in the area of classification and neural nets.
File access prediction using neural networks.
Patra, Prashanta Kumar; Sahu, Muktikanta; Mohapatra, Subasish; Samantray, Ronak Kumar
2010-06-01
One of the most vexing issues in design of a high-speed computer is the wide gap of access times between the memory and the disk. To solve this problem, static file access predictors have been used. In this paper, we propose dynamic file access predictors using neural networks to significantly improve upon the accuracy, success-per-reference, and effective-success-rate-per-reference by using neural-network-based file access predictor with proper tuning. In particular, we verified that the incorrect prediction has been reduced from 53.11% to 43.63% for the proposed neural network prediction method with a standard configuration than the recent popularity (RP) method. With manual tuning for each trace, we are able to improve upon the misprediction rate and effective-success-rate-per-reference using a standard configuration. Simulations on distributed file system (DFS) traces reveal that exact fit radial basis function (RBF) gives better prediction in high end system whereas multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) backpropagation outperforms in system having good computational capability. Probabilistic and competitive predictors are the most suitable for work stations having limited resources to deal with and the former predictor is more efficient than the latter for servers having maximum system calls. Finally, we conclude that MLP with LM backpropagation algorithm has better success rate of file prediction than those of simple perceptron, last successor, stable successor, and best k out of m predictors.
Chaotic diagonal recurrent neural network
International Nuclear Information System (INIS)
Wang Xing-Yuan; Zhang Yi
2012-01-01
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)
Control of beam halo-chaos using neural network self-adaptation method
International Nuclear Information System (INIS)
Fang Jinqing; Huang Guoxian; Luo Xiaoshu
2004-11-01
Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)
Inversion of a lateral log using neural networks
International Nuclear Information System (INIS)
Garcia, G.; Whitman, W.W.
1992-01-01
In this paper a technique using neural networks is demonstrated for the inversion of a lateral log. The lateral log is simulated by a finite difference method which in turn is used as an input to a backpropagation neural network. An initial guess earth model is generated from the neural network, which is then input to a Marquardt inversion. The neural network reacts to gross and subtle data features in actual logs and produces a response inferred from the knowledge stored in the network during a training process. The neural network inversion of lateral logs is tested on synthetic and field data. Tests using field data resulted in a final earth model whose simulated lateral is in good agreement with the actual log data
Prototype-Incorporated Emotional Neural Network.
Oyedotun, Oyebade K; Khashman, Adnan
2017-08-15
Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.
Neural Network Back-Propagation Algorithm for Sensing Hypergols
Perotti, Jose; Lewis, Mark; Medelius, Pedro; Bastin, Gary
2013-01-01
Fast, continuous detection of a wide range of hazardous substances simultaneously is needed to achieve improved safety for personnel working with hypergolic fuels and oxidizers, as well as other hazardous substances, with a requirement for such detection systems to warn personnel immediately upon the sudden advent of hazardous conditions, with a high probability of detection and a low false alarm rate. The primary purpose of this software is to read the voltage outputs from voltage dividers containing carbon nano - tube sensors as a variable resistance leg, and to recognize quickly when a leak has occurred through recognizing that a generalized pattern change in resistivity of a carbon nanotube sensor has occurred upon exposure to dangerous substances, and, further, to identify quickly just what substance is present through detailed pattern recognition of the shape of the response provided by the carbon nanotube sensor.
Ocean wave parameters estimation using backpropagation neural networks
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; SubbaRao; Raju, D.H.
is trained the ocean wave parameters can be estimated for unknown measured spectra, whereas significant wave height and spectral peak period are required to first generate the Scott spectra and then estimate other ocean wave parameters....
Stability Analysis of Neural Networks-Based System Identification
Directory of Open Access Journals (Sweden)
Talel Korkobi
2008-01-01
Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.
Neural Networks For Electrohydrodynamic Effect Modelling
Directory of Open Access Journals (Sweden)
Wiesław Wajs
2004-01-01
Full Text Available This paper presents currently achieved results concerning methods of electrohydrodynamiceffect used in geophysics simulated with feedforward networks trained with backpropagation algorithm, radial basis function networks and generalized regression networks.
Artificial neural network based approach to transmission lines protection
International Nuclear Information System (INIS)
Joorabian, M.
1999-05-01
The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection
Analysis of Accuracy and Epoch on Back-propagation BFGS Quasi-Newton
Silaban, Herlan; Zarlis, Muhammad; Sawaluddin
2017-12-01
Back-propagation is one of the learning algorithms on artificial neural networks that have been widely used to solve various problems, such as pattern recognition, prediction and classification. The Back-propagation architecture will affect the outcome of learning processed. BFGS Quasi-Newton is one of the functions that can be used to change the weight of back-propagation. This research tested some back-propagation architectures using classical back-propagation and back-propagation with BFGS. There are 7 architectures that have been tested on glass dataset with various numbers of neurons, 6 architectures with 1 hidden layer and 1 architecture with 2 hidden layers. BP with BFGS improves the convergence of the learning process. The average improvement convergence is 98.34%. BP with BFGS is more optimal on architectures with smaller number of neurons with decreased epoch number is 94.37% with the increase of accuracy about 0.5%.
Neural Networks: Implementations and Applications
Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.
1996-01-01
Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas
Yorek, Nurettin; Ugulu, Ilker
2015-01-01
In this study, artificial neural networks are suggested as a model that can be "trained" to yield qualitative results out of a huge amount of categorical data. It can be said that this is a new approach applied in educational qualitative data analysis. In this direction, a cascade-forward back-propagation neural network (CFBPN) model was…
Representation of neutron noise data using neural networks
International Nuclear Information System (INIS)
Korsah, K.; Damiano, B.; Wood, R.T.
1992-01-01
This paper describes a neural network-based method of representing neutron noise spectra using a model developed at the Oak Ridge National Laboratory (ORNL). The backpropagation neural network learned to represent neutron noise data in terms of four descriptors, and the network response matched calculated values to within 3.5 percent. These preliminary results are encouraging, and further research is directed towards the application of neural networks in a diagnostics system for the identification of the causes of changes in structural spectral resonances. This work is part of our current investigation of advanced technologies such as expert systems and neural networks for neutron noise data reduction, analysis, and interpretation. The objective is to improve the state-of-the-art of noise analysis as a diagnostic tool for nuclear power plants and other mechanical systems
DEFF Research Database (Denmark)
Krogh, Anders Stærmose; Riis, Søren Kamaric
1999-01-01
A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...
Implementation of neural network for color properties of polycarbonates
Saeed, U.; Ahmad, S.; Alsadi, J.; Ross, D.; Rizvi, G.
2014-05-01
In present paper, the applicability of artificial neural networks (ANN) is investigated for color properties of plastics. The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. An optimal design is completed for 10, 12, 14,16,18 & 20 hidden neurons on single hidden layer with five different algorithms: batch gradient descent (GD), batch variable learning rate (GDX), resilient back-propagation (RP), scaled conjugate gradient (SCG), levenberg-marquardt (LM) in the feed forward back-propagation neural network model. The training data for ANN is obtained from experimental measurements. There were twenty two inputs including resins, additives & pigments while three tristimulus color values L*, a* and b* were used as output layer. Statistical analysis in terms of Root-Mean-Squared (RMS), absolute fraction of variance (R squared), as well as mean square error is used to investigate the performance of ANN. LM algorithm with fourteen neurons on hidden layer in Feed Forward Back-Propagation of ANN model has shown best result in the present study. The degree of accuracy of the ANN model in reduction of errors is proven acceptable in all statistical analysis and shown in results. However, it was concluded that ANN provides a feasible method in error reduction in specific color tristimulus values.
Vibration monitoring with artificial neural networks
International Nuclear Information System (INIS)
Alguindigue, I.
1991-01-01
Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural network to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected from operating machinery. Two neural networks algorithms were used in our project: the Recirculation algorithm for data compression and the Backpropagation algorithm to perform the actual classification of the patterns. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results to date are very encouraging
Applying neural networks as software sensors for enzyme engineering.
Linko, S; Zhu, Y H; Linko, P
1999-04-01
The on-line control of enzyme-production processes is difficult, owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables. For example, intelligent methods to predict the end point of fermentation could be of great economic value. Computer-assisted control based on artificial-neural-network models offers a novel solution in such situations. Well-trained feedforward-backpropagation neural networks can be used as software sensors in enzyme-process control; their performance can be affected by a number of factors.
Neural networks for aircraft control
Linse, Dennis
1990-01-01
Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.
DEFF Research Database (Denmark)
Hansen, Lars Kai; Salamon, Peter
1990-01-01
We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....
Sea ice classification using fast learning neural networks
Dawson, M. S.; Fung, A. K.; Manry, M. T.
1992-01-01
A first learning neural network approach to the classification of sea ice is presented. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) were tested on simulated data sets based on the known dominant scattering characteristics of the target class. Four classes were used in the data simulation: open water, thick lossy saline ice, thin saline ice, and multiyear ice. The BP network was unable to consistently converge to less than 25 percent error while the FL method yielded an average error of approximately 1 percent on the first iteration of training. The fast learning method presented can significantly reduce the CPU time necessary to train a neural network as well as consistently yield higher classification accuracy than BP networks.
Critical Branching Neural Networks
Kello, Christopher T.
2013-01-01
It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…
Tomography using neural networks
International Nuclear Information System (INIS)
Demeter, G.; Zoletnik, S.
1997-01-01
Neural networks have been used for fast measurement evaluation in plasma physics, including nonlinear curve fitting to experimental data. Such an approach for fast evaluation of tomographic measurements was utilized on the MT-1M tokamak, especially in the study of impurity injection using laser accelerated pellets and of the transport of these injected impurities. Neural networks were studied for fast processing of tomographic data and large numbers of tomographic data
International Nuclear Information System (INIS)
Cheon, Se Woo; Kim, Wan Joo; Chang, Soon Heung; Roh, Myung Sub
1991-01-01
The Back-propagation Neural Network (BPN) algorithm is applied to connectionist expert system for the identification of BWR transients. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are described. The general mapping capability of the neural networks enables to identify transients easily. A number of case studies were performed with emphasis on the applicability of the neural networks to the diagnostic domain. It is revealed that the BPN algorithm can identify transients properly, even when incomplete or untrained symptoms are given. It is also shown that multiple transients are easily identified
Recognition of Roasted Coffee Bean Levels using Image Processing and Neural Network
Nasution, T. H.; Andayani, U.
2017-03-01
The coffee beans roast levels have some characteristics. However, some people cannot recognize the coffee beans roast level. In this research, we propose to design a method to recognize the coffee beans roast level of images digital by processing the image and classifying with backpropagation neural network. The steps consist of how to collect the images data with image acquisition, pre-processing, feature extraction using Gray Level Co-occurrence Matrix (GLCM) method and finally normalization of data extraction using decimal scaling features. The values of decimal scaling features become an input of classifying in backpropagation neural network. We use the method of backpropagation to recognize the coffee beans roast levels. The results showed that the proposed method is able to identify the coffee roasts beans level with an accuracy of 97.5%.
Classification of merged AVHRR and SMMR Arctic data with neural networks
Key, J.; Maslanik, J. A.; Schweiger, A. J.
1989-01-01
A forward-feed back-propagation neural network is used to classify merged AVHRR and SMMR summer Arctic data. Four surface and eight cloud classes are identified. Partial memberships of each pixel to each class are examined for spectral ambiguities. Classification results are compared to manual interpretations and to those determined by a supervised maximum likelihood procedure. Results indicate that a neural network approach offers advantages in ease of use, interpretability, and utility for indistinct and time-variant spectral classes.
Non-Linear Back-propagation: Doing Back-Propagation withoutDerivatives of the Activation Function
DEFF Research Database (Denmark)
Hertz, John; Krogh, Anders Stærmose; Lautrup, Benny
1997-01-01
The conventional linear back-propagation algorithm is replaced by a non-linear version, which avoids the necessity for calculating the derivative of the activation function. This may be exploited in hardware realizations of neural processors. In this paper we derive the non-linear back-propagatio......-propagation algorithms in the framework of recurrent back-propagation and present some numerical simulations of feed-forward networks on the NetTalk problem. A discussion of implementation in analog VLSI electronics concludes the paper.......The conventional linear back-propagation algorithm is replaced by a non-linear version, which avoids the necessity for calculating the derivative of the activation function. This may be exploited in hardware realizations of neural processors. In this paper we derive the non-linear back...
Energy Technology Data Exchange (ETDEWEB)
Nose Filho, Kenji; Araujo, Klayton A.M.; Maeda, Jorge L.Y.; Lotufo, Anna Diva P. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil)], Emails: kenjinose@yahoo.com.br, klayton_ama@hotmail.com, jorge-maeda@hotmail.com, annadiva@dee.feis.unesp.br
2009-07-01
This paper presents a development and implementation of a program to electrical load forecasting with data from a Brazilian electrical company, using four different architectures of neural networks of the MATLAB toolboxes: multilayer backpropagation gradient descendent with momentum, multilayer backpropagation Levenberg-Marquardt, adaptive network based fuzzy inference system and general regression neural network. The program presented a satisfactory performance, guaranteeing very good results. (author)
Learning behavior and temporary minima of two-layer neural networks
Annema, Anne J.; Hoen, Klaas; Hoen, Klaas; Wallinga, Hans
1994-01-01
This paper presents a mathematical analysis of the occurrence of temporary minima during training of a single-output, two-layer neural network, with learning according to the back-propagation algorithm. A new vector decomposition method is introduced, which simplifies the mathematical analysis of
Artificial Neural Network Model for Predicting Compressive
Directory of Open Access Journals (Sweden)
Salim T. Yousif
2013-05-01
Full Text Available Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.
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.
Gain and exposure scheduling to compensate for photorefractive neural-network weight decay
Goldstein, Adam A.; Petrisor, Gregory C.; Jenkins, B. Keith
1995-03-01
A gain and exposure schedule that theoretically eliminates the effect of photorefractive weight decay for the general class of outer-product neural-network learning algorithms (e.g., backpropagation, Widrow-Hoff, perceptron) is presented. This schedule compensates for photorefractive diffraction-efficiency decay by iteratively increasing the spatial-light-modulator transfer function gain and decreasing the weight-update exposure time. Simulation results for the scheduling procedure, as applied to backpropagation learning for the exclusive-OR problem, show improved learning performance compared with results for networks trained without scheduling.
A neural network regulator for turbogenerators.
Wu, Q H; Hogg, B W; Irwin, G W
1992-01-01
A neural network (NN) based regulator for nonlinear, multivariable turbogenerator control is presented. A hierarchical architecture of an NN is proposed for regulator design, consisting of two subnetworks which are used for input-output (I-O) mapping and control, respectively, based on the back-propagation (BP) algorithm. The regulator has the flexibility for accepting more sensory information to cater to multi-input, multioutput systems. Its operation does not require a reference model or inverse system model and it can produce more acceptable control signals than are obtained by using sign of plant errors during training I-O mapping of turbogenerator systems using NNs has been investigated and the regulator has been implemented on a complex turbogenerator system model. Simulation results show satisfactory control performance and illustrate the potential of the NN regulator in comparison with an existing adaptive controller.
Backpropagation and ordered derivatives in the time scales calculus.
Seiffertt, John; Wunsch, Donald C
2010-08-01
Backpropagation is the most widely used neural network learning technique. It is based on the mathematical notion of an ordered derivative. In this paper, we present a formulation of ordered derivatives and the backpropagation training algorithm using the important emerging area of mathematics known as the time scales calculus. This calculus, with its potential for application to a wide variety of inter-disciplinary problems, is becoming a key area of mathematics. It is capable of unifying continuous and discrete analysis within one coherent theoretical framework. Using this calculus, we present here a generalization of backpropagation which is appropriate for cases beyond the specifically continuous or discrete. We develop a new multivariate chain rule of this calculus, define ordered derivatives on time scales, prove a key theorem about them, and derive the backpropagation weight update equations for a feedforward multilayer neural network architecture. By drawing together the time scales calculus and the area of neural network learning, we present the first connection of two major fields of research.
Vibration Based Damage Assessment of a Civil Engineering Structures using a Neural Networks
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Rytter, A.
In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with the Backpropagation Algorith as a non-destructive damage assessment technique to locate and quantify a damage in Civil Engineering structures is investigated. Since artificial neural networks are proving...... 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 undamaged structure. Subjecting this trained neural network to measured modal parameters should imply information...
Introduction to Artificial Neural Networks
DEFF Research Database (Denmark)
Larsen, Jan
1999-01-01
The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....
Application of neural networks to seismic active control
International Nuclear Information System (INIS)
Tang, Yu.
1995-01-01
An exploratory study on seismic active control using an artificial neural network (ANN) is presented in which a singledegree-of-freedom (SDF) structural system is controlled by a trained neural network. A feed-forward neural network and the backpropagation training method are used in the study. In backpropagation training, the learning rate is determined by ensuring the decrease of the error function at each training cycle. The training patterns for the neural net are generated randomly. Then, the trained ANN is used to compute the control force according to the control algorithm. The control strategy proposed herein is to apply the control force at every time step to destroy the build-up of the system response. The ground motions considered in the simulations are the N21E and N69W components of the Lake Hughes No. 12 record that occurred in the San Fernando Valley in California on February 9, 1971. Significant reduction of the structural response by one order of magnitude is observed. Also, it is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history. These promising results assert the potential of applying ANNs to active structural control under seismic loads
Artificial neural network modelling
Samarasinghe, Sandhya
2016-01-01
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .
Neural Networks and Micromechanics
Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.
The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.
1993-07-01
simpler linearly separable majority function (Ahmad, Tesauro , 1988), the former has limited applicability to realistic problems and the latter has been...anwered. 6. References Ahmad, S., G. Tesauro , "Scaling and Generalization in Neural Networks: A Case Study", Proceedings of the 1988 Connectionist
Synthesis of recurrent neural networks for dynamical system simulation.
Trischler, Adam P; D'Eleuterio, Gabriele M T
2016-08-01
We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.
Wavelet-cellular neural network architecture and learning algorithm
Bal, Abdullah; Ucan, Osman N.; Pastaci, Halit; Alam, Mohammad S.
2004-04-01
Cellular Neural Networks (CNN) provides fast parallel computational capability for image processing applications. The behavior of the CNN is defined by two template matrices. In this paper, adjustment of these template-matrix coefficients have been realized using supervised learning algorithm based on back-propagation technique and wavelet function. Back-propagation algorithm has been modified for dynamic behavior of CNN. Wavelet function is utilized to provide the activation function derivation in this learning algorithm. The supervised learning algorithm is then executed to obtain a compact CNN architecture, called as Wave-CNN. The proposed new learning algorithm and Wave-CNN architecture performance have been tested for 2D image processing applications.
A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
A.J.G. da Cruz
1997-12-01
Full Text Available The production of penicillin G by Penicillium chrysogenum IFO 8644 was simulated employing a feedforward neural network with three layers. The neural network training procedure used an algorithm combining two procedures: random search and backpropagation. The results of this approach were very promising, and it was observed that the neural network was able to accurately describe the nonlinear behavior of the process. Besides, the results showed that this technique can be successfully applied to control process algorithms due to its long processing time and its flexibility in the incorporation of new data
Forecasting the mortality rates of Indonesian population by using neural network
Safitri, Lutfiani; Mardiyati, Sri; Rahim, Hendrisman
2018-03-01
A model that can represent a problem is required in conducting a forecasting. One of the models that has been acknowledged by the actuary community in forecasting mortality rate is the Lee-Certer model. Lee Carter model supported by Neural Network will be used to calculate mortality forecasting in Indonesia. The type of Neural Network used is feedforward neural network aligned with backpropagation algorithm in python programming language. And the final result of this study is mortality rate in forecasting Indonesia for the next few years
An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network
Directory of Open Access Journals (Sweden)
Kai Hu
2013-01-01
Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.
Neural networks for triggering
International Nuclear Information System (INIS)
Denby, B.; Campbell, M.; Bedeschi, F.; Chriss, N.; Bowers, C.; Nesti, F.
1990-01-01
Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab
Structured Pyramidal Neural Networks.
Soares, Alessandra M; Fernandes, Bruno J T; Bastos-Filho, Carmelo J A
2017-02-09
The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.
Artificial neural/chemical networks
Caulfield, H. John
2001-11-01
What strikes the attention of a neural network designer is that the chemicals seem to work not so much on individual neural circuits as on neural cell assemblies. These are large blocks of neural networks that carry out high level tasks using their constituent networks as needed. It follows to us that we might seek ways of achieving that same sort of behavior in an artificial neural network. In what follows, we provide two examples of how that might be done in an artificial system.
F77NNS - A FORTRAN-77 NEURAL NETWORK SIMULATOR
Mitchell, P. H.
1994-01-01
F77NNS (A FORTRAN-77 Neural Network Simulator) simulates the popular back error propagation neural network. F77NNS is an ANSI-77 FORTRAN program designed to take advantage of vectorization when run on machines having this capability, but it will run on any computer with an ANSI-77 FORTRAN Compiler. Artificial neural networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to biological nerve cells. Problems which involve pattern matching or system modeling readily fit the class of problems which F77NNS is designed to solve. The program's formulation trains a neural network using Rumelhart's back-propagation algorithm. Typically the nodes of a network are grouped together into clumps called layers. A network will generally have an input layer through which the various environmental stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. The back-propagation training algorithm can require massive computational resources to implement a large network such as a network capable of learning text-to-phoneme pronunciation rules as in the famous Sehnowski experiment. The Sehnowski neural network learns to pronounce 1000 common English words. The standard input data defines the specific inputs that control the type of run to be made, and input files define the NN in terms of the layers and nodes, as well as the input/output (I/O) pairs. The program has a restart capability so that a neural network can be solved in stages suitable to the user's resources and desires. F77NNS allows the user to customize the patterns of connections between layers of a network. The size of the neural network to be solved is limited only by the amount of random access memory (RAM) available to the
Neural electrical activity and neural network growth.
Gafarov, F M
2018-02-09
The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.
Hindcasting cyclonic waves using neural networks
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Rao, S.; Chakravarty, N.V.
value by a weight. Then they attach a bias to this sum and pass on the result through a nonlinearity such as the “sigmoid transfer function”. This forms the input to the output layer that operates identically with the hidden layer nodes. Resulting.... In the backpropagation networks the error between the target output and the network output is calculated and this will be backpropagated using the steepest descent or gradient descent approach. The network weights and biases are adjusted by moving a small step...
PREDIKSI BISNIS FOREX MENGGUNAKAN MODEL NEURAL NETWORK BERBASIS ADA BOOST MENGGUNAKAN 2047 DATA
Directory of Open Access Journals (Sweden)
Suyatno Suyatno
2016-11-01
Full Text Available Setelah melakukan penelitian dan percobaan maka didapatkan hasil penelitian pertama yang telah dilakukan dengan menggunakan Algoritma Neural Network Backpropagatioan dengan menggunakan data sebanyak 268 menunjungkan tingkat akurasi error prediksi pada waktu prediksi per 5 menit sebesar 0.758619403, bila menggunakan data sebanyak 2047 menunjukkan tingkat akurasi error prediksi sebesar 0.500161212 dan hasil penelitian kedua yang telah dilakukan menggunakan Algoritma Optimasi Adaboost pada proses trainning dan ditambah Neural Network Backpropagation pada proses learning menunjukkan tingkat akurasi error prediksi pada waktu prediksi per 5 menit menggunakan data sebanyak 268 sebesar 0.397014925, bila menggunakan data sebanyak 2047 menunjukkan tingkat akurasi error prediksi sebesar 0.099951148. Tahap awal dalam melakukan penelitian ini sampai dengan pengujian menggunakan perhitungan prediksi nilai akurasi error menggunakan rumus MSE (Mean Sequare Error dengan menggunakan algoritma optimasi adaboost untuk memberikan jawaban atas permasalahan bahwa nilai akurasi error Algoritma Neural Network Backpropagation perlu direndahkan agar akurasi prediksi meningkat dan tahap kedua dilakukan uji coba menggunakan data yang lebih banyak dibandingan dengan tahap ke satu. Berdasarkan hasil penelitian yang telah dilakukan, dapat disimpulkan bahwa Algoritma Neural Network memiliki akurasi yang lebih rendah bila dibandingkan dengan akurasi menggunakan metode optimasi adaboost pada proses trainning ditambah dengan Neural Network, ini dapat dilihat dengan rendahnya tingkat error MSE menggunakan metode adaboost + neural network dan dapat disimpukan pula bahwa dengan menggunakan jumlah data yang lebih banyak maka dapat menurunkan tingkat akurasi error MSE sehingga berhasil meningkatkan akurasi prediksi dalam bisnis forex trading. Kata kunci: forex, trading, neural network, adaboost, central capital futures.
Fault Tolerant Neural Network for ECG Signal Classification Systems
Directory of Open Access Journals (Sweden)
MERAH, M.
2011-08-01
Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.
Supervised learning of probability distributions by neural networks
Baum, Eric B.; Wilczek, Frank
1988-01-01
Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.
Estimation of Solar Radiation using Artificial Neural Network
Directory of Open Access Journals (Sweden)
Slamet Suprayogi
2004-01-01
Full Text Available The solar radiation is the most important fator affeccting evapotranspiration, the mechanism of transporting the vapor from the water surface has also a great effect. The main objectives of this study were to investigate the potential of using Artificial Neural Network (ANN to predict solar radiation related to temperature. The three-layer backpropagation were developed, trained, and tested to forecast solar radiation for Ciriung sub Cachment. Result revealed that the ANN were able to well learn the events they were trained to recognize. Moreover, they were capable of effecctively generalize their training by predicting solar radiation for sets unseen cases.
An Artificial Neural Network for Data Forecasting Purposes
Directory of Open Access Journals (Sweden)
Catalina Lucia COCIANU
2015-01-01
Full Text Available Considering the fact that markets are generally influenced by different external factors, the stock market prediction is one of the most difficult tasks of time series analysis. The research reported in this paper aims to investigate the potential of artificial neural networks (ANN in solving the forecast task in the most general case, when the time series are non-stationary. We used a feed-forward neural architecture: the nonlinear autoregressive network with exogenous inputs. The network training function used to update the weight and bias parameters corresponds to gradient descent with adaptive learning rate variant of the backpropagation algorithm. The results obtained using this technique are compared with the ones resulted from some ARIMA models. We used the mean square error (MSE measure to evaluate the performances of these two models. The comparative analysis leads to the conclusion that the proposed model can be successfully applied to forecast the financial data.
Directory of Open Access Journals (Sweden)
Lin Liu
2010-01-01
Full Text Available Cognitive radio (CR is a technology to implement opportunistic spectrum sharing to improve the spectrum utilization. However, there exists a hidden-node problem, which can be a big challenge to solve especially when the primary receiver is passive listening. We aim to provide a solution to the hidden-node problem for passive-listening receiver based on cooperation of multiple CRs. Specifically, we consider a cooperative GPS-enabled cognitive network. Once the existence of PU is detected, a localization algorithm will be employed to first estimate the path loss model for the environment based on backpropagation method and then to locate the position of PU. Finally, a disable region is identified taking into account the communication range of both the PU and the CR. The CRs within the disabled region are prohibited to transmit in order to avoid interfering with the primary receiver. Both analysis and simulation results are provided.
International Nuclear Information System (INIS)
Nabeshima, Kunihiko; Suzuki, Katsuo; Shinohara, Yoshikuni; Tuerkcan, E.
1995-11-01
In this paper, the anomaly detection method for nuclear power plant monitoring and its program are described by using a neural network approach, which is based on the deviation between measured signals and output signals of neural network model. The neural network used in this study has three layered auto-associative network with 12 input/output, and backpropagation algorithm is adopted for learning. Furthermore, to obtain better dynamical model of the reactor plant, a new learning technique was developed in which the learning process of the present neural network is divided into initial and adaptive learning modes. The test results at the actual nuclear reactor shows that the neural network plant monitoring system is successfull in detecting in real-time the symptom of small anomaly over a wide power range including reactor start-up, shut-down and stationary operation. (author)
International Nuclear Information System (INIS)
Cadini, F.; Zio, E.; Pedroni, N.
2007-01-01
In this paper, a locally recurrent neural network (LRNN) is employed for approximating the temporal evolution of a nonlinear dynamic system model of a simplified nuclear reactor. To this aim, an infinite impulse response multi-layer perceptron (IIR-MLP) is trained according to a recursive back-propagation (RBP) algorithm. The network nodes contain internal feedback paths and their connections are realized by means of IIR synaptic filters, which provide the LRNN with the necessary system state memory
Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network
Yu, Hongshan; Peng, Jinzhu; Tang, Yandong
2014-01-01
Hammerstein model has been popularly applied to identify the nonlinear systems. In this paper, a Hammerstein-type neural network (HTNN) is derived to formulate the well-known Hammerstein model. The HTNN consists of a nonlinear static gain in cascade with a linear dynamic part. First, the Lipschitz criterion for order determination is derived. Second, the backpropagation algorithm for updating the network weights is presented, and the stability analysis is also drawn. Finally, simulation resul...
Nonlinear signal processing using neural networks: Prediction and system modelling
Energy Technology Data Exchange (ETDEWEB)
Lapedes, A.; Farber, R.
1987-06-01
The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.
Neural network classification of sweet potato embryos
Molto, Enrique; Harrell, Roy C.
1993-05-01
Somatic embryogenesis is a process that allows for the in vitro propagation of thousands of plants in sub-liter size vessels and has been successfully applied to many significant species. The heterogeneity of maturity and quality of embryos produced with this technique requires sorting to obtain a uniform product. An automated harvester is being developed at the University of Florida to sort embryos in vitro at different stages of maturation in a suspension culture. The system utilizes machine vision to characterize embryo morphology and a fluidic based separation device to isolate embryos associated with a pre-defined, targeted morphology. Two different backpropagation neural networks (BNN) were used to classify embryos based on information extracted from the vision system. One network utilized geometric features such as embryo area, length, and symmetry as inputs. The alternative network utilized polar coordinates of an embryo's perimeter with respect to its centroid as inputs. The performances of both techniques were compared with each other and with an embryo classification method based on linear discriminant analysis (LDA). Similar results were obtained with all three techniques. Classification efficiency was improved by reducing the dimension of the feature vector trough a forward stepwise analysis by LDA. In order to enhance the purity of the sample selected as harvestable, a reject to classify option was introduced in the model and analyzed. The best classifier performances (76% overall correct classifications, 75% harvestable objects properly classified, homogeneity improvement ratio 1.5) were obtained using 8 features in a BNN.
Speech Recognizing for Presentation Tool Navigation Using Back Propagation Artificial Neural Network
Directory of Open Access Journals (Sweden)
Hasanah Nur
2016-01-01
Full Text Available Backpropagation Artificial Neural Network (ANN is a well known branch of Artificial Intelligence and has been proven to solve various problems of complex speech recognizing in health [1], [2], education [4] and engineering [3]. Today, many kinds of presentation tools are used by society. One popular example is MsPowerpoint. The transition process between slides in presentation tools will be more easily done through speech, the sound emitted directly by the user during the presentation. This study uses research and development to create a simulation using Backpropagation ANN for speech recognition from number one to five to navigate slides of the presentation tool. The Backpropagation ANN consists of one input layer, one hidden layer with 100 neurons and one output layer. The simulation is built by using a Neural Network Toolbox Matlab R2014a. Speech samples were taken from five different people with wav format. This research shows that the Backpropagation ANN can be used as navigation through speech with 96% accuracy rate based on the network training result. Thesimulation can produce 63% accuracy based on 100 new speech samples from various sources.
[Artificial neural networks in Neurosciences].
Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María
2011-11-01
This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.
Accelerating Learning By Neural Networks
Toomarian, Nikzad; Barhen, Jacob
1992-01-01
Electronic neural networks made to learn faster by use of terminal teacher forcing. Method of supervised learning involves addition of teacher forcing functions to excitations fed as inputs to output neurons. Initially, teacher forcing functions are strong enough to force outputs to desired values; subsequently, these functions decay with time. When learning successfully completed, terminal teacher forcing vanishes, and dynamics or neural network become equivalent to those of conventional neural network. Simulated neural network with terminal teacher forcing learned to produce close approximation of circular trajectory in 400 iterations.
Evolution of an artificial neural network based autonomous land vehicle controller.
Baluja, S
1996-01-01
This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks.
Iris double recognition based on modified evolutionary neural network
Liu, Shuai; Liu, Yuan-Ning; Zhu, Xiao-Dong; Huo, Guang; Liu, Wen-Tao; Feng, Jia-Kai
2017-11-01
Aiming at multicategory iris recognition under illumination and noise interference, this paper proposes a method of iris double recognition based on a modified evolutionary neural network. An equalization histogram and Laplace of Gaussian operator are used to process the iris to suppress illumination and noise interference and Haar wavelet to convert the iris feature to binary feature encoding. Calculate the Hamming distance for the test iris and template iris , and compare with classification threshold, determine the type of iris. If the iris cannot be identified as a different type, there needs to be a secondary recognition. The connection weights in back-propagation (BP) neural network use modified evolutionary neural network to adaptively train. The modified neural network is composed of particle swarm optimization with mutation operator and BP neural network. According to different iris libraries in different circumstances of experimental results, under illumination and noise interference, the correct recognition rate of this algorithm is higher, the ROC curve is closer to the coordinate axis, the training and recognition time is shorter, and the stability and the robustness are better.
Chen, Chau-Kuang
2010-01-01
Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…
Multithreading with separate data to improve the performance of Backpropagation method
Dhamma, Mulia; Zarlis, Muhammad; Budhiarti Nababan, Erna
2017-12-01
Backpropagation is one method of artificial neural network that can make a prediction for a new data with learning by supervised of the past data. The learning process of backpropagation method will become slow if we give too much data for backpropagation method to learn the data. Multithreading with a separate data inside of each thread are being used in order to improve the performance of backpropagtion method . Base on the research for 39 data and also 5 times experiment with separate data into 2 thread, the result showed that the average epoch become 6490 when using 2 thread and 453049 epoch when using only 1 thread. The most lowest epoch for 2 thread is 1295 and 1 thread is 356116. The process of improvement is caused by the minimum error from 2 thread that has been compared to take the weight and bias value. This process will be repeat as long as the backpropagation do learning.
Heiden, Uwe
1980-01-01
The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica ted throughout the text. However, they are not explored in de tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be havior of neurons or neuron pools. In this respect the essay is writt...
Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels
Hill, Eric v. K.; Dion, Seth-Andrew T.; Karl, Justin O.; Spivey, Nicholas S.; Walker, James L., II
2007-01-01
Acoustic emission data were collected during the hydroburst testing of eleven 15 inch diameter filament wound composite overwrapped pressure vessels. A neural network burst pressure prediction was generated from the resulting AE amplitude data. The bottles shared commonality of graphite fiber, epoxy resin, and cure time. Individual bottles varied by cure mode (rotisserie versus static oven curing), types of inflicted damage, temperature of the pressurant, and pressurization scheme. Three categorical variables were selected to represent undamaged bottles, impact damaged bottles, and bottles with lacerated hoop fibers. This categorization along with the removal of the AE data from the disbonding noise between the aluminum liner and the composite overwrap allowed the prediction of burst pressures in all three sets of bottles using a single backpropagation neural network. Here the worst case error was 3.38 percent.
Learning of N-layers neural network
Directory of Open Access Journals (Sweden)
Vladimír Konečný
2005-01-01
Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.
TAO-robust backpropagation learning algorithm.
Pernía-Espinoza, Alpha V; Ordieres-Meré, Joaquín B; Martínez-de-Pisón, Francisco J; González-Marcos, Ana
2005-03-01
In several fields, as industrial modelling, multilayer feedforward neural networks are often used as universal function approximations. These supervised neural networks are commonly trained by a traditional backpropagation learning format, which minimises the mean squared error (mse) of the training data. However, in the presence of corrupted data (outliers) this training scheme may produce wrong models. We combine the benefits of the non-linear regression model tau-estimates [introduced by Tabatabai, M. A. Argyros, I. K. Robust Estimation and testing for general nonlinear regression models. Applied Mathematics and Computation. 58 (1993) 85-101] with the backpropagation algorithm to produce the TAO-robust learning algorithm, in order to deal with the problems of modelling with outliers. The cost function of this approach has a bounded influence function given by the weighted average of two psi functions, one corresponding to a very robust estimate and the other to a highly efficient estimate. The advantages of the proposed algorithm are studied with an example.
Egg hatchability prediction by multiple linear regression and artificial neural networks
Directory of Open Access Journals (Sweden)
AC Bolzan
2008-06-01
Full Text Available An artificial neural network (ANN was compared with a multiple linear regression statistical method to predict hatchability in an artificial incubation process. A feedforward neural network architecture was applied. Network trainings were made by the backpropagation algorithm based on data obtained from industrial incubations. The ANN model was chosen as it produced data that fit better the experimental data as compared to the multiple linear regression model, which used coefficients determined by minimum square method. The proposed simulation results of these approaches indicate that this ANN can be used for incubation performance prediction.
Empirical modeling of nuclear power plants using neural networks
International Nuclear Information System (INIS)
Parlos, A.G.; Atiya, A.; Chong, K.T.
1991-01-01
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
Neural networks at the Tevatron
International Nuclear Information System (INIS)
Badgett, W.; Burkett, K.; Campbell, M.K.; Wu, D.Y.; Bianchin, S.; DeNardi, M.; Pauletta, G.; Santi, L.; Caner, A.; Denby, B.; Haggerty, H.; Lindsey, C.S.; Wainer, N.; Dall'Agata, M.; Johns, K.; Dickson, M.; Stanco, L.; Wyss, J.L.
1992-10-01
This paper summarizes neural network applications at the Fermilab Tevatron, including the first online hardware application in high energy physics (muon tracking): the CDF and DO neural network triggers; offline quark/gluon discrimination at CDF; ND a new tool for top to multijets recognition at CDF
Optoelectronic Implementation of Neural Networks
Indian Academy of Sciences (India)
optical neural network using photo refractive crystals and realized interconnection density of 10 8 to. 1010 per cm3. • B Javidi and others designed a correlato.,. based two-layer neural network associated with a supervised perceptron learning algorithm for r~al-time face recognition. electronic wiring altogether and replace it ...
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Tagliaferri, Roberto; Longo, Giuseppe; Milano, Leopoldo; Acernese, Fausto; Barone, Fabrizio; Ciaramella, Angelo; De Rosa, Rosario; Donalek, Ciro; Eleuteri, Antonio; Raiconi, Giancarlo; Sessa, Salvatore; Staiano, Antonino; Volpicelli, Alfredo
2003-01-01
In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of data reduction and data mining. The federation of heterogeneous large astronomical databases which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based on NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno).
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)
Method Accelerates Training Of Some Neural Networks
Shelton, Robert O.
1992-01-01
Three-layer networks trained faster provided two conditions are satisfied: numbers of neurons in layers are such that majority of work done in synaptic connections between input and hidden layers, and number of neurons in input layer at least as great as number of training pairs of input and output vectors. Based on modified version of back-propagation method.
Neural Networks in Control Applications
DEFF Research Database (Denmark)
Sørensen, O.
The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...
Artificial Neural Network applied to lightning flashes
Gin, R. B.; Guedes, D.; Bianchi, R.
2013-05-01
The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a
Falat, Lukas; Marcek, Dusan; Durisova, Maria
2016-01-01
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Directory of Open Access Journals (Sweden)
Lukas Falat
2016-01-01
Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Power plant fault detection using artificial neural network
Thanakodi, Suresh; Nazar, Nazatul Shiema Moh; Joini, Nur Fazriana; Hidzir, Hidzrin Dayana Mohd; Awira, Mohammad Zulfikar Khairul
2018-02-01
The fault that commonly occurs in power plants is due to various factors that affect the system outage. There are many types of faults in power plants such as single line to ground fault, double line to ground fault, and line to line fault. The primary aim of this paper is to diagnose the fault in 14 buses power plants by using an Artificial Neural Network (ANN). The Multilayered Perceptron Network (MLP) that detection trained utilized the offline training methods such as Gradient Descent Backpropagation (GDBP), Levenberg-Marquardt (LM), and Bayesian Regularization (BR). The best method is used to build the Graphical User Interface (GUI). The modelling of 14 buses power plant, network training, and GUI used the MATLAB software.
A Neural Network Approach for GMA Butt Joint Welding
DEFF Research Database (Denmark)
Christensen, Kim Hardam; Sørensen, Torben
2003-01-01
penetration, when the gap width is varying during the welding process. The process modeling to facilitate the mapping from joint geometry and reference weld quality to significant welding parameters has been based on a multi-layer feed-forward network. The Levenberg-Marquardt algorithm for non-linear least......This paper describes the application of the neural network technology for gas metal arc welding (GMAW) control. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a certain degree of quality in the field of butt joint welding with full...... squares has been used with the back-propagation algorithm for training the network, while a Bayesian regularization technique has been successfully applied for minimizing the risk of inexpedient over-training. Finally, a predictive closed-loop control strategy based on a so-called single-neuron self...
Selverston, A I; Moulins, M
1985-01-01
Despite the fact that a large number of neuronal oscillators have been described, there are only a few good examples that illustrate how they operate at the cellular level. For most, there is some isolated information about different aspects of the oscillator network, but too little to explain the whole mechanism. Two quite remarkable features do seem to be emerging from ongoing studies, however. One is that there are very few generalizable features common to neural oscillators. Many utilize reciprocal inhibitory circuits and endogenous burst-generating currents to some extent. All that have been well worked out utilize a combination of both cellular and network properties, but little else in the way of common mechanism is noteworthy. Perhaps the most interesting aspect of recent work is the ability of a particular oscillator to produce a large repertoire of different outputs. This is separate and in addition to changes occurring via phasic sensory feedback. It is in fact a radical functional "rewiring" of the network in response to neuromodulators. The CPG circuits represent only the most basic form of a given pattern. Finally, concerning the role of sensory feedback in generating oscillatory patterns, the concept of the CPG as a group of neurons able to produce oscillatory patterns without any sensory feedback is, in our opinion, still valid. There is no doubt that some oscillators may be quite weak when isolated, but they can still produce bursts with firing sequences similar to those seen in vivo. The fact that sensory feedback can both control and enhance the oscillations has never been in doubt. Similarly, entrainment of the pattern by sensory feedback does not mean that the receptor is part of the generator, only that it has access to it (as do command and coordinating fibers). The real question remains: Can a group of cells produce an oscillatory pattern without phasic sensory input? We must answer this affirmatively even for the insect-flight motor CPG
Nonlinear identification of process dynamics using neural networks
International Nuclear Information System (INIS)
Parlos, A.G.; Atiya, A.F.; Chong, K.T.
1992-01-01
In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. 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 temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios
International Nuclear Information System (INIS)
Seker, Serhat; Tuerkcan, Erdinc; Ayaz, Emine; Barutcu, Burak
2003-01-01
This paper addresses to the problem of utilisation of the artificial neural networks (ANNs) for detecting anomalies as well as physical parameters of a nuclear power plant during power operation in real time. Three different types of neural network algorithms were used namely, feed-forward neural network (back-propagation, BP) and two types of recurrent neural networks (RNN). The data used in this paper were gathered from the simulation of the power operation of the Japan's High Temperature Engineering Testing Reactor (HTTR). For the wide range of power operation, 56 signals were generated by the reactor dynamic simulation code for several hours of normal power operation at different power ramps between 30 and 100% nominal power. Paper will compare the outcomes of different neural networks and presents the neural network system and the determination of physical parameters from the simulated operating data
Vibration monitoring of EDF rotating machinery using artificial neural networks
International Nuclear Information System (INIS)
Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E.; Hamon, L.; Lefevre, F.
1991-01-01
Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging
Neural network classification of questionable EGRET events
Meetre, C. A.; Norris, J. P.
1992-01-01
High energy gamma rays (greater than 20 MeV) pair producing in the spark chamber of the Energetic Gamma Ray Telescope Experiment (EGRET) give rise to a characteristic but highly variable 3-D locus of spark sites, which must be processed to decide whether the event is to be included in the database. A significant fraction (about 15 percent or 10(exp 4) events/day) of the candidate events cannot be categorized (accept/reject) by an automated rule-based procedure; they are therefore tagged, and must be examined and classified manually by a team of expert analysts. We describe a feedforward, back-propagation neural network approach to the classification of the questionable events. The algorithm computes a set of coefficients using representative exemplars drawn from the preclassified set of questionable events. These coefficients map a given input event into a decision vector that, ideally, describes the correct disposition of the event. The net's accuracy is then tested using a different subset of preclassified events. Preliminary results demonstrate the net's ability to correctly classify a large proportion of the events for some categories of questionables. Current work includes the use of much larger training sets to improve the accuracy of the net.
Failure detection studies by layered neural network
International Nuclear Information System (INIS)
Ciftcioglu, O.; Seker, S.; Turkcan, E.
1991-06-01
Failure detection studies by layered neural network (NN) are described. The particular application area is an operating nuclear power plant and the failure detection is of concern as result of system surveillance in real-time. The NN system is considered to be consisting of 3 layers, one of which being hidden, and the NN parameters are determined adaptively by the backpropagation (BP) method, the process being the training phase. Studies are performed using the power spectra of the pressure signal of the primary system of an operating nuclear power plant of PWR type. The studies revealed that, by means of NN approach, failure detection can effectively be carried out using the redundant information as well as this is the case in this work; namely, from measurement of the primary pressure signals one can estimate the primary system coolant temperature and hence the deviation from the operational temperature state, the operational status identified in the training phase being referred to as normal. (author). 13 refs.; 4 figs.; 2 tabs
Design of an Adaptive-Neural Network Attitude Controller of a Satellite using Reaction Wheels
Directory of Open Access Journals (Sweden)
Abbas Ajorkar
2015-04-01
Full Text Available In this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. Then, an attitude control based on feedback linearization control has been designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. In order to eliminate the effect of these uncertainties, a multilayer neural network with back-propagation law is designed. In this structure, the parameters of the moment of inertia matrix and external disturbances are estimated and used in feedback linearization control law. Finally, the performance of the designed attitude controller is investigated by several simulations.
Energy Technology Data Exchange (ETDEWEB)
Koenig, R.W.T.; Von Hellermann, M. [Commission of the European Communities, Abingdon (United Kingdom). JET Joint Undertaking; Svensson, J. [Royal Inst. of Tech., Stockholm (Sweden)
1994-07-01
A back-propagation neural network technique is used at JET to extract plasma parameters like ion temperature, rotation velocities or spectral line intensities from charge exchange (CX) spectra. It is shown that in the case of the C VI CX spectra, neural networks can give a good estimation (better than +-20% accuracy) for the main plasma parameters (Ti, V{sub rot}). Since the neural network approach involves no iterations or initial guesses the speed with which a spectrum is processed is so high (0.2 ms/spectrum) that real time analysis will be achieved in the near future. 4 refs., 8 figs.
Novel maximum-margin training algorithms for supervised neural networks.
Ludwig, Oswaldo; Nunes, Urbano
2010-06-01
This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by
Maximum solid concentrations of coal water slurries predicted by neural network models
Energy Technology Data Exchange (ETDEWEB)
Cheng, Jun; Li, Yanchang; Zhou, Junhu; Liu, Jianzhong; Cen, Kefa
2010-12-15
The nonlinear back-propagation (BP) neural network models were developed to predict the maximum solid concentration of coal water slurry (CWS) which is a substitute for oil fuel, based on physicochemical properties of 37 typical Chinese coals. The Levenberg-Marquardt algorithm was used to train five BP neural network models with different input factors. The data pretreatment method, learning rate and hidden neuron number were optimized by training models. It is found that the Hardgrove grindability index (HGI), moisture and coalification degree of parent coal are 3 indispensable factors for the prediction of CWS maximum solid concentration. Each BP neural network model gives a more accurate prediction result than the traditional polynomial regression equation. The BP neural network model with 3 input factors of HGI, moisture and oxygen/carbon ratio gives the smallest mean absolute error of 0.40%, which is much lower than that of 1.15% given by the traditional polynomial regression equation. (author)
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...
Neural Networks For Robot Control
National Research Council Canada - National Science Library
Nasr, Chaiban
2001-01-01
...; and optimization of the architecture; (b) Application of artificial neural networks in controlling closed-loop 2D planar robot arm and comparison with the use of proportional-integral-differential (PID) controllers...
What are artificial neural networks?
DEFF Research Database (Denmark)
Krogh, Anders
2008-01-01
Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...
Research on artificial neural network applications for nuclear power plants
International Nuclear Information System (INIS)
Chang, Soon-Heung; Cheon, Se-Woo
1992-01-01
Artificial neural networks (ANNs) are an emerging computational technology which can significantly enhance a number of applications. These consist of many interconnected processing elements that exhibit human-like performance, i.e., learning, pattern recognition and associative memory skills. Several application studies on ANNs devoted to nuclear power plants have been carried out at the Korea Advanced Institute of Science and Technology since 1989. These studies include the feasibility of using ANNs for the following tasks: (1) thermal power prediction, (2) transient identification, (3) multiple alarm processing and diagnosis, (4) core thermal margin prediction, and (5) prediction of core parameters for fuel reloading. This paper introduces the back-propagation network (BPN) model which is the most commonly used algorithm, and summarizes each of the studies briefly. (author)
Complex-Valued Neural Networks
Hirose, Akira
2012-01-01
This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...
Robust recurrent neural network modeling for software fault detection and correction prediction
International Nuclear Information System (INIS)
Hu, Q.P.; Xie, M.; Ng, S.H.; Levitin, G.
2007-01-01
Software fault detection and correction processes are related although different, and they should be studied together. A practical approach is to apply software reliability growth models to model fault detection, and fault correction process is assumed to be a delayed process. On the other hand, the artificial neural networks model, as a data-driven approach, tries to model these two processes together with no assumptions. Specifically, feedforward backpropagation networks have shown their advantages over analytical models in fault number predictions. In this paper, the following approach is explored. First, recurrent neural networks are applied to model these two processes together. Within this framework, a systematic networks configuration approach is developed with genetic algorithm according to the prediction performance. In order to provide robust predictions, an extra factor characterizing the dispersion of prediction repetitions is incorporated into the performance function. Comparisons with feedforward neural networks and analytical models are developed with respect to a real data set
Advanced approach to numerical forecasting using artificial neural networks
Directory of Open Access Journals (Sweden)
Michael Štencl
2009-01-01
Full Text Available Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differences then verify on practical comparative example.
Aspects of the numerical analysis of neural networks
Ellacott, S. W.
This article starts with a brief introduction to neural networks for those unfamiliar with the basic concepts, together with a very brief overview of mathematical approaches to the subject. This is followed by a more detailed look at three areas of research which are of particular interest to numerical analysts.The first area is approximation theory. If K is a compact set in n, for some n, then it is proved that a semilinear feedforward network with one hidden layer can uniformly approximate any continuous function in C(K) to any required accuracy. A discussion of known results and open questions on the degree of approximation is included. We also consider the relevance of radial basis functions to neural networks.The second area considered is that of learning algorithms. A detailed analysis of one popular algorithm (the delta rule) will be given, indicating why one implementation leads to a stable numerical process, whereas an initially attractive variant (essentially a form of steepest descent) does not. Similar considerations apply to the backpropagation algorithm. The effect of filtering and other preprocessing of the input data will also be discussed systematically.Finally some applications of neural networks to numerical computation are considered.
Evolving RBF neural networks for adaptive soft-sensor design.
Alexandridis, Alex
2013-12-01
This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.
Directory of Open Access Journals (Sweden)
Tao Ma
2016-10-01
Full Text Available The development of intrusion detection systems (IDS that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC and deep neural network (DNN algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN, support vector machine (SVM, random forest (RF and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.
Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.
Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu
2017-10-01
This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.
Neural networks and wavelet analysis in the computer interpretation of pulse oximetry data
Energy Technology Data Exchange (ETDEWEB)
Dowla, F.U.; Skokowski, P.G.; Leach, R.R. Jr.
1996-03-01
Pulse oximeters determine the oxygen saturation level of blood by measuring the light absorption of arterial blood. The sensor consists of red and infrared light sources and photodetectors. A method based on neural networks and wavelet analysis is developed for improved saturation estimation in the presence of sensor motion. Spectral and correlation functions of the dual channel oximetry data are used by a backpropagation neural network to characterize the type of motion. Amplitude ratios of red to infrared signals as a function of time scale are obtained from the multiresolution wavelet decomposition of the two-channel data. Motion class and amplitude ratios are then combined to obtain a short-time estimate of the oxygen saturation level. A final estimate of oxygen saturation is obtained by applying a 15 s smoothing filter on the short-time measurements based on 3.5 s windows sampled every 1.75 s. The design employs two backpropagation neural networks. The first neural network determines the motion characteristics and the second network determines the saturation estimate. Our approach utilizes waveform analysis in contrast to the standard algorithms that are based on the successful detection of peaks and troughs in the signal. The proposed algorithm is numerically efficient and has stable characteristics with a reduced false alarm rate with a small loss in detection. The method can be rapidly developed on a digital signal processing platform.
Use of Neural Networks for Damage Assessment in a Steel Mast
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Rytter, A.
1994-01-01
In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with the Backpropagation Algorithm for detecting location and size of a damage in a civil engineering structure is investigated. The structure considered is a 20 m high steel lattice mast subjected to wind excita...... as well as full-scale tests where the mast is identified by an ARMA-model. The results show that a neural network trained with simulated data is capable for detecting location of a damage in a steel lattice mast when the network is subjected to experimental data.·...
Lure, Y. M. Fleming; Grody, Norman C.; Chiou, Y. S. Peter; Yeh, H. Y. Michael
1993-01-01
A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular
BACKPROPAGATION TRAINING ALGORITHM WITH ADAPTIVE PARAMETERS TO SOLVE DIGITAL PROBLEMS
Directory of Open Access Journals (Sweden)
R. Saraswathi
2011-01-01
Full Text Available An efficient technique namely Backpropagation training with adaptive parameters using Lyapunov Stability Theory for training single hidden layer feed forward network is proposed. A three-layered Feedforward neural network architecture is used to solve the selected problems. Sequential Training Mode is used to train the network. Lyapunov stability theory is employed to ensure the faster and steady state error convergence and to construct and energy surface with a single global minimum point through the adaptive adjustment of the weights and the adaptive parameter ß. To avoid local minima entrapment, an adaptive backpropagation algorithm based on Lyapunov stability theory is used. Lyapunov stability theory gives the algorithm, the efficiency of attaining a single global minimum point. The learning parameters used in this algorithm is responsible for the faster error convergence. The adaptive learning parameter used in this algorithm is chosen properly for faster error convergence. The error obtained has been asymptotically converged to zero according to Lyapunov Stability theory. The performance of the adaptive Backpropagation algorithm is measured by solving parity problem, half adder and full adder problems.
Modeling of methane emissions using artificial neural network approach
Directory of Open Access Journals (Sweden)
Stamenković Lidija J.
2015-01-01
Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007
Application of neural networks to signal prediction in nuclear power plant
International Nuclear Information System (INIS)
Wan Joo Kim; Soon Heung Chang; Byung Ho Lee
1993-01-01
This paper describes the feasibility study of an artificial neural network for signal prediction. The purpose of signal prediction is to estimate the value of undetected next time step signal. As the prediction method, based on the idea of auto regression, a few previous signals are inputs to the artificial neural network and the signal value of next time step is estimated with the outputs of the network. The artificial neural network can be applied to the nonlinear system and answers in short time. The training algorithm is a modified backpropagation model, which can effectively reduce the training time. The target signal of the simulation is the steam generator water level, which is one of the important parameters in nuclear power plants. The simulation result shows that the predicted value follows the real trend well
Putra, J. C. P.; Safrilah
2017-06-01
Artificial neural network approaches are useful to solve many complicated problems. It solves a number of problems in various areas such as engineering, medicine, business, manufacturing, etc. This paper presents an application of artificial neural network to predict a runway capacity at Juanda International Airport. An artificial neural network model of backpropagation and multi-layer perceptron is adopted to this research to learning process of runway capacity at Juanda International Airport. The results indicate that the training data is successfully recognizing the certain pattern of runway use at Juanda International Airport. Whereas, testing data indicate vice versa. Finally, it can be concluded that the approach of uniformity data and network architecture is the critical part to determine the accuracy of prediction results.
Directory of Open Access Journals (Sweden)
Małgorzata Pawul
2016-09-01
Full Text Available Recently, a lot of attention was paid to the improvement of methods which are used to air quality forecasting. Artificial neural networks can be applied to model these problems. Their advantage is that they can solve the problem in the conditions of incomplete information, without the knowledge of the analytical relationship between the input and output data. In this paper we applied artificial neural networks to predict the PM 10 concentrations as factors determining the occurrence of smog phenomena. To create these networks we used meteorological data and concentrations of PM 10. The data were recorded in 2014 and 2015 at three measuring stations operating in Krakow under the State Environmental Monitoring. The best results were obtained by three-layer perceptron with back-propagation algorithm. The neural networks received a good fit in all cases.
Neural networks in signal processing
International Nuclear Information System (INIS)
Govil, R.
2000-01-01
Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)
Hayashi, Hideaki; Shima, Keisuke; Shibanoki, Taro; Kurita, Yuichi; Tsuji, Toshio
2013-01-01
This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.
Determining the confidence levels of sensor outputs using neural networks
International Nuclear Information System (INIS)
Broten, G.S.; Wood, H.C.
1995-01-01
This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network's ability to determine the confidence level is influenced by the complexity of the sensor's response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in
Directory of Open Access Journals (Sweden)
Saleh Mohammed Al-Alawi
2002-06-01
Full Text Available Artificial Neural Networks (ANNs are computer software programs that mimic the human brain's ability to classify patterns or to make forecasts or decisions based on past experience. The development of this research area can be attributed to two factors, sufficient computer power to begin practical ANN-based research in the late 1970s and the development of back-propagation in 1986 that enabled ANN models to solve everyday business, scientific, and industrial problems. Since then, significant applications have been implemented in several fields of study, and many useful intelligent applications and systems have been developed. The objective of this paper is to generate awareness and to encourage applications development using artificial intelligence-based systems. Therefore, this paper provides basic ANN concepts, outlines steps used for ANN model development, and lists examples of engineering applications based on the use of the back-propagation paradigm conducted in Oman. The paper is intended to provide guidelines and necessary references and resources for novice individuals interested in conducting research in engineering or other fields of study using back-propagation artificial neural networks.
Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network
Directory of Open Access Journals (Sweden)
Hanxin Chen
2013-01-01
Full Text Available A novel intelligent method based on wavelet neural network (WNN was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.
Optical proximity correction using a multilayer perceptron neural network
International Nuclear Information System (INIS)
Luo, Rui
2013-01-01
Optical proximity correction (OPC) is one of the resolution enhancement techniques (RETs) in optical lithography, where the mask pattern is modified to improve the output pattern fidelity. Algorithms are needed to generate the modified mask pattern automatically and efficiently. In this paper, a multilayer perceptron (MLP) neural network (NN) is used to synthesize the mask pattern. We employ the pixel-based approach in this work. The MLP takes the pixel values of the desired output wafer pattern as input, and outputs the optimal mask pixel values. The MLP is trained with the backpropagation algorithm, with a training set retrieved from the desired output pattern, and the optimal mask pattern obtained by the model-based method. After training, the MLP is able to generate the optimal mask pattern non-iteratively with good pattern fidelity. (paper)
Neural Networks in Control Applications
DEFF Research Database (Denmark)
Sørensen, O.
study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...... concerning canonical, observable state space forms (minimum realizable form) for SISO as wll as MIMO processes. The tests show that all models, after succeeeful training, which is judged by correlation analysis of the prediction errors, are able to perform non-linear system identification, prediction...
Neural Networks for Flight Control
Jorgensen, Charles C.
1996-01-01
Neural networks are being developed at NASA Ames Research Center to permit real-time adaptive control of time varying nonlinear systems, enhance the fault-tolerance of mission hardware, and permit online system reconfiguration. In general, the problem of controlling time varying nonlinear systems with unknown structures has not been solved. Adaptive neural control techniques show considerable promise and are being applied to technical challenges including automated docking of spacecraft, dynamic balancing of the space station centrifuge, online reconfiguration of damaged aircraft, and reducing cost of new air and spacecraft designs. Our experiences have shown that neural network algorithms solved certain problems that conventional control methods have been unable to effectively address. These include damage mitigation in nonlinear reconfiguration flight control, early performance estimation of new aircraft designs, compensation for damaged planetary mission hardware by using redundant manipulator capability, and space sensor platform stabilization. This presentation explored these developments in the context of neural network control theory. The discussion began with an overview of why neural control has proven attractive for NASA application domains. The more important issues in control system development were then discussed with references to significant technical advances in the literature. Examples of how these methods have been applied were given, followed by projections of emerging application needs and directions.
Generalization performance of regularized neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1994-01-01
Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...
Chettri, Samir R.; Cromp, Robert F.
1993-01-01
In this paper we discuss a neural network architecture (the Probabilistic Neural Net or the PNN) that, to the best of our knowledge, has not previously been applied to remotely sensed data. The PNN is a supervised non-parametric classification algorithm as opposed to the Gaussian maximum likelihood classifier (GMLC). The PNN works by fitting a Gaussian kernel to each training point. The width of the Gaussian is controlled by a tuning parameter called the window width. If very small widths are used, the method is equivalent to the nearest neighbor method. For large windows, the PNN behaves like the GMLC. The basic implementation of the PNN requires no training time at all. In this respect it is far better than the commonly used backpropagation neural network which can be shown to take O(N6) time for training where N is the dimensionality of the input vector. In addition the PNN can be implemented in a feed forward mode in hardware. The disadvantage of the PNN is that it requires all the training data to be stored. Some solutions to this problem are discussed in the paper. Finally, we discuss the accuracy of the PNN with respect to the GMLC and the backpropagation neural network (BPNN). The PNN is shown to be better than GMLC and not as good as the BPNN with regards to classification accuracy.
Neural networks and applications tutorial
Guyon, I.
1991-09-01
The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.
Neural networks for improved target differentiation and localization with sonar.
Ayrulu, B; Barshan, B
2001-04-01
This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. Differentiation of such features is of interest for intelligent systems in a variety of applications. Different representations of amplitude and time-of-flight measurement patterns acquired from a real sonar system are processed. In most cases, best results are obtained with the low-frequency component of the discrete wavelet transform of these patterns. Modular and non-modular neural network structures trained with the back-propagation and generating-shrinking algorithms are used to incorporate learning in the identification of parameter relations for target primitives. Networks trained with the generating-shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. Neural networks can differentiate more targets employing only a single sensor node, with a higher correct differentiation percentage (99%) than achieved with previously reported methods (61-90%) employing multiple sensor nodes. A sensor node is a pair of transducers with fixed separation, that can rotate and scan the target to collect data. Had the number of sensing nodes been reduced in the other methods, their performance would have been even worse. The success of the neural network approach shows that the sonar signals do contain sufficient information to differentiate all target types, but the previously reported methods are unable to resolve this identifying information. This work can find application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems.
A Pruning Neural Network Model in Credit Classification Analysis
Directory of Open Access Journals (Sweden)
Yajiao Tang
2018-01-01
Full Text Available Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.
Gross domestic product estimation based on electricity utilization by artificial neural network
Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.
2018-01-01
The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.
Directory of Open Access Journals (Sweden)
Asad Dehghani Samani
2017-07-01
Full Text Available Application of Artificial Neural Network (ANN in modeling of combined cycle power plant (CCPP with dry cooling tower (Heller tower has been investigated in this paper. Prediction of power plant output (megawatt under different working conditions was made using multi-layer feed-forward ANN and training was performed with operational data using back-propagation. Two ANN network was constructed for the steam turbine (ST and the main cooling system(MCS. Results indicate that the ANN model is effective in predicting the power plant output with good accuracy.
Adaptive online state-of-charge determination based on neuro-controller and neural network
Energy Technology Data Exchange (ETDEWEB)
Shen Yanqing, E-mail: network_hawk@126.co [Department of Automation, Chongqing Industry Polytechnic College, Jiulongpo District, Chongqing 400050 (China)
2010-05-15
This paper presents a novel approach using adaptive artificial neural network based model and neuro-controller for online cell State of Charge (SOC) determination. Taking cell SOC as model's predictive control input unit, radial basis function neural network, which can adjust its structure to prediction error with recursive least square algorithm, is used to simulate battery system. Besides that, neuro-controller based on Back-Propagation Neural Network (BPNN) and modified PID controller is used to decide the control input of battery system, i.e., cell SOC. Finally this algorithm is applied for the SOC determination of lead-acid batteries, and results of lab tests on physical cells, compared with model prediction, are presented. Results show that the ANN based battery system model adaptively simulates battery system with great accuracy, and the predicted SOC simultaneously converges to the real value quickly within the error of +-1 as time goes on.
Directory of Open Access Journals (Sweden)
Sen Tian
2014-01-01
Full Text Available With the development of mine industry, tailings storage facility (TSF, as the important facility of mining, has attracted increasing attention for its safety problems. However, the problems of low accuracy and slow operation rate often occur in current TSF safety evaluation models. This paper establishes a reasonable TSF safety evaluation index system and puts forward a new TSF safety evaluation model by combining the theories for the analytic hierarchy process (AHP and improved back-propagation (BP neural network algorithm. The varying proportions of cross validation were calculated, demonstrating that this method has better evaluation performance with higher learning efficiency and faster convergence speed and avoids the oscillation in the training process in traditional BP neural network method and other primary neural network methods. The entire analysis shows the combination of the two methods increases the accuracy and reliability of the safety evaluation, and it can be well applied in the TSF safety evaluation.
Butterfly Classification by HSI and RGB Color Models Using Neural Networks
Directory of Open Access Journals (Sweden)
Jorge E. Grajales-Múnera
2013-11-01
Full Text Available This study aims the classification of Butterfly species through the implementation of Neural Networks and Image Processing. A total of 9 species of Morpho genre which has blue as a characteristic color are processed. For Butterfly segmentation we used image processing tools such as: Binarization, edge processing and mathematical morphology. For data processing RGB values are obtained for every image which are converted to HSI color model to identify blue pixels and obtain the data to the proposed Neural Networks: Back-Propagation and Perceptron. For analysis and verification of results confusion matrix are built and analyzed with the results of neural networks with the lowest error levels. We obtain error levels close to 1% in classification of some Butterfly species.
Using a multi-state recurrent neural network to optimize loading patterns in BWRs
International Nuclear Information System (INIS)
Ortiz, Juan Jose; Requena, Ignacio
2004-01-01
A Multi-State Recurrent Neural Network is used to optimize Loading Patterns (LP) in BWRs. We have proposed an energy function that depends on fuel assembly positions and their nuclear cross sections to carry out optimisation. Multi-State Recurrent Neural Networks creates LPs that satisfy the Radial Power Peaking Factor and maximize the effective multiplication factor at the Beginning of the Cycle, and also satisfy the Minimum Critical Power Ratio and Maximum Linear Heat Generation Rate at the End of the Cycle, thereby maximizing the effective multiplication factor. In order to evaluate the LPs, we have used a trained back-propagation neural network to predict the parameter values, instead of using a reactor core simulator, which saved considerable computation time in the search process. We applied this method to find optimal LPs for five cycles of Laguna Verde Nuclear Power Plant (LVNPP) in Mexico
Clustering-neural network models for freeway work zone capacity estimation.
Jiang, Xiaomo; Adeli, Hojjat
2004-06-01
Two neural network models, called clustering-RBFNN and clustering-BPNN models, are created for estimating the work zone capacity in a freeway work zone as a function of seventeen different factors through judicious integration of the subtractive clustering approach with the radial basis function (RBF) and the backpropagation (BP) neural network models. The clustering-RBFNN model has the attractive characteristics of training stability, accuracy, and quick convergence. The results of validation indicate that the work zone capacity can be estimated by clustering-neural network models in general with an error of less than 10%, even with limited data available to train the models. The clustering-RBFNN model is used to study several main factors affecting work zone capacity. The results of such parametric studies can assist work zone engineers and highway agencies to create effective traffic management plans (TMP) for work zones quantitatively and objectively.
Spatial Data Mining Toolbox for Mapping Suitability of Landfill Sites Using Neural Networks
Abujayyab, S. K. M.; Ahamad, M. S. S.; Yahya, A. S.; Aziz, H. A.
2016-09-01
Mapping the suitability of landfill sites is a complex field and is involved with multidiscipline. The purpose of this research is to create an ArcGIS spatial data mining toolbox for mapping the suitability of landfill sites at a regional scale using neural networks. The toolbox is constructed from six sub-tools to prepare, train, and process data. The employment of the toolbox is straightforward. The multilayer perceptron (MLP) neural networks structure with a backpropagation learning algorithm is used. The dataset is mined from the north states in Malaysia. A total of 14 criteria are utilized to build the training dataset. The toolbox provides a platform for decision makers to implement neural networks for mapping the suitability of landfill sites in the ArcGIS environment. The result shows the ability of the toolbox to produce suitability maps for landfill sites.
Directory of Open Access Journals (Sweden)
Kazem Barkhordari
2015-12-01
Full Text Available This research intends to develop a method based on the Artificial Neural Network (ANN to predict permanent earthquake-induced deformation of the earth dams and embankments. For this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. In order to predict earthquake-induced deformation of the earth dams and embankments a Multi-Layer Perceptron (MLP analysis was used. A four-layer, feed-forward, back-propagation neural network, with a topology of 7-9-7-1 was found to be optimum. The results showed that an appropriately trained neural network could reliably predict permanent earthquake-induced deformation of the earth dams and embankments.
Interest Rate Forecasting with Neural Networks
Jan Täppinen
1998-01-01
This paper compares neural networks and linear regression models in interest rate forecasting using US term structure data. The expectations hypothesis gets some extra support from the neural network model as compared to the regression model. A neural network with the whole yield curve spectre from the difference between 1 and 3-month rates to the difference between 5 and 10-year rates predicts changes in interest rates quite well. However, during 1994?1995 the neural networks (as well as the...
Neural network to diagnose lining condition
Yemelyanov, V. A.; Yemelyanova, N. Y.; Nedelkin, A. A.; Zarudnaya, M. V.
2018-03-01
The paper presents data on the problem of diagnosing the lining condition at the iron and steel works. The authors describe the neural network structure and software that are designed and developed to determine the lining burnout zones. The simulation results of the proposed neural networks are presented. The authors note the low learning and classification errors of the proposed neural networks. To realize the proposed neural network, the specialized software has been developed.
Numerical experiments with neural networks
International Nuclear Information System (INIS)
Miranda, Enrique.
1990-01-01
Neural networks are highly idealized models which, in spite of their simplicity, reproduce some key features of the real brain. In this paper, they are introduced at a level adequate for an undergraduate computational physics course. Some relevant magnitudes are defined and evaluated numerically for the Hopfield model and a short term memory model. (Author)
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 2. Artificial Neural Networks A Brief Introduction. Jitendra R Raol Sunilkumar S Mankame. General Article Volume 1 Issue 2 February 1996 pp 47-54. Fulltext. Click here to view fulltext PDF. Permanent link:
Optoelectronic Implementation of Neural Networks
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 3; Issue 9. Optoelectronic Implementation of Neural Networks - Use of Optics in Computing. R Ramachandran. General Article Volume 3 Issue 9 September 1998 pp 45-55. Fulltext. Click here to view fulltext PDF. Permanent link:
Indian Academy of Sciences (India)
works. They have the ability to learn from empirical datal information. They find use in computer science and control engineering fields. In recent years artificial ... However there are vast differences between biological neural networks (BNNs) of the brain and ANN s. A thorough understanding of biologically derived NNs ...
Medical Imaging with Neural Networks
International Nuclear Information System (INIS)
Pattichis, C.; Cnstantinides, A.
1994-01-01
The objective of this paper is to provide an overview of the recent developments in the use of artificial neural networks in medical imaging. The areas of medical imaging that are covered include : ultrasound, magnetic resonance, nuclear medicine and radiological (including computerized tomography). (authors)
Hysteretic recurrent neural networks: a tool for modeling hysteretic materials and systems
International Nuclear Information System (INIS)
Veeramani, Arun S; Crews, John H; Buckner, Gregory D
2009-01-01
This paper introduces a novel recurrent neural network, the hysteretic recurrent neural network (HRNN), that is ideally suited to modeling hysteretic materials and systems. This network incorporates a hysteretic neuron consisting of conjoined sigmoid activation functions. Although similar hysteretic neurons have been explored previously, the HRNN is unique in its utilization of simple recurrence to 'self-select' relevant activation functions. Furthermore, training is facilitated by placing the network weights on the output side, allowing standard backpropagation of error training algorithms to be used. We present two- and three-phase versions of the HRNN for modeling hysteretic materials with distinct phases. These models are experimentally validated using data collected from shape memory alloys and ferromagnetic materials. The results demonstrate the HRNN's ability to accurately generalize hysteretic behavior with a relatively small number of neurons. Additional benefits lie in the network's ability to identify statistical information concerning the macroscopic material by analyzing the weights of the individual neurons
Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization
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Yudong Zhang
2011-05-01
Full Text Available This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM based texture features. Then, the features were reduced by principle component analysis (PCA. Finally, a two-hidden-layer forward neural network (NN was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO. K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP, adaptive BP (ABP, momentum BP (MBP, Particle Swarm Optimization (PSO, and Resilient back-propagation (RPROP methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s.
Simplified LQG Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...
Novel quantum inspired binary neural network algorithm
Indian Academy of Sciences (India)
In this paper, a quantum based binary neural network algorithm is proposed, named as novel quantum binary neural network algorithm (NQ-BNN). It forms a neural network structure by deciding weights and separability parameter in quantum based manner. Quantum computing concept represents solution probabilistically ...
Analysis of neural networks through base functions
van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.
Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more
Wan'e, Wu; Zuoming, Zhu
2012-01-01
A practical scheme for selecting characterization parameters of boron-based fuel-rich propellant formulation was put forward; a calculation model for primary combustion characteristics of boron-based fuel-rich propellant based on backpropagation neural network was established, validated, and then was used to predict primary combustion characteristics of boron-based fuel-rich propellant. The results show that the calculation error of burning rate is less than ± 7 . 3 %; in the formulation rang...
Computation within cultured neural networks.
DeMarse, T; Cadotte, A; Douglas, P; He, P; Trinh, V
2004-01-01
In this paper we present three related areas of research we are pursuing to study neural computation in vitro. Rat cortical neurons cultured on 60 channel multielectrode array (MEA) allow the researcher to measure from and stimulate sixty different sites across a small population of neurons grown in vitro. Using this system we can send stimulation patterns into the network and study how these living neural networks compute by measuring its outputs. Our first series of studies uses chaotic control techniques to study the dynamics and potentially control the behavior of cortical network. At the same time, we are beginning to apply a model of computation called the liquid state machine or LSM model developed by Wolfgang Maass to provide a firm mathematical framework from which to proceed with our investigations. Each of these components is integrated into a third area investigating the role of computation and feedback using a real-time sensory-motor feedback robotic flight system.
Neural network stochastic simulation applied for quantifying uncertainties
Directory of Open Access Journals (Sweden)
N Foudil-Bey
2016-09-01
Full Text Available Generally the geostatistical simulation methods are used to generate several realizations of physical properties in the sub-surface, these methods are based on the variogram analysis and limited to measures correlation between variables at two locations only. In this paper, we propose a simulation of properties based on supervised Neural network training at the existing drilling data set. The major advantage is that this method does not require a preliminary geostatistical study and takes into account several points. As a result, the geological information and the diverse geophysical data can be combined easily. To do this, we used a neural network with multi-layer perceptron architecture like feed-forward, then we used the back-propagation algorithm with conjugate gradient technique to minimize the error of the network output. The learning process can create links between different variables, this relationship can be used for interpolation of the properties on the one hand, or to generate several possible distribution of physical properties on the other hand, changing at each time and a random value of the input neurons, which was kept constant until the period of learning. This method was tested on real data to simulate multiple realizations of the density and the magnetic susceptibility in three-dimensions at the mining camp of Val d'Or, Québec (Canada.
Gap Filling of Daily Sea Levels by Artificial Neural Networks
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Lyubka Pashova
2013-06-01
Full Text Available In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN architectures - Feed-Forward Backpropagation (FFBP and recurrent Echo state network (ESN. In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5-years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real-time interpolation of missing data in the time series.
Forecasting the Number of Patients Diseases Using Backpropagation
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Rachmad Aeri
2016-01-01
Full Text Available Forecasting with various types of disease is important for health centers, because it can be used to help the health center management in conducting strategic planning and decision making. Health Care Center Torjun in Indonesiahas made estimationabout the number of patients with various types of diseases, such as Acute Respiratory Infections(ISPA, RA(Rheumatoid Arthritis, diarrhea, HT(Hypertension, Skin Allergies, Conjunctivitis, Asthma, Febrile, TB(Tuberculosis. Lung, scabies, Gastritis, typus and scarlet fever with reports the number of patients with certain diseases in the coming period and prepare the necessary needs both medical services and as well as drugs for use later. In this study, Artificial Neural Network (ANN is one model that is used to identify patterns of images of people with various kinds of diseases. Backpropagation is one of the popular models of Neural Networkwhich is used for forecasting, prediction, and decision makers based on the input of data entry that has been studied in advance. The resultsis HT (0.35 %with parameters for forecasting system using Neural Network Backpropagation is the best of the trial results that shows disease HT which are obtained from the experiments. They predict the number of patients with a disease that needs to be watched for in the coming period and prepare all the needs of both medical and medication needed to handle the number of people with the disease.
Benediktsson, J. A.; Swain, P. H.; Ersoy, O. K.
1993-01-01
Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data, but do not compare as well with statistical methods in classification of very-high-dimentional data.
Photon spectrometry utilizing neural networks
International Nuclear Information System (INIS)
Silveira, R.; Benevides, C.; Lima, F.; Vilela, E.
2015-01-01
Having in mind the time spent on the uneventful work of characterization of the radiation beams used in a ionizing radiation metrology laboratory, the Metrology Service of the Centro Regional de Ciencias Nucleares do Nordeste - CRCN-NE verified the applicability of artificial intelligence (artificial neural networks) to perform the spectrometry in photon fields. For this, was developed a multilayer neural network, as an application for the classification of patterns in energy, associated with a thermoluminescent dosimetric system (TLD-700 and TLD-600). A set of dosimeters was initially exposed to various well known medium energies, between 40 keV and 1.2 MeV, coinciding with the beams determined by ISO 4037 standard, for the dose of 10 mSv in the quantity Hp(10), on a chest phantom (ISO slab phantom) with the purpose of generating a set of training data for the neural network. Subsequently, a new set of dosimeters irradiated in unknown energies was presented to the network with the purpose to test the method. The methodology used in this work was suitable for application in the classification of energy beams, having obtained 100% of the classification performed. (authors)
Classification of brain compartments and head injury lesions by neural networks applied to MRI
International Nuclear Information System (INIS)
Kischell, E.R.; Kehtarnavaz, N.; Hillman, G.R.; Levin, H.; Lilly, M.; Kent, T.A.
1995-01-01
An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and 'unknown'. A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician's report used to train the neural network. (orig.)
Classification of brain compartments and head injury lesions by neural networks applied to MRI.
Kischell, E R; Kehtarnavaz, N; Hillman, G R; Levin, H; Lilly, M; Kent, T A
1995-10-01
An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and "unknown." A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classification of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician's report used to train the neural network.
Classification of brain compartments and head injury lesions by neural networks applied to MRI
Energy Technology Data Exchange (ETDEWEB)
Kischell, E.R. [Dept. of Electrical Engineering, Texas A and M Univ., College Station, TX (United States); Kehtarnavaz, N. [Dept. of Electrical Engineering, Texas A and M Univ., College Station, TX (United States); Hillman, G.R. [Dept. of Pharmacology, Univ. of Texas Medical Branch, Galveston, TX (United States); Levin, H. [Dept. of Neurosurgery, Univ. of Texas Medical Branch, Galveston, TX (United States); Lilly, M. [Dept. of Neurosurgery, Univ. of Texas Medical Branch, Galveston, TX (United States); Kent, T.A. [Dept. of Neurology and Psychiatry, Univ. of Texas Medical Branch, Galveston, TX (United States)
1995-10-01
An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and `unknown`. A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician`s report used to train the neural network. (orig.)
Directory of Open Access Journals (Sweden)
Suhendry Effendy
2010-12-01
Full Text Available This paper discusses the facial image recognition system using Discrete Wavelet Transform and back-propagation artificial neural network. Discrete Wavelet Transform processes the input image to obtain the essential features found on the face image. These features are then classified using an back-propagation artificial neural network for the input image to be identified. Testing the system using facial images in AT & T Database of Faces of 400 images comprising 40 facial images of individuals and web-camera catches as many as 100 images of 10 individuals. The accuracy of level of recognition on AT & T Database of Faces reaches 93.5%, while the accuracy of level of recognition on a web-camera capture images up to 96%. Testing is also done on image of AT & T Database of Faces with given noise. Apparently the noise in the image does not give meaningful effect on the level of recognition accuracy.
Peng, Jinzhu; Dubay, Rickey
2011-10-01
In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.
Study of a transient identification system using a neural network for a PWR plant
International Nuclear Information System (INIS)
Ishihara, Yoshinao; Kasai, Masao; Kambara, Masayuki; Mitsuda, Hiromichi; Kurata, Toshikazu; Shirosaki, Hidekazu
1996-01-01
This paper presents the procedure and results of a system for identifying PWR plant abnormal events, which uses neural network techniques. The neural network recognizes the abnormal event from the patterns of the transient changes of analog data from plant parameters when they deport from their normal state. For the identification of abnormal events in this study, events that cause a reactor to scram during power operation were selected as the design base events. The test data were prepared by simulating the transients on a compact PWR simulator. The simulation data were analyzed to determine how the plant parameters respond after the occurrence of a transient. A method of converting the pattern of the transient changes into characteristic parameters by fitting the data to pre-determined functions was developed. These characteristic parameters were used as the input data to the neural network. The neural network learning procedure used a generalized delta rule, namely a back-propagation algorithm. The neural network can identify the type of an abnormal event from a limited set of events by using these characteristic parameters obtained from the pattern of the changes in the analog data. From the results of this application of a neural network, it was concluded that it would be possible to use the method to identify abnormal events in a nuclear power plant
Laidi, Maamar; Hanini, Salah; Rezrazi, Ahmed; Yaiche, Mohamed Redha; El Hadj, Abdallah Abdallah; Chellali, Farouk
2017-04-01
In this study, a backpropagation artificial neural network (BP-ANN) model is used as an alternative approach to predict solar radiation on tilted surfaces (SRT) using a number of variables involved in physical process. These variables are namely the latitude of the site, mean temperature and relative humidity, Linke turbidity factor and Angstrom coefficient, extraterrestrial solar radiation, solar radiation data measured on horizontal surfaces (SRH), and solar zenith angle. Experimental solar radiation data from 13 stations spread all over Algeria around the year (2004) were used for training/validation and testing the artificial neural networks (ANNs), and one station was used to make the interpolation of the designed ANN. The ANN model was trained, validated, and tested using 60, 20, and 20 % of all data, respectively. The configuration 8-35-1 (8 inputs, 35 hidden, and 1 output neurons) presented an excellent agreement between the prediction and the experimental data during the test stage with determination coefficient of 0.99 and root meat squared error of 5.75 Wh/m2, considering a three-layer feedforward backpropagation neural network with Levenberg-Marquardt training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. This novel model could be used by researchers or scientists to design high-efficiency solar devices that are usually tilted at an optimum angle to increase the solar incident on the surface.
Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures
Directory of Open Access Journals (Sweden)
Mosbeh R. Kaloop
2015-09-01
Full Text Available The Global Positioning System (GPS is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents.
Transform preprocessing for neural networks for object recogniition and localization with sonar
Barshan, Billur; Ayrulu, Birsel
2003-04-01
We investigate the pre-processing of sonar signals prior to using neural networks for robust differentiation of commonly encountered features in indoor environments. Amplitude and time-of-flight measurement patterns acquired from a real sonar system are pre-processed using various techniques including wavelet transforms, Fourier and fractional Fourier transforms, and Kohonen's self-organizing feature map. Modular and non-modular neural network structures trained with the back-propagation and generating-shrinking algorithms are used to incorporate learning in the identification of parameter relations for target primitives. Networks trained with the generating-shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications. Neural networks can differentiate more targets, employing only a single sensor node, with a higher correct differentiation percentage than achieved with previously reported methods employing multiple sensor nodes. The success of the neural network approach shows that the sonar signals do contain sufficient information to differentiate a considerable number of target types, but the previously reported methods are unable to resolve this identifying information. This work can find application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems.
Neural Networks Methodology and Applications
Dreyfus, Gérard
2005-01-01
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented...
Directory of Open Access Journals (Sweden)
José Fernando Moretti
2016-01-01
Full Text Available Currently, artificial neural networks are being widely used in various fields of science and engineering. Neural networks have the ability to learn through experience and existing examples, and then generate solutions and answers to new problems, involving even the effects of non-linearity in their variables. The aim of this study is to use a feed-forward neural network with back-propagation technique, to predict the values of compressive strength and modulus of elasticity, at 28 days, of different concrete mixtures prepared and tested in the laboratory. It demonstrates the ability of the neural networks to quantify the strength and the elastic modulus of concrete specimens prepared using different mix proportions.
Chaotic wandering motion in connected neural networks
Ohno, Takashi; Shimizu, Toshihiro
2000-06-01
A new type of neural network is proposed. In a system, which consists of one host network and three terminal networks, the associative memory problem is investigated. In each network different patterns are stored. It is shown that each network can retrieve the patterns stored in other networks, and the terminal networks exhibit synchronized behavior. The time evolution and the mechanism of retrieval investigated. .
The LILARTI neural network system
Energy Technology Data Exchange (ETDEWEB)
Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.
1992-10-01
The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.
Stellar Image Interpretation System Using Artificial Neural Networks:
Directory of Open Access Journals (Sweden)
A. El-Bassuny Alawy
2004-01-01
Full Text Available A supervised Artificial Neural Network (ANN based system is being developed employing the Bi-polar function for identifying stellar images in CCD frames. It is based on feed-forward artificial neural networks with error back-propagation learning. It has been coded in C language. The learning process was performed on a 341 input pattern set, while a similar set was used for testing. The present approach has been applied on a CCD frame of the open star cluster M67. The results obtained have been discussed and compared with those derived in our previous work employing the Uni-polar function and by a package known in the astronomical community (DAOPHOT-II. Full agreement was found between the present approach, that of Elnagahy et al, and the standard astronomical data for the cluster. It has been shown that the developed technique resembles that of the Uni-Polar function, possessing a simple, much faster yet reliable approach. Moreover, neither prior knowledge on, nor initial data from, the frame to be analysed is required, as it is for DAOPHOT-II.
Sleep snoring detection using multi-layer neural networks.
Nguyen, Tan Loc; Won, Yonggwan
2015-01-01
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.
Parameterization Of Solar Radiation Using Neural Network
International Nuclear Information System (INIS)
Jiya, J. D.; Alfa, B.
2002-01-01
This paper presents a neural network technique for parameterization of global solar radiation. The available data from twenty-one stations is used for training the neural network and the data from other ten stations is used to validate the neural model. The neural network utilizes latitude, longitude, altitude, sunshine duration and period number to parameterize solar radiation values. The testing data was not used in the training to demonstrate the performance of the neural network in unknown stations to parameterize solar radiation. The results indicate a good agreement between the parameterized solar radiation values and actual measured values
Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods
International Nuclear Information System (INIS)
Upadhyaya, B.R.; Yan, W.
1993-11-01
The primary purpose of the current research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive examination data. Specifically, data from eddy current inspection of heat exchanger tubing were utilized to evaluate this technology. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fully-connected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture. A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of ORNL. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. A PC-based data preprocessing and display program was also developed as part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods. The integration of data compression and artificial neural networks for information processing was established as an effective technique for automation of diagnostics using nondestructive examination methods
Modification Of Learning Rate With Lvq Model Improvement In Learning Backpropagation
Tata Hardinata, Jaya; Zarlis, Muhammad; Budhiarti Nababan, Erna; Hartama, Dedy; Sembiring, Rahmat W.
2017-12-01
One type of artificial neural network is a backpropagation, This algorithm trained with the network architecture used during the training as well as providing the correct output to insert a similar but not the same with the architecture in use at training.The selection of appropriate parameters also affects the outcome, value of learning rate is one of the parameters which influence the process of training, Learning rate affects the speed of learning process on the network architecture.If the learning rate is set too large, then the algorithm will become unstable and otherwise the algorithm will converge in a very long period of time.So this study was made to determine the value of learning rate on the backpropagation algorithm. LVQ models of learning rate is one of the models used in the determination of the value of the learning rate of the algorithm LVQ.By modifying this LVQ model to be applied to the backpropagation algorithm. From the experimental results known to modify the learning rate LVQ models were applied to the backpropagation algorithm learning process becomes faster (epoch less).
Directory of Open Access Journals (Sweden)
Oliveira-Esquerre K.P.
2002-01-01
Full Text Available This work presents a way to predict the biochemical oxygen demand (BOD of the output stream of the biological wastewater treatment plant at RIPASA S/A Celulose e Papel, one of the major pulp and paper plants in Brazil. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA before they are fed to a backpropagated neural network. The influence of input variables is analyzed and satisfactory prediction results are obtained for an optimized situation.
Directory of Open Access Journals (Sweden)
Devinder Kaur
2010-12-01
to compare the selective cloning technique with the conventional GA and the back-propagation algorithm. For comparative analysis, same neural network architecture is used for both the back propagation and the genetic algorithms. The selective cloning approach is based on the schema theorem. By using selective cloning, it has been shown that GA is 27.78% more efficient than the conventional GA and 83.33% more efficient than the back propagation approach. The results of selective cloning on other data sets are also discussed.
International Nuclear Information System (INIS)
Ferreira, Francisco J.O.; Crispim, Verginia R.; Silva, Ademir X.
2009-01-01
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)
PENERAPAN ALGORITMA BAYESIAN REGULARIZATION BACKPROPAGATION UNTUK MEMPREDIKSI PENYAKIT DIABETES
Directory of Open Access Journals (Sweden)
S Suwarno
2017-03-01
Full Text Available Pada tahun 2015, penderita diabetes di Indonesia sebanyak 10 juta jiwa. Banyaknya penderita diabetes ini semakin bertambah dari tahun ke tahun. Berdasarkan data International Diabetes Federation, diperkirakan pada tahun 2040 banyaknya penduduk Indonesia yang terkena penyakit diabates akan meningkat menjadi 16.2 juta jiwa penduduk. Upaya pendeteksian sejak dini penyakit diabetes perlu dilakukan. Hal ini untuk mengurangi komplikasi penyakit pada penderita pada masa yang akan datang. Neural network merupakan salah satu metode klasifikasi yang dapat digunakan untuk memprediksi penyakit diabetes. Penelitian ini bertujuan membuat sistem prediksi penyakit diabetes. Kinerja diagnostik sistem Jaringan syaraf tiruan dievaluasi menggunakan analisis Receiver Operating Characteristic (ROC untuk mengetahui tingkat accuracy, sensitivity, dan specificity. Hasil evaluasi menunjukkan klasifikasi menggunakan sistem jaringan syaraf tiruan backpropagation masuk ke dalam kriteria good classification. Artinya, hasil klasifikasi ini dapat digunakan untuk membuat sistem prediksi penyakit diabates.As 2015, an estimated 10 million people had diabetes in Indonesia. Trends suggested the rate would continue to rise year by year. According to the latest International Diabetes Federation, people living with diabetes is expected to rise to 16.2 million by 2040. Early detection of diabetes is needed to reduce number of people living with diabetes. Neural network classification is one method that can be used to predict diabetes. This research aims to make diabetes disease prediction systems. Artificial neural network diagnostic system performance was evaluated using analysis of Receiver Operating Characteristic (ROC to determine the level of accuracy, sensitivity, and specificity. The results of the evaluation showed that the classification system using backpropagation neural network is good. The results of the classification is used to make diabetes disease prediction
Practical neural network recipies in C++
Masters
2014-01-01
This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assum
Neural network modeling of emotion
Levine, Daniel S.
2007-03-01
This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.
Prediction of geomagnetic storms from solar wind data with the use of a neural network
Directory of Open Access Journals (Sweden)
H. Lundstedt
Full Text Available An artificial feed-forward neural network with one hidden layer and error back-propagation learning is used to predict the geomagnetic activity index (D_{st} one hour in advance. The B_{z}-component and Σ_{Bz}, the density, and the velocity of the solar wind are used as input to the network. The network is trained on data covering a total of 8700 h, extracted from the 25-year period from 1963 to 1987, taken from the NSSDC data base. The performance of the network is examined with test data, not included in the training set, which covers 386 h and includes four different storms. Whilst the network predicts the initial and main phase well, the recovery phase is not modelled correctly, implying that a single hidden layer error back-propagation network is not enough, if the measured D_{st} is not available instantaneously. The performance of the network is independent of whether the raw parameters are used, or the electric field and square root of the dynamical pressure.
International Nuclear Information System (INIS)
Zio, Enrico; Pedroni, Nicola; Broggi, Matteo; Golea, Lucia Roxana
2009-01-01
In this paper, an infinite impulse response locally recurrent neural network (IIR-LRNN) is employed for modelling the dynamics of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The network is trained by recursive back-propagation (RBP) and its ability in estimating transients is tested under various conditions. The results demonstrate the robustness of the locally recurrent scheme in the reconstruction of complex nonlinear dynamic relationships
Neural Networks in Nonlinear Aircraft Control
Linse, Dennis J.
1990-01-01
Recent research indicates that artificial neural networks offer interesting learning or adaptive capabilities. The current research focuses on the potential for application of neural networks in a nonlinear aircraft control law. The current work has been to determine which networks are suitable for such an application and how they will fit into a nonlinear control law.
International Nuclear Information System (INIS)
Yang, A.-S.; Kuo, T.-C.; Ling, P.-H.
2003-01-01
The phase transport phenomenon of the high-pressure two-phase turbulent bubbly flow involves complicated interfacial interactions of the mass, momentum, and energy transfer processes between phases, revealing that an enormous effort is required in characterizing the liquid-gas flow behavior. Nonetheless, the instantaneous information of bubbly flow properties is often desired for many industrial applications. This investigation aims to demonstrate the successful use of neural networks in the real-time determination of two-phase flow properties at elevated pressures. Three back-propagation neural networks, trained with the simulation results of a comprehensive theoretical model, are established to predict the transport characteristics (specifically the distributions of void-fraction and axial liquid-gas velocities) of upward turbulent bubbly pipe flows at pressures covering 3.5-7.0 MPa. Comparisons of the predictions with the test target vectors indicate that the averaged root-mean-squared (RMS) error for each one of three back-propagation neural networks is within 4.59%. In addition, this study appraises the effects of different network parameters, including the number of hidden nodes, the type of transfer function, the number of training pairs, the learning rate-increasing ratio, the learning rate-decreasing ratio, and the momentum value, on the training quality of neural networks.
Directory of Open Access Journals (Sweden)
Jesús Salvador Velázquez-González
2015-01-01
Full Text Available Una de las complicaciones más graves de la Diabetes Mellitus tipo 2 es la Retinopatía Diabética (RD. La RD es una enfermedad silenciosa y solo es reconocida por el portador cuándo los cambios en la retina han progresado a un nivel en el cual el tratamiento se complica, por lo que el diagnóstico oportuno y la remisión al oftalmólogo u optometrista para el manejo de esta enfermedad pueden prevenir el 98% de la pérdida visual grave. El objetivo de este trabajo es identificar de manera automática la No Retinopatía Diabética (NRD y la Retinopatía de Fondo, utilizando imágenes del fondo de ojo. Nuestros resultados muestran una efectividad del 92%, con una sensitividad y especificidad del 95%.
2011-03-01
are made of three different meats (20 Beef, 17 “ Meat ”, & 17 Poultry ). The data set containing the following features: • $/oz • $/lb protein...structure in minimal training time. Data sets used include an XOR data set, Fisher’s iris data set, and a financial industry data set, among others. v...4.2.3. Finance Industry Data Set ...................................................................................................... 38 4.2.4. Hot
Vehicles Potholes Detection Based Blob Detection Method and Neural Network Backpropagation Model
Dewiani, Dewiani; Achmad, Andani; Parung, Rivanto
2016-01-01
In Indonesia, especially on developing area, many potholes are occurred almost on every part of the road. The situation is exacerbated on how potholes location data gathering is performed manually by field personnel of the Department of Transportation or other related services, which would require more time and cost. This study aimed to produce a prototype of detection system and potholes location automatically. The prototype is a device attached on public transport so that it can be a soluti...
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
Oil reservoir properties estimation using neural networks
Energy Technology Data Exchange (ETDEWEB)
Toomarian, N.B. [California Inst. of Tech., Pasadena, CA (United States); Barhen, J.; Glover, C.W. [Oak Ridge National Lab., TN (United States). Center for Engineering Systems Advanced Research; Aminzadeh, F. [UNOCAL Corp., Sugarland, TX (United States)
1997-02-01
This paper investigates the applicability as well as the accuracy of artificial neural networks for estimating specific parameters that describe reservoir properties based on seismic data. This approach relies on JPL`s adjoint operators general purpose neural network code to determine the best suited architecture. The authors believe that results presented in this work demonstrate that artificial neural networks produce surprisingly accurate estimates of the reservoir parameters.
Application of Artificial Neural Networks in Canola Crop Yield Prediction
Directory of Open Access Journals (Sweden)
S. J. Sajadi
2014-02-01
Full Text Available Crop yield prediction has an important role in agricultural policies such as specification of the crop price. Crop yield prediction researches have been based on regression analysis. In this research canola yield was predicted using Artificial Neural Networks (ANN using 11 crop year climate data (1998-2009 in Gonbad-e-Kavoos region of Golestan province. ANN inputs were mean weekly rainfall, mean weekly temperature, mean weekly relative humidity and mean weekly sun shine hours and ANN output was canola yield (kg/ha. Multi-Layer Perceptron networks (MLP with Levenberg-Marquardt backpropagation learning algorithm was used for crop yield prediction and Root Mean Square Error (RMSE and square of the Correlation Coefficient (R2 criterions were used to evaluate the performance of the ANN. The obtained results show that the 13-20-1 network has the lowest RMSE equal to 101.235 and maximum value of R2 equal to 0.997 and is suitable for predicting canola yield with climate factors.
Artificial neural networks (ANN: prediction of sensory measurements from instrumental data
Directory of Open Access Journals (Sweden)
Naiara Barbosa Carvalho
2013-12-01
Full Text Available The objective of this study was to predict by means of Artificial Neural Network (ANN, multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters. Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.
Fuzzy neural network theory and application
Liu, Puyin
2004-01-01
This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he
Artificial Neural Networks and Concentration Residual Augmented ...
African Journals Online (AJOL)
Artificial Neural Networks and Concentration Residual Augmented Classical Least Squares for the Simultaneous Determination of Diphenhydramine, Benzonatate, Guaifenesin and Phenylephrine in their Quaternary Mixture.
Boolean Factor Analysis by Attractor Neural Network
Czech Academy of Sciences Publication Activity Database
Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.
2007-01-01
Roč. 18, č. 3 (2007), s. 698-707 ISSN 1045-9227 R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Institutional research plan: CEZ:AV0Z10300504 Keywords : recurrent neural network * Hopfield-like neural network * associative memory * unsupervised learning * neural network architecture * neural network application * statistics * Boolean factor analysis * dimensionality reduction * features clustering * concepts search * information retrieval Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.769, year: 2007
Neural network based system for equipment surveillance
Vilim, R.B.; Gross, K.C.; Wegerich, S.W.
1998-04-28
A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.
Aphasia Classification Using Neural Networks
DEFF Research Database (Denmark)
Axer, H.; Jantzen, Jan; Berks, G.
2000-01-01
A web-based software model (http://fuzzy.iau.dtu.dk/aphasia.nsf) was developed as an example for classification of aphasia using neural networks. Two multilayer perceptrons were used to classify the type of aphasia (Broca, Wernicke, anomic, global) according to the results in some subtests...... 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...
Finite connectivity attractor neural networks
International Nuclear Information System (INIS)
Wemmenhove, B; Coolen, A C C
2003-01-01
We study a family of diluted attractor neural networks with a finite average number of (symmetric) connections per neuron. As in finite connectivity spin glasses, their equilibrium properties are described by order parameter functions, for which we derive an integral equation in replica symmetric approximation. A bifurcation analysis of this equation reveals the locations of the paramagnetic to recall and paramagnetic to spin-glass transition lines in the phase diagram. The line separating the retrieval phase from the spin-glass phase is calculated at zero temperature. All phase transitions are found to be continuous
Finite connectivity attractor neural networks
Wemmenhove, B.; Coolen, A. C. C.
2003-09-01
We study a family of diluted attractor neural networks with a finite average number of (symmetric) connections per neuron. As in finite connectivity spin glasses, their equilibrium properties are described by order parameter functions, for which we derive an integral equation in replica symmetric approximation. A bifurcation analysis of this equation reveals the locations of the paramagnetic to recall and paramagnetic to spin-glass transition lines in the phase diagram. The line separating the retrieval phase from the spin-glass phase is calculated at zero temperature. All phase transitions are found to be continuous.
Directory of Open Access Journals (Sweden)
Rohmatulloh 1
2007-12-01
Full Text Available This paper discussed quality improvement of black tea using fuzzy approach on quality functions deployment and the development of backpropagation neural the software NWP II plus. The research was conducted at PTPN VIII tea industry, Goalpara plantation. Result of the study showed that, parameter first priority based on customer evaluation was tea flavour. The Important process parameter of black tea based on result of fuzzy relationship matrix was the withering process. Based on the test of “trial and error” of network training process, the best network architecture for withering process monitoring [3-15-1] was obtained, that is 3 neurons in input layer, 15 neurons in hidden layer and 1 neuron in output layer. Three inputs and output consist of time, flow, temperature and moisture content. The result sugges that development of backpropagation neural network can be used for process evaluation of withering processes.
Directory of Open Access Journals (Sweden)
Yasir Hassan Ali
2015-01-01
Full Text Available The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (λ. The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.
Directory of Open Access Journals (Sweden)
A. K. CHOWDHURY
2016-02-01
Full Text Available In this paper an evolutionary technique for synthesizing Multi-Valued Logic (MVL functions using Neural Network Deployment Algorithm (NNDA is presented. The algorithm is combined with back-propagation learning capability and neural MVL operators. This research article is done to observe the anomalistic characteristics of MVL neural operators and their role in synthesis. The advantages of NNDA-MVL algorithm is demonstrated with realization of synthesized many valued functions with lesser MVL operators. The characteristic feature set consists of MVL gate count, network link count, network propagation delay and accuracy achieved in training. In brief, this paper depicts an effort of reduced network size for synthesized MVL functions. Trained MVL operators improve the basic architecture by reducing MIN gate and interlink connection by 52.94% and 23.38% respectively.
The Laplacian spectrum of neural networks
de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.
2014-01-01
The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286
Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong
2013-11-01
In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.
Tseng, Y H; Hwang, J N; Sheehan, F H
1997-01-01
3D object recognition under partial object viewing is a difficult pattern recognition task. In this paper, we introduce a neural-network solution that is robust to partial viewing of objects and noise corruption. This method directly utilizes the acquired 3D data and requires no feature extraction. The object is first parametrically represented by a continuous distance transform neural network (CDTNN) trained by the surface points of the exemplar object. The CDTNN maps any 3D coordinate into a value that corresponds to the distance between the point and the nearest surface point of the object. Therefore, a mismatch between the exemplar object and an unknown object can be easily computed. When encountered with deformed objects, this mismatch information can be backpropagated through the CDTNN to iteratively determine the deformation in terms of affine transform. Application to 3D heart contour delineation and invariant recognition of 3D rigid-body objects is presented.
Mazurowski, Maciej A; Habas, Piotr A; Zurada, Jacek M; Lo, Joseph Y; Baker, Jay A; Tourassi, Georgia D
2008-01-01
This study investigates the effect of class imbalance in training data when developing neural network classifiers for computer-aided medical diagnosis. The investigation is performed in the presence of other characteristics that are typical among medical data, namely small training sample size, large number of features, and correlations between features. Two methods of neural network training are explored: classical backpropagation (BP) and particle swarm optimization (PSO) with clinically relevant training criteria. An experimental study is performed using simulated data and the conclusions are further validated on real clinical data for breast cancer diagnosis. The results show that classifier performance deteriorates with even modest class imbalance in the training data. Further, it is shown that BP is generally preferable over PSO for imbalanced training data especially with small data sample and large number of features. Finally, it is shown that there is no clear preference between oversampling and no compensation approach and some guidance is provided regarding a proper selection.
Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting
Directory of Open Access Journals (Sweden)
Jianjin Wang
2017-01-01
Full Text Available Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper and knowledge-based method (traditional hydrological model may booster simulation accuracy. In this study, we proposed a new back-propagation (BP neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.
Smith, James A.
1992-01-01
The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI 1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.
Neural networks with discontinuous/impact activations
Akhmet, Marat
2014-01-01
This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...
Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems
Innocenti, M.; Napolitano, M.
2003-01-01
Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the use of learning algorithms more efficient than the classic Back-Propagation, and through the implementation of the neural schemes in parallel hardware. The results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model are presented. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. In addition, alternative methods for communications links fault detection and accomodation are presented, relative to multiple unmanned aircraft applications.
Drift chamber tracking with neural networks
International Nuclear Information System (INIS)
Lindsey, C.S.; Denby, B.; Haggerty, H.
1992-10-01
We discuss drift chamber tracking with a commercial log VLSI neural network chip. Voltages proportional to the drift times in a 4-layer drift chamber were presented to the Intel ETANN chip. The network was trained to provide the intercept and slope of straight tracks traversing the chamber. The outputs were recorded and later compared off line to conventional track fits. Two types of network architectures were studied. Applications of neural network tracking to high energy physics detector triggers is discussed
A review and analysis of neural networks for classification of remotely sensed multispectral imagery
Paola, Justin D.; Schowengerdt, Robert A.
1993-01-01
A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.
Molecular evolution of a peptide GPCR ligand driven by artificial neural networks.
Directory of Open Access Journals (Sweden)
Sebastian Bandholtz
Full Text Available Peptide ligands of G protein-coupled receptors constitute valuable natural lead structures for the development of highly selective drugs and high-affinity tools to probe ligand-receptor interaction. Currently, pharmacological and metabolic modification of natural peptides involves either an iterative trial-and-error process based on structure-activity relationships or screening of peptide libraries that contain many structural variants of the native molecule. Here, we present a novel neural network architecture for the improvement of metabolic stability without loss of bioactivity. In this approach the peptide sequence determines the topology of the neural network and each cell corresponds one-to-one to a single amino acid of the peptide chain. Using a training set, the learning algorithm calculated weights for each cell. The resulting network calculated the fitness function in a genetic algorithm to explore the virtual space of all possible peptides. The network training was based on gradient descent techniques which rely on the efficient calculation of the gradient by back-propagation. After three consecutive cycles of sequence design by the neural network, peptide synthesis and bioassay this new approach yielded a ligand with 70fold higher metabolic stability compared to the wild type peptide without loss of the subnanomolar activity in the biological assay. Combining specialized neural networks with an exploration of the combinatorial amino acid sequence space by genetic algorithms represents a novel rational strategy for peptide design and optimization.
An alternative approach for adaptive real-time control using a nonparametric neural network
Energy Technology Data Exchange (ETDEWEB)
Alves da Silva, A.P.; Nascimento, P.C.; Lambert-Torres, G.; Borges da Silva, L.E. [Escola Federal de Engenharia de Itajuba, Minas Gerais (Brazil)
1995-12-31
This paper presents a nonparametric Artificial Neural Network (ANN) model for adaptive control of nonlinear systems. The proposed ANN, Functional Polynomial Network (FPN), mixes the concept of orthogonal basis functions with the idea of polynomial networks. A combination of orthogonal functions can be used to produce a desired mapping. However, there is no way besides trial and error to choose which orthogonal functions should be selected. Polynomial nets can be used for function approximation, but, it is not easy to set the order of the activation function. The combination of the two concepts produces a very powerful ANN model due to the automatic input selection capability of the polynomial networks. The proposed FPN has been tested for speed control of a DC motor. The results have been compared with the ones provided by an indirect adaptive control scheme based on multilayer perceptrons trained by backpropagation.
Toward Content Based Image Retrieval with Deep Convolutional Neural Networks.
Sklan, Judah E S; Plassard, Andrew J; Fabbri, Daniel; Landman, Bennett A
2015-03-19
Content-based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and eventually, improve patient care. Recent advances in database capacity, algorithm efficiency, and deep Convolutional Neural Networks (dCNN), a machine learning technique, have enabled great CBIR success for general photographic images. Here, we investigate applying the leading ImageNet CBIR technique to clinically acquired medical images captured by the Vanderbilt Medical Center. Briefly, we (1) constructed a dCNN with four hidden layers, reducing dimensionality of an input scaled to 128×128 to an output encoded layer of 4×384, (2) trained the network using back-propagation 1 million random magnetic resonance (MR) and computed tomography (CT) images, (3) labeled an independent set of 2100 images, and (4) evaluated classifiers on the projection of the labeled images into manifold space. Quantitative results were disappointing (averaging a true positive rate of only 20%); however, the data suggest that improvements would be possible with more evenly distributed sampling across labels and potential re-grouping of label structures. This prelimainry effort at automated classification of medical images with ImageNet is promising, but shows that more work is needed beyond direct adaptation of existing techniques.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.
Hidden neural networks: application to speech recognition
DEFF Research Database (Denmark)
Riis, Søren Kamaric
1998-01-01
We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...
Neural Networks for Non-linear Control
DEFF Research Database (Denmark)
Sørensen, O.
1994-01-01
This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....
Neural Network Algorithm for Particle Loading
International Nuclear Information System (INIS)
Lewandowski, J.L.V.
2003-01-01
An artificial neural network algorithm for continuous minimization is developed and applied to the case of numerical particle loading. It is shown that higher-order moments of the probability distribution function can be efficiently renormalized using this technique. A general neural network for the renormalization of an arbitrary number of moments is given
Neural Network to Solve Concave Games
Liu, Zixin; Wang, Nengfa
2014-01-01
The issue on neural network method to solve concave games is concerned. Combined with variational inequality, Ky Fan inequality, and projection equation, concave games are transformed into a neural network model. On the basis of the Lyapunov stable theory, some stability results are also given. Finally, two classic games’ simulation results are given to illustrate the theoretical results.
Neural network classification - A Bayesian interpretation
Wan, Eric A.
1990-01-01
The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical framework.
Neural Network Classifier Based on Growing Hyperspheres
Czech Academy of Sciences Publication Activity Database
Jiřina Jr., Marcel; Jiřina, Marcel
2000-01-01
Roč. 10, č. 3 (2000), s. 417-428 ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant - others:MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics
Adaptive Neurons For Artificial Neural Networks
Tawel, Raoul
1990-01-01
Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.
Recognizing changing seasonal patterns using neural networks
Ph.H.B.F. Franses (Philip Hans); G. Draisma (Gerrit)
1997-01-01
textabstractIn this paper we propose a graphical method based on an artificial neural network model to investigate how and when seasonal patterns in macroeconomic time series change over time. Neural networks are useful since the hidden layer units may become activated only in certain seasons or
A Survey of Neural Network Publications.
Vijayaraman, Bindiganavale S.; Osyk, Barbara
This paper is a survey of publications on artificial neural networks published in business journals for the period ending July 1996. Its purpose is to identify and analyze trends in neural network research during that period. This paper shows which topics have been heavily researched, when these topics were researched, and how that research has…
Some Examples of Identification with Neural Networks
Sjöberg, Jonas
1994-01-01
In this report some examples on system identification of non-linear systems with neural networks are presented. The systems being identified all have different kinds of non-linearities, more or less known. The examples in this paper show that these non-linearities can be successfully modeled by non-linear models based on neural networks.
Neural network monitoring of resistance welding processes
Quero Reboul, José Manuel; Millán Vázquez de la Torre, Rafael Luis; García Franquelo, Leopoldo; Cañas, J.
1994-01-01
Control of weld quality is one of the most important and complex processes to be carried out on production lines. Neural networks have shown good results in fields such as modelling and control of physical processes. It is suggested in this article that a neural classifier should be used to carry out non‐destructive on‐line analysis. This system has been developed and installed at resistance welding stations. Results confirm the validity of neural networks used for this type of application.
Introduction to Concepts in Artificial Neural Networks
Niebur, Dagmar
1995-01-01
This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.
Energy Technology Data Exchange (ETDEWEB)
Junghui Chen; Kuan-Po Wang [Chung-Yuan Christian University (China). Dept. of Chemical Engineering; Ming-Tsai Liang [I-Shou University (China). Dept. of Chemical Engineering
2005-06-01
An overlapped type of local neural network is proposed to improve accuracy of the heat transfer coefficient estimation of the supercritical carbon dioxide. The idea of this work is to use the network to estimate the heat transfer coefficient for which there is no accurate correlation model due to the complexity of the thermo-physical properties involved around the critical region. Unlike the global approximation network (e.g. backpropagation network) and the local approximation network (e.g. the radial basis function network), the proposed network allows us to match the quick changes in the near-critical local region where the rate of heat transfer is significantly increased and to construct the global smooth perspective far away from that local region. Based on the experimental data for carbon dioxide flowing inside a heated tube at the supercritical condition, the proposed network significantly outperformed some the conventional correlation method and the traditional network models. (Author)
Firearm Classification using Neural Networks on Ring of Firing Pin Impression Images
Directory of Open Access Journals (Sweden)
Abdul AZIZ JEMAIN
2013-07-01
Full Text Available This paper implements two layer neural networks with different feedforward backpropagation algorithms for better performance of firearm classification us-ing numerical features from the ring image. A total of 747 ring images which are extracted from centre of the firing pin impression have been captured from five different pistols of the Parabellum Vector SPI 9mm model. Then, based on finding from the previous studies, the six best geometric moments numerical fea-tures were extracted from those ring images. The elements of the dataset were further randomly divided into the training set (523 elements, testing set (112 el-ements and validation set (112 elements in accordance with the requirement of the supervised learning nature of the backpropagation neural network (BPNN. Empirical results show that a two layer BPNN with a 6-7-5 configura-tion and tansig/tansig transfer functions with ‘trainscg’ training algorithm has produced the best classification result of 98%. The classification result is an improvement compared to the previous studies as well as confirming that the ring image region contains useful information for firearm classification.
Firearm Classification using Neural Networks on Ring of Firing Pin Impression Images
Directory of Open Access Journals (Sweden)
SAADI Bin Ahmad KAMARUDDIN
2012-12-01
Full Text Available This paper implements two layer neural networks with different feedforward backpropagation algorithms for better performance of firearm classification us-ing numerical features from the ring image. A total of 747 ring images which are extracted from centre of the firing pin impression have been captured from five different pistols of the Parabellum Vector SPI 9mm model. Then, based on finding from the previous studies, the six best geometric moments numerical fea-tures were extracted from those ring images. The elements of the dataset were further randomly divided into the training set (523 elements, testing set (112 el-ements and validation set (112 elements in accordance with the requirement of the supervised learning nature of the backpropagation neural network (BPNN. Empirical results show that a two layer BPNN with a 6-7-5 configura-tion and tansig/tansig transfer functions with ‘trainscg’ training algorithm has produced the best classification result of 98%. The classification result is an improvement compared to the previous studies as well as confirming that the ring image region contains useful information for firearm classification.
Directory of Open Access Journals (Sweden)
Bashar Tarawneh
2017-01-01
Full Text Available Standard Penetration Test (SPT and Cone Penetration Test (CPT are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design. Numerous soil parameters are related to the SPT N-value. In contrast, CPT is becoming more popular for site investigation and geotechnical design. Correlation of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values. A back-propagation artificial neural network (ANN model was developed to predict the N60-value from CPT data. Data used in this study consisted of 109 CPT-SPT pairs for sand, sandy silt, and silty sand soils. The ANN model input variables are: CPT tip resistance (qc, effective vertical stress (σv′, and CPT sleeve friction (fs. A different set of SPT-CPT data was used to check the reliability of the developed ANN model. It was shown that ANN model either under-predicted the N60-value by 7–16% or over-predicted it by 7–20%. It is concluded that back-propagation neural networks is a good tool to predict N60-value from CPT data with acceptable accuracy.
Earthquake-induced landslide-susceptibility mapping using an artificial neural network
Directory of Open Access Journals (Sweden)
S. Lee
2006-01-01
Full Text Available The purpose of this study was to apply and verify landslide-susceptibility analysis techniques using an artificial neural network and a Geographic Information System (GIS applied to Baguio City, Philippines. The 16 July 1990 earthquake-induced landslides were studied. Landslide locations were identified from interpretation of aerial photographs and field survey, and a spatial database was constructed from topographic maps, geology, land cover and terrain mapping units. Factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from faults were derived from the geology database. Land cover was identified from the topographic database. Terrain map units were interpreted from aerial photographs. These factors were used with an artificial neural network to analyze landslide susceptibility. Each factor weight was determined by a back-propagation exercise. Landslide-susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from GIS data. The susceptibility map was compared with known landslide locations and verified. The demonstrated prediction accuracy was 93.20%.
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...
Neural Based Orthogonal Data Fitting The EXIN Neural Networks
Cirrincione, Giansalvo
2008-01-01
Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh
Recurrent neural network based hybrid model for reconstructing gene regulatory network.
Raza, Khalid; Alam, Mansaf
2016-10-01
One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.
On-line validation of feedwater flow rate in nuclear power plants using neural networks
International Nuclear Information System (INIS)
Khadem, M.; Ipakchi, A.; Alexandro, F.J.; Colley, R.W.
1994-01-01
On-line calibration of feedwater flow rate measurement in nuclear power plants provides a continuous realistic value of feedwater flow rate. It also reduces the manpower required for periodic calibration needed due to the fouling and defouling of the venturi meter surface condition. This paper presents a method for on-line validation of feedwater flow rate in nuclear power plants. The method is an improvement of the previously developed method which is based on the use of a set of process variables dynamically related to the feedwater flow rate. The online measurements of this set of variables are used as inputs to a neural network to obtain an estimate of the feedwater flow rate reading. The difference between the on-line feedwater flow rate reading, and the neural network estimate establishes whether there is a need to apply a correction factor to the feedwater flow rate measurement for calculation of the actual reactor power. The method was applied to the feedwater flow meters in the two feedwater flow loops of the TMI-1 nuclear power plant. The venturi meters used for flow measurements are susceptible to frequent fouling that degrades their measurement accuracy. The fouling effects can cause an inaccuracy of up to 3% relative error in feedwater flow rate reading. A neural network, whose inputs were the readings of a set of reference instruments, was designed to predict both feedwater flow rates simultaneously. A multi-layer feedforward neural network employing the backpropagation algorithm was used. A number of neural network training tests were performed to obtain an optimum filtering technique of the input/output data of the neural networks. The result of the selection of the filtering technique was confirmed by numerous Fast Fourier Transform (FFT) tests. Training and testing were done on data from TMI-1 nuclear power plant. The results show that the neural network can predict the correct flow rates with an absolute relative error of less than 2%
Clustering: a neural network approach.
Du, K-L
2010-01-01
Clustering is a fundamental data analysis method. It is widely used for pattern recognition, feature extraction, vector quantization (VQ), image segmentation, function approximation, and data mining. As an unsupervised classification technique, clustering identifies some inherent structures present in a set of objects based on a similarity measure. Clustering methods can be based on statistical model identification (McLachlan & Basford, 1988) or competitive learning. In this paper, we give a comprehensive overview of competitive learning based clustering methods. Importance is attached to a number of competitive learning based clustering neural networks such as the self-organizing map (SOM), the learning vector quantization (LVQ), the neural gas, and the ART model, and clustering algorithms such as the C-means, mountain/subtractive clustering, and fuzzy C-means (FCM) algorithms. Associated topics such as the under-utilization problem, fuzzy clustering, robust clustering, clustering based on non-Euclidean distance measures, supervised clustering, hierarchical clustering as well as cluster validity are also described. Two examples are given to demonstrate the use of the clustering methods.
Application of neural networks in coastal engineering
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.
/plain; charset=UTF-8 ~lffE~fSTE?rS'ponsoredShort-Term Training Programme on EtiStal Erosion Areas (CEA) - Protection & Management ~_.Jai_ .........~••_ •. 06 - 18, January, 2003 II LECTURE VOLUME II Dr. A. Vittal Hegde Co-ordinator Dr. Subba Rao Co...Clion & Management " 6-18, Jal1uory 2003 at NlTK. Suralhkal 227 NEURAL·NETWORK The word 'Neural Network' is used to normally describe the "Artificial Neural Network"(ANN). Biological neural networks are much more complicated in their elementary structures than...
Neural Networks in Control Applications
DEFF Research Database (Denmark)
Sørensen, O.
concerning canonical, observable state space forms (minimum realizable form) for SISO as wll as MIMO processes. The tests show that all models, after succeeeful training, which is judged by correlation analysis of the prediction errors, are able to perform non-linear system identification, prediction......, simulation and filtering of dynamic, non-linear, multi-variable and noisy processes in a very satisfactory manner. The further examinations mainly concentrate on two models, the Non-linear ARMAX (NARMAX) model representing input/output description, and the Non-linear Innovation state Space (NISS) model (a...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...
Neural Network with Local Memory for Nuclear Reactor Power Level Control
International Nuclear Information System (INIS)
Uluyol, Oender; Ragheb, Magdi; Tsoukalas, Lefteri
2001-01-01
A methodology is introduced for a neural network with local memory called a multilayered local output gamma feedback (LOGF) neural network within the paradigm of locally-recurrent globally-feedforward neural networks. It appears to be well-suited for the identification, prediction, and control tasks in highly dynamic systems; it allows for the presentation of different timescales through incorporation of a gamma memory. A learning algorithm based on the backpropagation-through-time approach is derived. The spatial and temporal weights of the network are iteratively optimized for a given problem using the derived learning algorithm. As a demonstration of the methodology, it is applied to the task of power level control of a nuclear reactor at different fuel cycle conditions. The results demonstrate that the LOGF neural network controller outperforms the classical as well as the state feedback-assisted classical controllers for reactor power level control by showing a better tracking of the demand power, improving the fuel and exit temperature responses, and by performing robustly in different fuel cycle and power level conditions
Artificial neural networks in neurosurgery.
Azimi, Parisa; Mohammadi, Hasan Reza; Benzel, Edward C; Shahzadi, Sohrab; Azhari, Shirzad; Montazeri, Ali
2015-03-01
Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literature review of all full publications in English biomedical journals (1993-2013) was undertaken. The strategy included a combination of key words 'artificial neural networks', 'prognostic', 'brain', 'tumor tracking', 'head', 'tumor', 'spine', 'classification' and 'back pain' in the title and abstract of the manuscripts using the PubMed search engine. The major findings are summarized, with a focus on the application of ANNs for diagnostic and prognostic purposes. Finally, the future of ANNs in neurosurgery is explored. A total of 1093 citations were identified and screened. In all, 57 citations were found to be relevant. Of these, 50 articles were eligible for inclusion in this review. The synthesis of the data showed several applications of ANN in neurosurgery, including: (1) diagnosis and assessment of disease progression in low back pain, brain tumours and primary epilepsy; (2) enhancing clinically relevant information extraction from radiographic images, intracranial pressure processing, low back pain and real-time tumour tracking; (3) outcome prediction in epilepsy, brain metastases, lumbar spinal stenosis, lumbar disc herniation, childhood hydrocephalus, trauma mortality, and the occurrence of symptomatic cerebral vasospasm in patients with aneurysmal subarachnoid haemorrhage; (4) the use in the biomechanical assessments of spinal disease. ANNs can be effectively employed for diagnosis, prognosis and outcome prediction in neurosurgery. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Evolving Resilient Back-Propagation Algorithm for Energy Efficiency Problem
Directory of Open Access Journals (Sweden)
Yang Fei
2016-01-01
Full Text Available Energy efficiency is one of our most economical sources of new energy. When it comes to efficient building design, the computation of the heating load (HL and cooling load (CL is required to determine the specifications of the heating and cooling equipment. The objective of this paper is to model heating load and cooling load buildings using neural networks in order to predict HL load and CL load. Rprop with genetic algorithm was proposed to increase the global convergence capability of Rprop by modifying a corresponding weight. Comparison results show that Rprop with GA can successfully improve the global convergence capability of Rprop and achieve lower MSE than other perceptron training algorithms, such as Back-Propagation or original Rprop. In addition, the trained network has better generalization ability and stabilization performance.
Artificial neural network approach for estimation of surface specific ...
Indian Academy of Sciences (India)
R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22
mine it from space is an active research topic. Few attempts (Jourdan and .... Multi-layer networks use a variety of learn- ing techniques, the most popular being .... a learning rate of 0.7. Thus the back-propagation formula computes the delta error from the output layer back towards the input layer, in a layer-by- layer manner.
Comparative performance of some popular artificial neural network ...
Indian Academy of Sciences (India)
merits such as self-learning, self-adapting, good robustness and capability of deal- ing with non-linear problems. .... such as the standard backpropagation, resilient, scale and self-conjugate, higher- order network functions ... This was done by monitoring the RMS error while training the ANN. The RMS error employed here ...
A Newton-type neural network learning algorithm
International Nuclear Information System (INIS)
Ivanov, V.V.; Puzynin, I.V.; Purehvdorzh, B.
1993-01-01
First- and second-order learning methods for feed-forward multilayer networks are considered. A Newton-type algorithm is proposed and compared with the common back-propagation algorithm. It is shown that the proposed algorithm provides better learning quality. Some recommendations for their usage are given. 11 refs.; 1 fig.; 1 tab
Local Dynamics in Trained Recurrent Neural Networks
Rivkind, Alexander; Barak, Omri
2017-06-01
Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.
Local Dynamics in Trained Recurrent Neural Networks.
Rivkind, Alexander; Barak, Omri
2017-06-23
Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.
Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.
2000-01-01
Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.
Neural networks and orbit control in accelerators
International Nuclear Information System (INIS)
Bozoki, E.; Friedman, A.
1994-01-01
An overview of the architecture, workings and training of Neural Networks is given. We stress the aspects which are important for the use of Neural Networks for orbit control in accelerators and storage rings, especially its ability to cope with the nonlinear behavior of the orbit response to 'kicks' and the slow drift in the orbit response during long-term operation. Results obtained for the two NSLS storage rings with several network architectures and various training methods for each architecture are given
VLSI Cells Placement Using the Neural Networks
International Nuclear Information System (INIS)
Azizi, Hacene; Zouaoui, Lamri; Mokhnache, Salah
2008-01-01
The artificial neural networks have been studied for several years. Their effectiveness makes it possible to expect high performances. The privileged fields of these techniques remain the recognition and classification. Various applications of optimization are also studied under the angle of the artificial neural networks. They make it possible to apply distributed heuristic algorithms. In this article, a solution to placement problem of the various cells at the time of the realization of an integrated circuit is proposed by using the KOHONEN network
Robust Planning and Control Using Neural Networks
1990-06-30
hyperspace . We have been investigating CMAC neural networks with tapered, rather than rectangular, receptive fields. Such networks promise better (continuous...CMOS Logic Cell Arrays.’ UNH Intelligent Structures Group Report ECE.IS.90.01, Feb. 6,1990. Miller, W. T., Box, B. A., Whitney, E. C., and Glynn, J...M., ’Design and Implementation of a High Speed CMAC Neural Network Using Logic Programmable CMOS Logic Cell Arrays." To be presented at the Naval
Pattern recognition using chaotic neural networks
Tan, Z.; Hepburn, B. S.; Tucker, C.; Ali, M. K.
1998-01-01
Pattern recognition by chaotic neural networks is studied using a hyperchaotic neural network as model. Virtual basins of attraction are introduced around unstable periodic orbits which are then used as patterns. Search for periodic orbits in dynamical systems is treated as a process of pattern recognition. The role of synapses on patterns in chaotic networks is discussed. It is shown that distorted states having only limited information of the patterns are successfully recognized.
Pattern recognition using chaotic neural networks
Directory of Open Access Journals (Sweden)
Z. Tan
1998-01-01
Full Text Available Pattern recognition by chaotic neural networks is studied using a hyperchaotic neural network as model. Virtual basins of attraction are introduced around unstable periodic orbits which are then used as patterns. Search for periodic orbits in dynamical systems is treated as a process of pattern recognition. The role of synapses on patterns in chaotic networks is discussed. It is shown that distorted states having only limited information of the patterns are successfully recognized.
Structured neural networks for pattern recognition.
Murino, V
1998-01-01
This paper proposes a novel approach for the design of structures of neural networks for pattern recognition. The basic idea lies in subdividing the whole classification problem in smaller and simpler problems at different levels, each managed by appropriate components of a complex neural architecture. Three neural structures are presented and applied in a surveillance system aimed at monitoring a railway waiting room classifying potential dangerous situations. Each architecture is composed by nodes, which are actual multilayer perceptrons trained to discriminate between subsets of classes until a complete separation among the classes is achieved. This approach showed better performances with respect to a classical statistical classification procedures and to a single neural network.
Modular representation of layered neural networks.
Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio
2018-01-01
Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.
Learning and optimization with cascaded VLSI neural network building-block chips
Duong, T.; Eberhardt, S. P.; Tran, M.; Daud, T.; Thakoor, A. P.
1992-01-01
To demonstrate the versatility of the building-block approach, two neural network applications were implemented on cascaded analog VLSI chips. Weights were implemented using 7-b multiplying digital-to-analog converter (MDAC) synapse circuits, with 31 x 32 and 32 x 32 synapses per chip. A novel learning algorithm compatible with analog VLSI was applied to the two-input parity problem. The algorithm combines dynamically evolving architecture with limited gradient-descent backpropagation for efficient and versatile supervised learning. To implement the learning algorithm in hardware, synapse circuits were paralleled for additional quantization levels. The hardware-in-the-loop learning system allocated 2-5 hidden neurons for parity problems. Also, a 7 x 7 assignment problem was mapped onto a cascaded 64-neuron fully connected feedback network. In 100 randomly selected problems, the network found optimal or good solutions in most cases, with settling times in the range of 7-100 microseconds.
Weight Constraints in Neural Networks
Directory of Open Access Journals (Sweden)
Subha Fernando
2012-01-01
Full Text Available Hebbian plasticity precisely describes how synapses increase their synaptic strengths according to the correlated activities between two neurons; however, it fails to explain how these activities dilute the strength of the same synapses. Recent literature has proposed spike-timing-dependent plasticity and short-term plasticity on multiple dynamic stochastic synapses that can control synaptic excitation and remove many user-defined constraints. Under this hypothesis, a network model was implemented giving more computational power to receptors, and the behavior at a synapse was defined by the collective dynamic activities of stochastic receptors. An experiment was conducted to analyze can spike-timing-dependent plasticity interplay with short-term plasticity to balance the excitation of the Hebbian neurons without weight constraints? If so what underline mechanisms help neurons to maintain such excitation in computational environment? According to our results both plasticity mechanisms work together to balance the excitation of the neural network as our neurons stabilized its weights for Poisson inputs with mean firing rates from 10 Hz to 40 Hz. The behavior generated by the two neurons was similar to the behavior discussed under synaptic redistribution, so that synaptic weights were stabilized while there was a continuous increase of presynaptic probability of release and higher turnover rate of postsynaptic receptors.
Energy Technology Data Exchange (ETDEWEB)
Baptista Filho, Benedito Dias
1998-07-01
A new architecture of artificial neural networks was developed with the development of new control systems for nuclear facilities and robotics. The new concepts introduced with this architecture were based in the design of task dedicated networks, in the use of multiple synaptic contacts with new transfer functions, and in the use of synaptic plasticity processes similar to that of biological organisms. These concepts take advantage of the evolutionary aspects of biological systems in their architecture, signal transmission, and memory and learning processes, given rise to a generalization capacity not obtained with classical feed-forward (F-F) neural networks. The new concepts were applied to the control of a planar two-link robotic manipulator and to the control of a natural circulation water loop. Its efficiency and generalization capacity were compared with a classic neural network in F-F, trained with the back propagation algorithm. The excellent performance obtained, mainly in the manipulator's problem, that characterizes a system of dynamic more complex, demonstrated that the use of the new task specialized network, produces much better results than the results obtained with use of generic F-F networks. The training task in the new proposed neural network was 150 times faster than the F-F neural network training with back-propagation. The results showed that the positioning errors with the new model were up to 60 times smaller than the errors found with the F-F network. (author)
Application of neural network to CT
International Nuclear Information System (INIS)
Ma, Xiao-Feng; Takeda, Tatsuoki
1999-01-01
This paper presents a new method for two-dimensional image reconstruction by using a multilayer neural network. Multilayer neural networks are extensively investigated and practically applied to solution of various problems such as inverse problems or time series prediction problems. From learning an input-output mapping from a set of examples, neural networks can be regarded as synthesizing an approximation of multidimensional function (that is, solving the problem of hypersurface reconstruction, including smoothing and interpolation). From this viewpoint, neural networks are well suited to the solution of CT image reconstruction. Though a conventionally used object function of a neural network is composed of a sum of squared errors of the output data, we can define an object function composed of a sum of residue of an integral equation. By employing an appropriate line integral for this integral equation, we can construct a neural network that can be used for CT. We applied this method to some model problems and obtained satisfactory results. As it is not necessary to discretized the integral equation using this reconstruction method, therefore it is application to the problem of complicated geometrical shapes is also feasible. Moreover, in neural networks, interpolation is performed quite smoothly, as a result, inverse mapping can be achieved smoothly even in case of including experimental and numerical errors, However, use of conventional back propagation technique for optimization leads to an expensive computation cost. To overcome this drawback, 2nd order optimization methods or parallel computing will be applied in future. (J.P.N.)
Flight control with adaptive critic neural network
Han, Dongchen
2001-10-01
In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.
Machine Learning Topological Invariants with Neural Networks
Zhang, Pengfei; Shen, Huitao; Zhai, Hui
2018-02-01
In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.
Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling
Energy Technology Data Exchange (ETDEWEB)
Afshar, Mohammad [Islamic Azad University, Kharg (Iran, Islamic Republic of); Gholami, Amin [Petroleum University of Technology, Abadan (Iran, Islamic Republic of); Asoodeh, Mojtaba [Islamic Azad University, Birjand (Iran, Islamic Republic of)
2014-03-15
Bubble point pressure is a critical pressure-volume-temperature (PVT) property of reservoir fluid, which plays an important role in almost all tasks involved in reservoir and production engineering. We developed two sophisticated models to estimate bubble point pressure from gas specific gravity, oil gravity, solution gas oil ratio, and reservoir temperature. Neural network and adaptive neuro-fuzzy inference system are powerful tools for extracting the underlying dependency of a set of input/output data. However, the mentioned tools are in danger of sticking in local minima. The present study went further by optimizing fuzzy logic and neural network models using the genetic algorithm in charge of eliminating the risk of being exposed to local minima. This strategy is capable of significantly improving the accuracy of both neural network and fuzzy logic models. The proposed methodology was successfully applied to a dataset of 153 PVT data points. Results showed that the genetic algorithm can serve the neural network and neuro-fuzzy models from local minima trapping, which might occur through back-propagation algorithm.
Directory of Open Access Journals (Sweden)
Hamid Taghavifar
2014-08-01
Full Text Available This paper describes the measurement of contact pressure in the context of wheel–terrain interaction as affected by wheel load and tire inflation pressure when fusion of the wavelet transform with the back-propagation (BP neural network is applied to construct the wavelet neural network contact pressure prediction model. To this aim, a controlled soil bin testing facility equipped with single-wheel tester was utilized while three levels of velocity, three levels of slippage and three levels of wheel load were applied. Using image processing technique, contact area values were determined which were subsequently used for quantification of contact pressure. Performances of the different predictor models incorporated of various mother wavelets were evaluated using standard statistical evaluation criteria. Root mean square error and coefficient of determination values of 0.1382 and 0.9864 achieved by the optimal wavelet neural network are better than that of BP neural network. The proposed tool typifies a high learning speed, enhanced predicting accuracy, and strong robustness.
VULNERABILITY INDEX ASSESSMENT USING NEURAL NETWORKS (VIANN: A CASE STUDY OF NICOSIA, CYPRUS
Directory of Open Access Journals (Sweden)
H. Dindar
2017-11-01
Full Text Available Many scholars have used microtremor applications to evaluate the vulnerability index. In order to reach fast and reliable results, microtremor measurement is preferred as it is a cost-effective method. In this paper, the vulnerability index will be reviewed by utilization of microtremor measurement results in Nicosia city. 100 measurement stations have been used to collect microtremor data and the data were analysed by using Nakamura’s method. The value of vulnerability index (Kg has been evaluated by using the fundamental frequency and amplification factor. The results obtained by the artificial neural network (ANN will be compared with microtremor measurements. Vulnerability Index Assessment using Neural Networks (VIANN is a backpropagation neural network, which uses the original input microtremor Horizontal Vertical Spectrum Ratio (HVSR spectrum set. A 3-layer back propagation neural network which contains 4096 input, 28 hidden and 3 output neurons are used in this suggested system. The output neurons are classified according to acceleration sensitivity zone, velocity zones, or displacement zones. The sites are classified by their vulnerability index values using binary coding: [1 0 0] for the acceleration sensitive zone, [0 1 0] for the velocity sensitive zone, and [0 0 1] for the displacement sensitive zone.
Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran
Directory of Open Access Journals (Sweden)
Zahra Pezeshki
2016-02-01
Full Text Available Background: Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera. Objectives: In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model. Materials and Methods: Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters. Results: After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other. Conclusions: Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms
Directory of Open Access Journals (Sweden)
Beatriz A. Garro
2015-01-01
Full Text Available Artificial Neural Network (ANN design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO, Second Generation of Particle Swarm Optimization (SGPSO, and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE and the classification error (CER and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.
Artificial Neural Network-Based System for PET Volume Segmentation
Directory of Open Access Journals (Sweden)
Mhd Saeed Sharif
2010-01-01
Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.
Novel Screening Tool for Stroke Using Artificial Neural Network.
Abedi, Vida; Goyal, Nitin; Tsivgoulis, Georgios; Hosseinichimeh, Niyousha; Hontecillas, Raquel; Bassaganya-Riera, Josep; Elijovich, Lucas; Metter, Jeffrey E; Alexandrov, Anne W; Liebeskind, David S; Alexandrov, Andrei V; Zand, Ramin
2017-06-01
The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiate that from stroke mimics in an emergency setting. Consecutive patients presenting to the emergency department with stroke-like symptoms, within 4.5 hours of symptoms onset, in 2 tertiary care stroke centers were randomized for inclusion in the model. We developed an artificial neural network (ANN) model. The learning algorithm was based on backpropagation. To validate the model, we used a 10-fold cross-validation method. A total of 260 patients (equal number of stroke mimics and ACIs) were enrolled for the development and validation of our ANN model. Our analysis indicated that the average sensitivity and specificity of ANN for the diagnosis of ACI based on the 10-fold cross-validation analysis was 80.0% (95% confidence interval, 71.8-86.3) and 86.2% (95% confidence interval, 78.7-91.4), respectively. The median precision of ANN for the diagnosis of ACI was 92% (95% confidence interval, 88.7-95.3). Our results show that ANN can be an effective tool for the recognition of ACI and differentiation of ACI from stroke mimics at the initial examination. © 2017 American Heart Association, Inc.
AN APPLICATION OF SPEAKER RECOGNITION USING ARTIFICIAL NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
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.
Artificial Neural Network-Based System for PET Volume Segmentation.
Sharif, Mhd Saeed; Abbod, Maysam; Amira, Abbes; Zaidi, Habib
2010-01-01
Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.
Energy demand estimation of South Korea using artificial neural network
International Nuclear Information System (INIS)
Geem, Zong Woo; Roper, William E.
2009-01-01
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.
Estimation of Conditional Quantile using Neural Networks
DEFF Research Database (Denmark)
Kulczycki, P.; Schiøler, Henrik
1999-01-01
The problem of estimating conditional quantiles using neural networks is investigated here. A basic structure is developed using the methodology of kernel estimation, and a theory guaranteeing con-sistency on a mild set of assumptions is provided. The constructed structure constitutes a basis...... for the design of a variety of different neural networks, some of which are considered in detail. The task of estimating conditional quantiles is related to Bayes point estimation whereby a broad range of applications within engineering, economics and management can be suggested. Numerical results illustrating...... the capabilities of the elaborated neural network are also given....
Genetic algorithm for neural networks optimization
Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta
2004-11-01
This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.
Applications of neural network to numerical analyses
International Nuclear Information System (INIS)
Takeda, Tatsuoki; Fukuhara, Makoto; Ma, Xiao-Feng; Liaqat, Ali
1999-01-01
Applications of a multi-layer neural network to numerical analyses are described. We are mainly concerned with the computed tomography and the solution of differential equations. In both cases as the objective functions for the training process of the neural network we employed residuals of the integral equation or the differential equations. This is different from the conventional neural network training where sum of the squared errors of the output values is adopted as the objective function. For model problems both the methods gave satisfactory results and the methods are considered promising for some kind of problems. (author)
Decision boundary feature extraction for neural networks
Lee, Chulhee; Landgrebe, David A.
1992-01-01
We propose a new feature extraction method for neural networks. The method is based on the recently published decision boundary feature extraction algorithm. It has been shown that all the necessary features for classification can be extracted from the decision boundary. To apply the decision boundary feature extraction method, we first define the decision boundary in neural networks. Next, we propose a procedure for extracting all the necessary features for classification from the decision boundary. The proposed algorithm preserves the characteristics of neural networks, which can define arbitrary decision boundary. Experiments show promising results.
Convolutional Neural Network for Image Recognition
Seifnashri, Sahand
2015-01-01
The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.
Nonequilibrium landscape theory of neural networks
Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin
2013-01-01
The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451
Neural Network for Estimating Conditional Distribution
DEFF Research Database (Denmark)
Schiøler, Henrik; Kulczycki, P.
Neural networks for estimating conditional distributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency is proved from a mild set of assumptions. A number of applications within...... statistcs, decision theory and signal processing are suggested, and a numerical example illustrating the capabilities of the elaborated network is given...
Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis
Directory of Open Access Journals (Sweden)
Chernoded Andrey
2017-01-01
Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.
Improved transformer protection using probabilistic neural network ...
African Journals Online (AJOL)
user
secure and dependable protection for power transformers. Owing to its superior learning and generalization capabilities Artificial. Neural Network (ANN) can considerably enhance the scope of WI method. ANN approach is faster, robust and easier to implement than the conventional waveform approach. The use of neural ...
Artificial neural networks a practical course
da Silva, Ivan Nunes; Andrade Flauzino, Rogerio; Liboni, Luisa Helena Bartocci; dos Reis Alves, Silas Franco
2017-01-01
This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.
Neural Networks in Mobile Robot Motion
Directory of Open Access Journals (Sweden)
Danica Janglova
2008-11-01
Full Text Available This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the "free" space using ultrasound range finder data. The second neural network "finds" a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.
Memory pattern analysis of cellular neural networks
International Nuclear Information System (INIS)
Zeng Zhigang; Huang Deshuang; Wang Zengfu
2005-01-01
In this Letter, we have shown that the n-dimensional cellular neural network and delay cellular neural network can have not more than 3 n memory patterns, can have 2 n memory patterns which are locally exponentially stable. And we have obtained the estimates of attractive domain of such 2 n locally exponentially stable memory patterns. In addition, we have derived the conditions that the equilibrium point is locally exponentially stable when the equilibrium point locate the designated position. Some sufficient conditions have been obtained to guarantee the global exponential stability for the cellular neural networks. Those conditions can be directly derived from the parameters of the neural networks, are very easy to verified. The results presented in this Letter are the improvement and extension of the existed ones. Finally, the validity and performance of the results are illustrated by two simulation results
Control Augmentation Using Adaptive Fuzzy Neural Networks
Kato, Akio; Wada, Yoshihisa
Control to improve control characteristics of aircraft, CA (Control Augmentation), is used to realize the desirable motion of aircraft corresponding to pilot's control action. When the control laws using fuzzy inference were designed, trial and error was repeated for optimization of the parameter. Here, in designing control laws using fuzzy neural networks, the systematic optimization of the parameter was possible using the learning algorithm usually used in neural networks, by expressing the fuzzy inference in the form of neural networks. Here, the control laws, which learned the characteristics of the aircraft for one flight condition only, were used in all flight conditions without changing any parameter. Evaluation of the designed control laws showed good performance in all flight conditions. This proves that fuzzy neural networks are an effective and flexible method when applied to control laws for control augmentation of aircraft.
Imbibition well stimulation via neural network design
Weiss, William
2007-08-14
A method for stimulation of hydrocarbon production via imbibition by utilization of surfactants. The method includes use of fuzzy logic and neural network architecture constructs to determine surfactant use.
Neural Networks in Mobile Robot Motion
Directory of Open Access Journals (Sweden)
Danica Janglová
2004-03-01
Full Text Available This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.
Using Neural Networks in Diagnosing Breast Cancer
National Research Council Canada - National Science Library
Fogel, David
1997-01-01
.... In the current study, evolutionary programming is used to train neural networks and linear discriminant models to detect breast cancer in suspicious and microcalcifications using radiographic features and patient age...
Control of autonomous robot using neural networks
Barton, Adam; Volna, Eva
2017-07-01
The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.
PREDIKSI FOREX MENGGUNAKAN MODEL NEURAL NETWORK
Directory of Open Access Journals (Sweden)
R. Hadapiningradja Kusumodestoni
2015-11-01
Full Text Available ABSTRAK Prediksi adalah salah satu teknik yang paling penting dalam menjalankan bisnis forex. Keputusan dalam memprediksi adalah sangatlah penting, karena dengan prediksi dapat membantu mengetahui nilai forex di waktu tertentu kedepan sehingga dapat mengurangi resiko kerugian. Tujuan dari penelitian ini dimaksudkan memprediksi bisnis fores menggunakan model neural network dengan data time series per 1 menit untuk mengetahui nilai akurasi prediksi sehingga dapat mengurangi resiko dalam menjalankan bisnis forex. Metode penelitian pada penelitian ini meliputi metode pengumpulan data kemudian dilanjutkan ke metode training, learning, testing menggunakan neural network. Setelah di evaluasi hasil penelitian ini menunjukan bahwa penerapan algoritma Neural Network mampu untuk memprediksi forex dengan tingkat akurasi prediksi 0.431 +/- 0.096 sehingga dengan prediksi ini dapat membantu mengurangi resiko dalam menjalankan bisnis forex. Kata kunci: prediksi, forex, neural network.
Pilot Model Using Neural Networks
Kato, Akio; Matsubara, Genyo; Nakamura, Takeshi
The motion of an aircraft controlled by a pilot is decided depending on the characteristics of a man-machine system. Although analysis and investigation are usually performed using a mathematical model of the aircraft including the control system, a method for making a mathematical model of the pilot, which is necessary for the analysis and study of man-machine systems, has not been established. Although a method for constructing a mathematical model of a pilot using a transfer function 1) has been reported, it is thought that a more accurate and more flexible pilot model may be obtained by applying a neural network (NN). Therefore, various studies have examined a pilot model to which a NN has been applied. As a result, it has been clarified that the application of a NN to a pilot model provides better performance compared to the case of applying a transfer function. Moreover, it has also been clarified that a single versatile pilot model, which can deal with various conditions, can be obtained by applying a NN and studying the control results under various conditions.
Pandey, Daya Shankar; Das, Saptarshi; Pan, Indranil; Leahy, James J; Kwapinski, Witold
2016-12-01
In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHV p ) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the Levenberg-Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidized bed gasifier. Copyright © 2016 Elsevier Ltd. All rights reserved.
Modelling of solar energy potential in Nigeria using an artificial neural network model
International Nuclear Information System (INIS)
Fadare, D.A.
2009-01-01
In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14 o N, log. 2-15 o E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m 2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.
Learning Maneuvers Using Neural Network Models
1994-08-07
parametric function approximators such as neural networks ( Tesauro 1991). The prediction process runs in a series of epochs. Each epoch ends when a...function approximator such as a neural network. This technique has recently been used successfully on a large complex problem, Backgammon, by Tesauro (1991...Morgan Kaufman. Tesauro , G. J. (1991). Practical Issues in Temporal Difference Learning. Report RC 17223 (76307), IBM T. J. Watson Research Center
Neural Photo Editing with Introspective Adversarial Networks
Brock, Andrew; Lim, Theodore; Ritchie, J. M.; Weston, Nick
2016-01-01
The increasingly photorealistic sample quality of generative image models suggests their feasibility in applications beyond image generation. We present the Neural Photo Editor, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images. To tackle the challenge of achieving accurate reconstructions without loss of feature quality, we introduce the Introspective Adversarial Network, a novel hybridization of the VAE and GA...
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...
Using neural networks in software repositories
Eichmann, David (Editor); Srinivas, Kankanahalli; Boetticher, G.
1992-01-01
The first topic is an exploration of the use of neural network techniques to improve the effectiveness of retrieval in software repositories. The second topic relates to a series of experiments conducted to evaluate the feasibility of using adaptive neural networks as a means of deriving (or more specifically, learning) measures on software. Taken together, these two efforts illuminate a very promising mechanism supporting software infrastructures - one based upon a flexible and responsive technology.
Genetic Algorithms for Evolving Deep Neural Networks
David, Eli; Greental, Iddo
2017-01-01
In recent years, deep learning methods applying unsupervised learning to train deep layers of neural networks have achieved remarkable results in numerous fields. In the past, many genetic algorithms based methods have been successfully applied to training neural networks. In this paper, we extend previous work and propose a GA-assisted method for deep learning. Our experimental results indicate that this GA-assisted approach improves the performance of a deep autoencoder, producing a sparser...
Neural networks, D0, and the SSC
International Nuclear Information System (INIS)
Barter, C.; Cutts, D.; Hoftun, J.S.; Partridge, R.A.; Sornborger, A.T.; Johnson, C.T.; Zeller, R.T.
1989-01-01
We outline several exploratory studies involving neural network simulations applied to pattern recognition in high energy physics. We describe the D0 data acquisition system and a natual means by which algorithms derived from neural networks techniques may be incorporated into recently developed hardware associated with the D0 MicroVAX farm nodes. Such applications to the event filtering needed by SSC detectors look interesting. 10 refs., 11 figs
Application of neural networks in CRM systems
Directory of Open Access Journals (Sweden)
Bojanowska Agnieszka
2017-01-01
Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.
A generalized LSTM-like training algorithm for second-order recurrent neural networks.
Monner, Derek; Reggia, James A
2012-01-01
The long short term memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM's original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting its applicability to a small set of network architectures. Here we introduce the generalized long short-term memory(LSTM-g) training algorithm, which provides LSTM-like locality while being applicable without modification to a much wider range of second-order network architectures. With LSTM-g, all units have an identical set of operating instructions for both activation and learning, subject only to the configuration of their local environment in the network; this is in contrast to the original LSTM training algorithm, where each type of unit has its own activation and training instructions. When applied to LSTM architectures with peephole connections, LSTM-g takes advantage of an additional source of back-propagated error which can enable better performance than the original algorithm. Enabled by the broad architectural applicability of LSTM-g, we demonstrate that training recurrent networks engineered for specific tasks can produce better results than single-layer networks. We conclude that LSTM-g has the potential to both improve the performance and broaden the applicability of spatially and temporally local gradient-based training algorithms for recurrent neural networks. Copyright © 2011 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Héliton Pandorfi
2016-06-01
Full Text Available ABSTRACT This study aimed to investigate the applicability of artificial neural networks (ANNs in the prediction of evapotranspiration of sweet pepper cultivated in a greenhouse. The used data encompass the second crop cycle, from September 2013 to February 2014, constituting 135 days of daily meteorological data, referring to the following variables: temperature and relative air humidity, wind speed and solar radiation (input variables, as well as evapotranspiration (output variable, determined using data obtained by load-cell weighing lysimeter. The recorded data were divided into three sets for training, testing and validation. The ANN learning model recognized the evapotranspiration patterns with acceptable accuracy, with mean square error of 0.005, in comparison to the data recorded in the lysimeter, with coefficient of determination of 0.87, demonstrating the best approximation for the 4-21-1 network architecture, with multilayers, error back-propagation learning algorithm and learning rate of 0.01.
Learning to train neural networks for real-world control problems
Feldkamp, Lee A.; Puskorius, G. V.; Davis, L. I., Jr.; Yuan, F.
1994-01-01
Over the past three years, our group has concentrated on the application of neural network methods to the training of controllers for real-world systems. This presentation describes our approach, surveys what we have found to be important, mentions some contributions to the field, and shows some representative results. Topics discussed include: (1) executing model studies as rehearsal for experimental studies; (2) the importance of correct derivatives; (3) effective training with second-order (DEKF) methods; (4) the efficacy of time-lagged recurrent networks; (5) liberation from the tyranny of the control cycle using asynchronous truncated backpropagation through time; and (6) multistream training for robustness. Results from model studies of automotive idle speed control serve as examples for several of these topics.
Failure forecast of Boeing 737 bleed air system using artificial neural networks
Al-Wadiee, Waheed
In this study, the failure rate of different types of bleed air control valves for the Boeing 737 aircraft is modeled. Two approaches are utilized to perform this work. In the first approach, Weibull model, in which different parameters are utilized and tested, is used. In the second one, a common type of the Artificial Neural Network (ANN) modeling is used. A Feed-forward back-propagation algorithm is implemented to train the network. Subsequently, the optimum number of neurons and layers that give the best result compared to the actual data are determined. Finally, the outputs from both models are compared against the actual data. The final results show a high level of accuracy of the ANN's predictions compared to the more traditional Weibull modeling. The developed verified model lends itself to applications that extend from scheduling replacements operations of these valves, to developing plans for inventory management in any aviation engines maintenance facility.
Directory of Open Access Journals (Sweden)
Ruiyi Que
2012-08-01
Full Text Available Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed.
Que, Ruiyi; Zhu, Rong
2012-01-01
Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed.
Salu, Yehuda; Tilton, James
1993-01-01
The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.
Artificial Astrocytes Improve Neural Network Performance
Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso
2011-01-01
Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157
Artificial astrocytes improve neural network performance.
Directory of Open Access Journals (Sweden)
Ana B Porto-Pazos
Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.
NEURAL NETWORKS FOR STOCK MARKET OPTION PRICING
Directory of Open Access Journals (Sweden)
Sergey A. Sannikov
2017-03-01
Full Text Available Introduction: The use of neural networks for non-linear models helps to understand where linear model drawbacks, coused by their specification, reveal themselves. This paper attempts to find this out. The objective of research is to determine the meaning of “option prices calculation using neural networks”. Materials and Methods: We use two kinds of variables: endogenous (variables included in the model of neural network and variables affecting on the model (permanent disturbance. Results: All data are divided into 3 sets: learning, affirming and testing. All selected variables are normalised from 0 to 1. Extreme values of income were shortcut. Discussion and Conclusions: Using the 33-14-1 neural network with direct links we obtained two sets of forecasts. Optimal criteria of strategies in stock markets’ option pricing were developed.
Sub-basalt imaging problems and the application of Artificial Neural Networks
Fitzgerald, Elaine M.; Bean, Christopher J.
2001-12-01
In many areas of the world, the presence of shallow high velocity, highly heterogeneous layers complicate seismic imaging of deeper reflectors. Of particular economic interest are areas where potentially hydrocarbon-bearing strata are obscured by layers of basalt. Basalt layers are highly reflective and heterogeneous. Using reflection seismic, top basalt is typified by a high-amplitude, coherent reflector with poor resolution of reflectors below the basalt, and even bottom basalt. Here, we present a new approach to the imaging problem using the pattern recognition abilities of a back-propagation Artificial Neural Network (ANN). ANNs are computational systems that attempt to mimic natural biological neural networks. They have the ability to recognize patterns and develop their own generalizations about a given data set. Back-propagation neural networks are trained on data sets for which the solution is known and tested on the data that are not previously presented to the ANN in order to validate the network result. We show that Artificial Neural Networks, due to their pattern recognition capabilities, can invert the medium statistics based on the seismic character. We produce statistically defined models involving a basalt analogous layer, and calculate full wavefield finite difference synthetic seismograms. We vary basalt layer thickness and source frequency to generate a synthetic model that produces seismic that is similar to real sub-basalt seismic, i.e. high amplitude top basalt reflector and the absence of base basalt and sub-basalt events. Using synthetic shot gathers, generated in a synthetic representation of the sub-basalt case, we can invert the velocity medium standard deviation by using an ANN. By inverting the velocity medium standard deviation, we successfully identified the transition from basalt to sub-basalt on the synthetic shot gathers. We also show that ANNs are capable of identifying the basalt to sub-basalt transition in the presence of
Neural Network Course Changing and Track Keeping Controller for a Submarine
Directory of Open Access Journals (Sweden)
Dur Muhammad Pathan
2012-10-01
Full Text Available This paper presents the performance of ANN (Artificial Neural Networks technique for the development of controller for heading motions of submarine. A MLP (Multi-Layer Preceptron FFNN (Feed-Forward Neural Network is used for development of controller. Supervised type of learning is used for training of network by using back-propagation Algorithm. The training is performed by providing a nonlinear sliding mode controller as a supervisor. The development of controller is based on nonlinear decoupled heading model of a submarine without consideration of external environmental disturbances. To demonstrate the robustness of controller the performance of controller is tested in different operating conditions: course changing, track keeping and under the influence of sea currents. Simulations results show that in all cases, the heading error comes to zero, which indicates that the actual heading converges to the desired heading in finite time. The maximum error is observed 0.5o for 45o command angle, in presence of sea currents. The result demonstrates that the performance neural network controller has been robust.
Neural Network course changing and track keeping controller for a submarine
International Nuclear Information System (INIS)
Pathan, D.M.; Abbasi, A.F.; Memon, Z.
2012-01-01
This paper present the performance of ANN (Artificial Neural Networks) technique for the development of controller for heading motions of submarine. AMLP (Multi-Layer Preceptron) FFNN (Feed-Forward Neural Network) is used for development of controller. Supervised type of learning is used for training of network by using back-propagation Algorithm. The training is performed by providing a nonlinear sliding mode controller as a supervisor. The development of controller is based on nonlinear decoupled heading model of a submarine without consideration of external environmental disturbances. To demonstrate the robustness of controller the performance of controller is tested in different operating conditions: course changing, track keeping and under the influence of sea currents. Simulations results show that in all cases, the heading error comes to zero, which indicates that the actual heading converges to the desired heading in finite time. The maximum error is observed 0.50 for 450 command angle, in presence of sea currents. The result demonstrates that the performance neural network controller has been robust. (author)
HTTR operation monitoring with neural network in 30 days operation at 850degC
International Nuclear Information System (INIS)
Shimizu, Atsushi; Nabeshima, Kunihiko; Nakagawa, Shigeaki
2009-01-01
The High temperature engineering test reactor (HTTR) executed the rated power operation for 30days of the first time (850degC in temperature of the nuclear reactor outlet coolant) until March, 27th through April, 26th, 2007. In this operation, HTTR was observed according to the operation monitoring model with the neural network, and the detection performance of neural network was verified during slight changes of reactor state at rated power. The neural network used for the operation monitoring was an auto-associative network, where 31 input 31 outputs and the hidden layers were connected with 20 units by the hierarchy of three layer structure. Back-propagation algorithm was used for study rule. The operation monitoring model in initial study was constructed by using the power up data between 30% and rated power, which were randomly studied. The adjustment study during the operation monitoring changes the internal structure of the initial study model to follow the changes of reactor status, such as the burn-up of the nuclear fuel for the rated power operation. As a monitoring result, slight changes of reactor state by the control system operation were correctly detected, and the on-line application to an early anomaly diagnosis for HTTR facilities will be expected. (author)
Neural network approaches to tracer identification as related to PIV research
Energy Technology Data Exchange (ETDEWEB)
Seeley, C.H. Jr.
1992-12-01
Neural networks have become very powerful tools in many fields of interest. This thesis examines the application of neural networks to another rapidly growing field flow visualization. Flow visualization research is used to experimentally determine how fluids behave and to verify computational results obtained analytically. A form of flow visualization, particle image velocimetry (PIV). determines the flow movement by tracking neutrally buoyant particles suspended in the fluid. PIV research has begun to improve rapidly with the advent of digital imagers, which can quickly digitize an image into arrays of grey levels. These grey level arrays are analyzed to determine the location of the tracer particles. Once the particles positions have been determined across multiple image frames, it is possible to track their movements, and hence, the flow of the fluid. This thesis explores the potential of several different neural networks to identify the positions of the tracer particles. Among these networks are Backpropagation, Kohonen (counter-propagation), and Cellular. Each of these algorithms were employed in their basic form, and training and testing were performed on a synthetic grey level array. Modifications were then made to them in attempts to improve the results.
Neural network approaches to tracer identification as related to PIV research
International Nuclear Information System (INIS)
Seeley, C.H. Jr.
1992-12-01
Neural networks have become very powerful tools in many fields of interest. This thesis examines the application of neural networks to another rapidly growing field flow visualization. Flow visualization research is used to experimentally determine how fluids behave and to verify computational results obtained analytically. A form of flow visualization, particle image velocimetry (PIV). determines the flow movement by tracking neutrally buoyant particles suspended in the fluid. PIV research has begun to improve rapidly with the advent of digital imagers, which can quickly digitize an image into arrays of grey levels. These grey level arrays are analyzed to determine the location of the tracer particles. Once the particles positions have been determined across multiple image frames, it is possible to track their movements, and hence, the flow of the fluid. This thesis explores the potential of several different neural networks to identify the positions of the tracer particles. Among these networks are Backpropagation, Kohonen (counter-propagation), and Cellular. Each of these algorithms were employed in their basic form, and training and testing were performed on a synthetic grey level array. Modifications were then made to them in attempts to improve the results
Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach
2016-03-30
Undergraduate Student Paper Postgraduate Student Paper Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach...monitoring, flight parameter, nonlinear modeling, Artificial Neural Network , typical loadcase. Introduction Aircraft load monitoring is an... Neural Networks (ANN), i.e. the BP network and Kohonen Clustering Network , are applied and revised by Kalman Filter and Genetic Algorithm to build
Self-organized critical neural networks
International Nuclear Information System (INIS)
Bornholdt, Stefan; Roehl, Torsten
2003-01-01
A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks, network connectivity is closely related to a phase transition between ordered and disordered dynamics. A slow topology change is imposed on the network through a local rewiring rule motivated by activity-dependent synaptic development: Neighbor neurons whose activity is correlated, on average develop a new connection while uncorrelated neighbors tend to disconnect. As a result, robust self-organization of the network towards the order disorder transition occurs. Convergence is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters
Directory of Open Access Journals (Sweden)
Fereydoon Sarmadian
2014-01-01
Full Text Available The two common methods used to develop PTFs are multiple-linear regression method and Artificial Neural Network. One of the advantages of neural networks compared to traditional regression PTFs is that they do not require a priori regression model, which relates input and output data and in general is difficult because these models are not known. So at present research, we compare performance of feed-forward back-propagation network to predict soil properties. Soil samples were collected from different horizons profiles located in the Gorgan Province, North of Iran. Measured soil variables included texture, organic carbon, water saturation percentage Bulk density, Infiltration rate and deep percolation. Then, multiple linear regression and neural network model were employed to develop a pedotransfer function for predicting soil parameters using easily measurable characteristics of clay, silt, SP, Bd and organic carbon. The performance of the multiple linear regression and neural network model was evaluated using a test data set by R2, RMSE and RSE. Results showed that artificial neural network with two and five neurons in hidden layer had better performance in predicting soil hydraulic properties than multivariate regression. In conclusion, the result of this study showed that both ANN and regression predicted soil properties with relatively high accuracy that showed that strong relationship between input and output data and also high accuracy in determining of data.
Fin-and-tube condenser performance evaluation using neural networks
Energy Technology Data Exchange (ETDEWEB)
Zhao, Ling-Xiao [Institute of Refrigeration and Cryogenics, Shanghai Jiaotong University, Shanghai 200240 (China); Zhang, Chun-Lu [China R and D Center, Carrier Corporation, No. 3239 Shen Jiang Road, Shanghai 201206 (China)
2010-05-15
The paper presents neural network approach to performance evaluation of the fin-and-tube air-cooled condensers which are widely used in air-conditioning and refrigeration systems. Inputs of the neural network include refrigerant and air-flow rates, refrigerant inlet temperature and saturated temperature, and entering air dry-bulb temperature. Outputs of the neural network consist of the heating capacity and the pressure drops on both refrigerant and air sides. The multi-input multi-output (MIMO) neural network is separated into multi-input single-output (MISO) neural networks for training. Afterwards, the trained MISO neural networks are combined into a MIMO neural network, which indicates that the number of training data sets is determined by the biggest MISO neural network not the whole MIMO network. Compared with a validated first-principle model, the standard deviations of neural network models are less than 1.9%, and all errors fall into {+-}5%. (author)
Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui
2014-01-01
The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes.
International Nuclear Information System (INIS)
Dashtbayazi, M.R.; Shokuhfar, A.; Simchi, A.
2007-01-01
An artificial neural network model was developed for modeling of the effects of mechanical alloying parameters including milling time, milling speed and ball to powder weight ratio on the characteristics of Al-8 vol%SiC nanocomposite powders. The crystallite size and lattice strain of the aluminum matrix were considered for modeling. This nanostructured nanocomposite powder was synthesized by utilizing planetary high energy ball mill and the required data for training were collected from the experimental results. The characteristics of the particles were determined by X-ray diffraction, scanning and transmission electron microscopy. Two types of neural network architecture, i.e. multi-layer perceptron (MLP) and radial basis function (RBF), were used. The steepest descent along with variable learning rate back-propagation algorithm known as a heuristic technique was utilized for training the MLP network. It was found that MLP network yields better results compared to RBF network, giving an acceptable mapping between the network responses and the target data with a high correlation coefficients. The response surfaces between the response variables, i.e. crystallite size, lattice strain of the aluminum matrix and the processing parameters are presented. The procedure modeling can be used for optimization of the MA process for synthesizing of nanostructured metal matrix nanocomposites
Artificial neural network intelligent method for prediction
Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi
2017-09-01
Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.
Estimating Conditional Distributions by Neural Networks
DEFF Research Database (Denmark)
Kulczycki, P.; Schiøler, Henrik
1998-01-01
Neural Networks for estimating conditionaldistributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency property is considered from a mild set of assumptions. A number of applications...
Medical Text Classification Using Convolutional Neural Networks.
Hughes, Mark; Li, Irene; Kotoulas, Spyros; Suzumura, Toyotaro
2017-01-01
We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health information. Through a detailed evaluation, we demonstrate that our method outperforms several approaches widely used in natural language processing tasks by about 15%.
Learning drifting concepts with neural networks
Biehl, Michael; Schwarze, Holm
1993-01-01
The learning of time-dependent concepts with a neural network is studied analytically and numerically. The linearly separable target rule is represented by an N-vector, whose time dependence is modelled by a random or deterministic drift process. A single-layer network is trained online using
Design of Robust Neural Network Classifiers
DEFF Research Database (Denmark)
Larsen, Jan; Andersen, Lars Nonboe; Hintz-Madsen, Mads
1998-01-01
This paper addresses a new framework for designing robust neural network classifiers. The network is optimized using the maximum a posteriori technique, i.e., the cost function is the sum of the log-likelihood and a regularization term (prior). In order to perform robust classification, we present...... a modified likelihood function which incorporates the potential risk of outliers in the data. This leads to the introduction of a new parameter, the outlier probability. Designing the neural classifier involves optimization of network weights as well as outlier probability and regularization parameters. We...
Neural network for sonogram gap filling
DEFF Research Database (Denmark)
Klebæk, Henrik; Jensen, Jørgen Arendt; Hansen, Lars Kai
1995-01-01
. The neural network is trained on part of the data and the network is pruned by the optimal brain damage procedure in order to reduce the number of parameters in the network, and thereby reduce the risk of overfitting. The neural predictor is compared to using a linear filter for the mean and variance time......In duplex imaging both an anatomical B-mode image and a sonogram are acquired, and the time for data acquisition is divided between the two images. This gives problems when rapid B-mode image display is needed, since there is not time for measuring the velocity data. Gaps then appear...
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.
Evaluation of scoliosis using baropodometer and artificial neural network
Directory of Open Access Journals (Sweden)
Caroline Meireles Fanfoni
Full Text Available Abstract Introduction: One of the most recurrent pathologies in the spine is scoliosis. It occurs in the frontal plane and is formed by one or more curves in the spinal column. The scoliosis causes global postural misalignment in an individual. One of the modifications produced by postural misalignment is the way in which an individual distributes weight to the feet. We aimed to implement an electronic system for separating patients with Degree I scoliosis (i.e., 1° to 19° scoliosis according to the Ricard classification into two groups: C1 (1°-9° and C2 (10°-9°. The highest percentage of patients with scoliosis is in this range: those who do not need to wear vests or undergo surgery and whose treatment is performed via special physical exercise and frequent evaluations by healthcare professionals. Methods The electronic system consists of a baropodometer and artificial neural networks (ANNs. The classification of patients in the scoliosis groups was performed with MATLAB software and a Single Layer Perceptron network using the backpropagation training algorithm. Evaluations were performed on 63 volunteers. Results The mean classification sensitivity was 93.7% in the C1 group and 94.5% in the C2 group. The classification accuracy was 83.3% in the C1 group and 96.0% in the C2 group. Conclusion The implemented system can contribute to the treatment of patients with scoliosis grades ranging from 1° to 19°, which represents the highest incidence of this pathology, for which the monitoring of the clinical condition using noninvasive techniques is of fundamental importance.
A quantum-implementable neural network model
Chen, Jialin; Wang, Lingli; Charbon, Edoardo
2017-10-01
A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.
Neutron spectrometry using artificial neural networks
International Nuclear Information System (INIS)
Vega-Carrillo, Hector Rene; Martin Hernandez-Davila, Victor; Manzanares-Acuna, Eduardo; Mercado Sanchez, Gema A.; Pilar Iniguez de la Torre, Maria; Barquero, Raquel; Palacios, Francisco; Mendez Villafane, Roberto; Arteaga Arteaga, Tarcicio; Manuel Ortiz Rodriguez, Jose
2006-01-01
An artificial neural network has been designed to obtain neutron spectra from Bonner spheres spectrometer count rates. The neural network was trained using 129 neutron spectra. These include spectra from isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra based on mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. The re-binned spectra and the UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and their respective spectra were used as output during the neural network training. After training, the network was tested with the Bonner spheres count rates produced by folding a set of neutron spectra with the response matrix. This set contains data used during network training as well as data not used. Training and testing was carried out using the Matlab ( R) program. To verify the network unfolding performance, the original and unfolded spectra were compared using the root mean square error. The use of artificial neural networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem
Neutron spectrometry with artificial neural networks
International Nuclear Information System (INIS)
Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Rodriguez, J.M.; Mercado S, G.A.; Iniguez de la Torre Bayo, M.P.; Barquero, R.; Arteaga A, T.
2005-01-01
An artificial neural network has been designed to obtain the neutron spectra from the Bonner spheres spectrometer's count rates. The neural network was trained using 129 neutron spectra. These include isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra from mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-bin ned to 31 energy groups using the MCNP 4C code. Re-binned spectra and UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and the respective spectrum was used as output during neural network training. After training the network was tested with the Bonner spheres count rates produced by a set of neutron spectra. This set contains data used during network training as well as data not used. Training and testing was carried out in the Mat lab program. To verify the network unfolding performance the original and unfolded spectra were compared using the χ 2 -test and the total fluence ratios. The use of Artificial Neural Networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)
Neutron spectrometry with artificial neural networks
Energy Technology Data Exchange (ETDEWEB)
Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Rodriguez, J.M.; Mercado S, G.A. [Universidad Autonoma de Zacatecas, A.P. 336, 98000 Zacatecas (Mexico); Iniguez de la Torre Bayo, M.P. [Universidad de Valladolid, Valladolid (Spain); Barquero, R. [Hospital Universitario Rio Hortega, Valladolid (Spain); Arteaga A, T. [Envases de Zacatecas, S.A. de C.V., Zacatecas (Mexico)]. e-mail: rvega@cantera.reduaz.mx
2005-07-01
An artificial neural network has been designed to obtain the neutron spectra from the Bonner spheres spectrometer's count rates. The neural network was trained using 129 neutron spectra. These include isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra from mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-bin ned to 31 energy groups using the MCNP 4C code. Re-binned spectra and UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and the respective spectrum was used as output during neural network training. After training the network was tested with the Bonner spheres count rates produced by a set of neutron spectra. This set contains data used during network training as well as data not used. Training and testing was carried out in the Mat lab program. To verify the network unfolding performance the original and unfolded spectra were compared using the {chi}{sup 2}-test and the total fluence ratios. The use of Artificial Neural Networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)
A neural network approach to cloud classification
Lee, Jonathan; Weger, Ronald C.; Sengupta, Sailes K.; Welch, Ronald M.
1990-01-01
It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery achieves an overall cloud identification accuracy of 93 percent. Cirrus can be distinguished from boundary layer cloudiness with an accuracy of 96 percent, without the use of an infrared channel. Stratocumulus is retrieved with an accuracy of 92 percent, cumulus at 90 percent. The use of the neural network does not improve cirrus classification accuracy. Rather, its main effect is in the improved separation between stratocumulus and cumulus cloudiness. While most cloud classification algorithms rely on linear parametric schemes, the present study is based on a nonlinear, nonparametric four-layer neural network approach. A three-layer neural network architecture, the nonparametric K-nearest neighbor approach, and the linear stepwise discriminant analysis procedure are compared. A significant finding is that significantly higher accuracies are attained with the nonparametric approaches using only 20 percent of the database as training data, compared to 67 percent of the database in the linear approach.
Neural network based multiscale image restoration approach
de Castro, Ana Paula A.; da Silva, José D. S.
2007-02-01
This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded image as input and the non-degraded image as output for the supervised learning process. The neural network thus performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image.
Neural network technologies for image classification
Korikov, A. M.; Tungusova, A. V.
2015-11-01
We analyze the classes of problems with an objective necessity to use neural network technologies, i.e. representation and resolution problems in the neural network logical basis. Among these problems, image recognition takes an important place, in particular the classification of multi-dimensional data based on information about textural characteristics. These problems occur in aerospace and seismic monitoring, materials science, medicine and other. We reviewed different approaches for the texture description: statistical, structural, and spectral. We developed a neural network technology for resolving a practical problem of cloud image classification for satellite snapshots from the spectroradiometer MODIS. The cloud texture is described by the statistical characteristics of the GLCM (Gray Level Co- Occurrence Matrix) method. From the range of neural network models that might be applied for image classification, we chose the probabilistic neural network model (PNN) and developed an implementation which performs the classification of the main types and subtypes of clouds. Also, we chose experimentally the optimal architecture and parameters for the PNN model which is used for image classification.
Inverting radiometric measurements with a neural network
Measure, Edward M.; Yee, Young P.; Balding, Jeff M.; Watkins, Wendell R.
1992-02-01
A neural network scheme for retrieving remotely sensed vertical temperature profiles was applied to observed ground based radiometer measurements. The neural network used microwave radiance measurements and surface measurements of temperature and pressure as inputs. Because the microwave radiometer is capable of measuring 4 oxygen channels at 5 different elevation angles (9, 15, 25, 40, and 90 degs), 20 microwave measurements are potentially available. Because these measurements have considerable redundancy, a neural network was experimented with, accepting as inputs microwave measurements taken at 53.88 GHz, 40 deg; 57.45 GHz, 40 deg; and 57.45, 90 deg. The primary test site was located at White Sands Missile Range (WSMR), NM. Results are compared with measurements made simultaneously with balloon borne radiosonde instruments and with radiometric temperature retrievals made using more conventional retrieval algorithms. The neural network was trained using a Widrow-Hoff delta rule procedure. Functions of date to include season dependence in the retrieval process and functions of time to include diurnal effects were used as inputs to the neural network.
Vibration Based Damage Assessment of a Cantilever using a Neural Network
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Rytter, A.
In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with with the Backpropagation Algorithm as a non-destructive damage assessment technique to locate and quantify a damage in structures is investigated.......In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with with the Backpropagation Algorithm as a non-destructive damage assessment technique to locate and quantify a damage in structures is investigated....
Automatic adjustment of display window (gray-level condition) for MR images using neural networks
International Nuclear Information System (INIS)
Ohhashi, Akinami; Nambu, Kyojiro.
1992-01-01
We have developed a system to automatically adjust the display window width and level (WWL) for MR images using neural networks. There were three main points in the development of our system as follows: 1) We defined an index for the clarity of a displayed image, and called 'EW'. EW is a quantitative measure of the clarity of an image displayed in a certain WWL, and can be derived from the difference between gray-level with the WWL adjusted by a human expert and with a certain WWL. 2) We extracted a group of six features from a gray-level histogram of a displayed image. We designed two neural networks which are able to learn the relationship between these features and the desired output (teaching signal), 'EQ', which is normalized to 0 to 1.0 from EW. Two neural networks were used to share the patterns to be learned; one learns a variety of patterns with less accuracy, and the other learns similar patterns with accuracy. Learning was performed using a back-propagation method. As a result, the neural networks after learning are able to provide a quantitative measure, 'Q', of the clarity of images displayed in the designated WWL. 3) Using the 'Hill climbing' method, we have been able to determine the best possible WWL for a displaying image. We have tested this technique for MR brain images. The results show that this system can adjust WWL comparable to that adjusted by a human expert for the majority of test images. The neural network is effective for the automatic adjustment of the display window for MR images. We are now studying the application of this method to MR images of another regions. (author)
Neural Network and Response Surface Methodology for Rocket Engine Component Optimization
Vaidyanathan, Rajkumar; Papita, Nilay; Shyy, Wei; Tucker, P. Kevin; Griffin, Lisa W.; Haftka, Raphael; Fitz-Coy, Norman; McConnaughey, Helen (Technical Monitor)
2000-01-01
The goal of this work is to compare the performance of response surface methodology (RSM) and two types of neural networks (NN) to aid preliminary design of two rocket engine components. A data set of 45 training points and 20 test points obtained from a semi-empirical model based on three design variables is used for a shear coaxial injector element. Data for supersonic turbine design is based on six design variables, 76 training, data and 18 test data obtained from simplified aerodynamic analysis. Several RS and NN are first constructed using the training data. The test data are then employed to select the best RS or NN. Quadratic and cubic response surfaces. radial basis neural network (RBNN) and back-propagation neural network (BPNN) are compared. Two-layered RBNN are generated using two different training algorithms, namely solverbe and solverb. A two layered BPNN is generated with Tan-Sigmoid transfer function. Various issues related to the training of the neural networks are addressed including number of neurons, error goals, spread constants and the accuracy of different models in representing the design space. A search for the optimum design is carried out using a standard gradient-based optimization algorithm over the response surfaces represented by the polynomials and trained neural networks. Usually a cubic polynominal performs better than the quadratic polynomial but exceptions have been noticed. Among the NN choices, the RBNN designed using solverb yields more consistent performance for both engine components considered. The training of RBNN is easier as it requires linear regression. This coupled with the consistency in performance promise the possibility of it being used as an optimization strategy for engineering design problems.
Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network
Directory of Open Access Journals (Sweden)
M. Fatih Adak
2016-02-01
Full Text Available Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC, which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data.
Flexible body control using neural networks
Mccullough, Claire L.
1992-01-01
Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.
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..
RBF Neural Network Approach for Identification and Control of DC Motors
Directory of Open Access Journals (Sweden)
EA Feilat
2012-12-01
Full Text Available In this paper, a neural network approach for the identification and control of a separately excited direct (DC motor (SEDCM driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN. The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.
Xia, Peng; Hu, Jie; Peng, Yinghong
2017-10-25
A novel model based on deep learning is proposed to estimate kinematic information for myoelectric control from multi-channel electromyogram (EMG) signals. The neural information of limb movement is embedded in EMG signals that are influenced by all kinds of factors. In order to overcome the negative effects of variability in signals, the proposed model employs the deep architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The EMG signals are transformed to time-frequency frames as the input to the model. The limb movement is estimated by the model that is trained with the gradient descent and backpropagation procedure. We tested the model for simultaneous and proportional estimation of limb movement in eight healthy subjects and compared it with support vector regression (SVR) and CNNs on the same data set. The experimental studies show that the proposed model has higher estimation accuracy and better robustness with respect to time. The combination of CNNs and RNNs can improve the model performance compared with using CNNs alone. The model of deep architecture is promising in EMG decoding and optimization of network structures can increase the accuracy and robustness. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.
Identification and control of plasma vertical position using neural network in Damavand tokamak
Energy Technology Data Exchange (ETDEWEB)
Rasouli, H. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of); Advanced Process Automation and Control (APAC) Research Group, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran (Iran, Islamic Republic of); Rasouli, C.; Koohi, A. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of)
2013-02-15
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.
Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.
Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried
2017-01-01
In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.
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.
Artificial neural network analysis for reliability prediction of regional runoff utilization.
Lee, S C; Lin, H T; Yang, T Y
2010-02-01
Many factors in the reliability analysis of planning the regional rainwater utilization tank capacity need to be considered. Based on the historical daily rainfall data, the following four analyzing procedures will be conducted: the regional daily rainfall frequency, the amount of runoff, the water continuity, and the reliability. Thereafter, the suggested designed storage capacity can be obtained according to the conditions with the demand and supply reliability. By using the output data, two different types of artificial neural network models are used to build up small area rainfall-runoff supply systems for the simulation of reliability and the prediction model. They are also used for the testing of stability and learning speed assessment. Based on the result of this research, the radial basis function neural network (RBFNN) model, using the Gaussian function that has a similar trend as the nature as basic function, has better stability than using the back-propagation neural network (BPNN) model. Despite the fact that RBFNN was more reliable than BPNN, it still made a conservative estimate for the actual monitoring data. The error rate of RBFNN was still higher than the correction of BPNN 4-3-1-1. This should have significant benefit in the future application of the instantaneous prediction or the development of related intelligent instantaneous control equipment.
Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun
2017-08-01
The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P intelligent diagnosis tool for lung cancer.
Identification and Position Control of Marine Helm using Artificial Neural Network Neural Network
Directory of Open Access Journals (Sweden)
Hui ZHU
2008-02-01
Full Text Available If nonlinearities such as saturation of the amplifier gain and motor torque, gear backlash, and shaft compliances- just to name a few - are considered in the position control system of marine helm, traditional control methods are no longer sufficient to be used to improve the performance of the system. In this paper an alternative approach to traditional control methods - a neural network reference controller - is proposed to establish an adaptive control of the position of the marine helm to achieve the controlled variable at the command position. This neural network controller comprises of two neural networks. One is the plant model network used to identify the nonlinear system and the other the controller network used to control the output to follow the reference model. The experimental results demonstrate that this adaptive neural network reference controller has much better control performance than is obtained with traditional controllers.
Implementing Signature Neural Networks with Spiking Neurons.
Carrillo-Medina, José Luis; Latorre, Roberto
2016-01-01
Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence
Fault Diagnosis Using Artificial Neural Network
International Nuclear Information System (INIS)
Maayof, R.M.A.; Abdelwahed, S.M.; Ayad, N.M.A.; Elmeniawy, N.M.H.
2004-01-01
This paper represents a special diagnostic system for handling and curing the possible failures of the Cairo Fourier Diffractometer Facility (CFDF). Two intelligent techniques, the neural network system (back propagation method) and the rule-based expert system are discussed. Both systems are integrated together as a pre-processor loosely coupled in order to build the proposed hybrid expert system. The inputs to the neural network level are the indicators conditions (symptoms), from the CFDF control panel. The outputs correspond to the status of the main parts of the CFDF. The rule-based expert system takes the inputs and outputs of the neural networks and also information from the user, to isolate and define precisely the possible faults of the CFDF. It has been found that the developed diagnostic system is both adequate and flexible for the CFDF
Recurrent Neural Network for Computing Outer Inverse.
Živković, Ivan S; Stanimirović, Predrag S; Wei, Yimin
2016-05-01
Two linear recurrent neural networks for generating outer inverses with prescribed range and null space are defined. Each of the proposed recurrent neural networks is based on the matrix-valued differential equation, a generalization of dynamic equations proposed earlier for the nonsingular matrix inversion, the Moore-Penrose inversion, as well as the Drazin inversion, under the condition of zero initial state. The application of the first approach is conditioned by the properties of the spectrum of a certain matrix; the second approach eliminates this drawback, though at the cost of increasing the number of matrix operations. The cases corresponding to the most common generalized inverses are defined. The conditions that ensure stability of the proposed neural network are presented. Illustrative examples present the results of numerical simulations.
Kannada character recognition system using neural network
Kumar, Suresh D. S.; Kamalapuram, Srinivasa K.; Kumar, Ajay B. R.
2013-03-01
Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. As there is no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India. In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten Kannada character is resized into 20x30 Pixel. The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.
Deep Neural Network Detects Quantum Phase Transition
Arai, Shunta; Ohzeki, Masayuki; Tanaka, Kazuyuki
2018-03-01
We detect the quantum phase transition of a quantum many-body system by mapping the observed results of the quantum state onto a neural network. In the present study, we utilized the simplest case of a quantum many-body system, namely a one-dimensional chain of Ising spins with the transverse Ising model. We prepared several spin configurations, which were obtained using repeated observations of the model for a particular strength of the transverse field, as input data for the neural network. Although the proposed method can be employed using experimental observations of quantum many-body systems, we tested our technique with spin configurations generated by a quantum Monte Carlo simulation without initial relaxation. The neural network successfully identified the strength of transverse field only from the spin configurations, leading to consistent estimations of the critical point of our model Γc = J.
Open quantum generalisation of Hopfield neural networks
Rotondo, P.; Marcuzzi, M.; Garrahan, J. P.; Lesanovsky, I.; Müller, M.
2018-03-01
We propose a new framework to understand how quantum effects may impact on the dynamics of neural networks. We implement the dynamics of neural networks in terms of Markovian open quantum systems, which allows us to treat thermal and quantum coherent effects on the same footing. In particular, we propose an open quantum generalisation of the Hopfield neural network, the simplest toy model of associative memory. We determine its phase diagram and show that quantum fluctuations give rise to a qualitatively new non-equilibrium phase. This novel phase is characterised by limit cycles corresponding to high-dimensional stationary manifolds that may be regarded as a generalisation of storage patterns to the quantum domain.
Neural Network Classifiers for Local Wind Prediction.
Kretzschmar, Ralf; Eckert, Pierre; Cattani, Daniel; Eggimann, Fritz
2004-05-01
This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.
Reconstruction of neutron spectra through neural networks
International Nuclear Information System (INIS)
Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.
2003-01-01
A neural network has been used to reconstruct the neutron spectra starting from the counting rates of the detectors of the Bonner sphere spectrophotometric system. A group of 56 neutron spectra was selected to calculate the counting rates that would produce in a Bonner sphere system, with these data and the spectra it was trained the neural network. To prove the performance of the net, 12 spectra were used, 6 were taken of the group used for the training, 3 were obtained of mathematical functions and those other 3 correspond to real spectra. When comparing the original spectra of those reconstructed by the net we find that our net has a poor performance when reconstructing monoenergetic spectra, this attributes it to those characteristic of the spectra used for the training of the neural network, however for the other groups of spectra the results of the net are appropriate with the prospective ones. (Author)
Equivalence of Conventional and Modified Network of Generalized Neural Elements
Directory of Open Access Journals (Sweden)
E. V. Konovalov
2016-01-01
Full Text Available The article is devoted to the analysis of neural networks consisting of generalized neural elements. The first part of the article proposes a new neural network model — a modified network of generalized neural elements (MGNE-network. This network developes the model of generalized neural element, whose formal description contains some flaws. In the model of the MGNE-network these drawbacks are overcome. A neural network is introduced all at once, without preliminary description of the model of a single neural element and method of such elements interaction. The description of neural network mathematical model is simplified and makes it relatively easy to construct on its basis a simulation model to conduct numerical experiments. The model of the MGNE-network is universal, uniting properties of networks consisting of neurons-oscillators and neurons-detectors. In the second part of the article we prove the equivalence of the dynamics of the two considered neural networks: the network, consisting of classical generalized neural elements, and MGNE-network. We introduce the definition of equivalence in the functioning of the generalized neural element and the MGNE-network consisting of a single element. Then we introduce the definition of the equivalence of the dynamics of the two neural networks in general. It is determined the correlation of different parameters of the two considered neural network models. We discuss the issue of matching the initial conditions of the two considered neural network models. We prove the theorem about the equivalence of the dynamics of the two considered neural networks. This theorem allows us to apply all previously obtained results for the networks, consisting of classical generalized neural elements, to the MGNE-network.
Directory of Open Access Journals (Sweden)
Dimililer Kamil
2018-01-01
Full Text Available Pests are divided into two as herbal and animal pests in agriculture, and detection and use of minimum pesticides are quite challenging task. Last three decades, researchers have been improving their studies on these manners. Therefore, effective, efficient, and as well as intelligent systems are designed and modelled. In this paper, an intelligent classification system is designed for detecting pests as herbal or animal to use of proper pesticides accordingly. The designed system suggests two main stages. Firstly, images are processed using different image processing techniques that images have specific distinguishing geometric patterns. The second stage is neural network phase for classification. A backpropagation neural network is used for training and testing with processed images. System is tested, and experiment results show efficiency and effective classification rate. Autonomy and time efficiency within the pesticide usage are also discussed.
Directory of Open Access Journals (Sweden)
Tosun Erdi
2017-01-01
Full Text Available This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and back-propagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.
International Nuclear Information System (INIS)
Bocco, M.
2006-01-01
The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m -2 d -1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation [pt
Mjahed, M
1999-01-01
In this work, we aim to construct a new set of variables, to recognize the number of jets produced in the e/sup +/ e/sup -/ events. These so-called morphological variables usually used in image processing and recognition problems, are comparable to the classical sphericity, aplanarity etc.. The amelioration of the recognition efficiency is obtained thanks to the use of a back-propagation neural network. The survey first done on the generated Lund Monte Carlo events could be reinforced thereafter by taking into account the simulation of the ALEPH detector. The neural network performed on this later kind of events, successfully identifies the 4 classes of events (event with 2, 3, 4 jets or with an isotropic distribution (0 jets)). (33 refs).
International Nuclear Information System (INIS)
Wardaya, P D; Ridha, S
2014-01-01
In this paper a backpropagation neural network is utilized to perform house cluster segmentation from Google Earth data. The algorithm is subjected to identify houses in the image based on the RGB pattern within each pixel. Training data is given through cropping selection for a target that is a house cluster and a non object. The algorithm assigns 1 to a pixel belong to a class of object and 0 to a class of non object. The resulting outcome, a binary image, is then utilized to perform quantification to estimate the number of house clusters. The number of the hidden layer is varying in order to find its effect to the neural network performance and total computational time
Neural networks and particle physics
Peterson, Carsten
1993-01-01
1. Introduction : Structure of the Central Nervous System Generics2. Feed-forward networks, Perceptions, Function approximators3. Self-organisation, Feature Maps4. Feed-back Networks, The Hopfield model, Optimization problems, Feed-back, Networks, Deformable templates, Graph bisection
Cotton genotypes selection through artificial neural networks.
Júnior, E G Silva; Cardoso, D B O; Reis, M C; Nascimento, A F O; Bortolin, D I; Martins, M R; Sousa, L B
2017-09-27
Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.
Neural network approaches for noisy language modeling.
Li, Jun; Ouazzane, Karim; Kazemian, Hassan B; Afzal, Muhammad Sajid
2013-11-01
Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.
Generating Seismograms with Deep Neural Networks
Krischer, L.; Fichtner, A.
2017-12-01
The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of
Directory of Open Access Journals (Sweden)
Jokić Aleksandar I.
2012-01-01
Full Text Available In this paper the bioethanol production in batch culture by free Saccharomyces cerevisiae cells from thick juice as intermediate product of sugar beet processing was examined. The obtained results suggest that it is possible to decrease fermentation time for the cultivation medium based on thick juice with starting sugar content of 5-15 g kg-1. For the fermentation of cultivation medium based on thick juice with starting sugar content of 20 and 25 g kg-1 significant increase in ethanol content was attained during the whole fermentation process, resulting in 12.51 and 10.95 dm3 m-3 ethanol contents after 48 h, respectively. Other goals of this work were to investigate the possibilities for experimental results prediction using artificial neural networks (ANNs and to find its optimal topology. A feed-forward back-propagation artificial neural network was used to test the hypothesis. As input variables fermentation time and starting sugar content were used. Neural networks had one output value, ethanol content, yeast cell number or sugar content. There was one hidden layer and the optimal number of neurons was found to be nine for all selected network outputs. In this study transfer function was tansig and the selected learning rule was Levenberg-Marquardt. Results suggest that artificial neural networks are good prediction tool for selected network outputs. It was found that experimental results are in very good agreement with computed ones. The coefficient of determination (the R-squared was found to be 0.9997, 0.9997 and 0.9999 for ethanol content, yeast cell number and sugar content, respectively.
The nuclear fuel rod character recognition system based on neural network technique
International Nuclear Information System (INIS)
Kim, Woong-Ki; Park, Soon-Yong; Lee, Yong-Bum; Kim, Seung-Ho; Lee, Jong-Min; Chien, Sung-Il.
1994-01-01
The nuclear fuel rods should be discriminated and managed systematically by numeric characters which are printed at the end part of each rod in the process of producing fuel assembly. The characters are used to examine manufacturing process of the fuel rods in the inspection process of irradiated fuel rod. Therefore automatic character recognition is one of the most important technologies to establish automatic manufacturing process of fuel assembly. In the developed character recognition system, mesh feature set extracted from each character written in the fuel rod is employed to train a neural network based on back-propagation algorithm as a classifier for character recognition system. Performance evaluation has been achieved on a test set which is not included in a training character set. (author)
The Sustainable Development Assessment of Reservoir Resettlement Based on a BP Neural Network.
Huang, Li; Huang, Jian; Wang, Wei
2018-01-18
Resettlement affects not only the resettlers' production activities and life but also, directly or indirectly, the normal operation of power stations, the sustainable development of the resettlers, and regional social stability. Therefore, a scientific evaluation index system for the sustainable development of reservoir resettlement must be established that fits Chinese national conditions and not only promotes reservoir resettlement research but also improves resettlement practice. This essay builds an evaluation index system for resettlers' sustainable development based on a back-propagation (BP) neural network, which can be adopted in China, taking the resettlement necessitated by step hydropower stations along the Wujiang River cascade as an example. The assessment results show that the resettlement caused by step power stations along the Wujiang River is sustainable, and this evaluation supports the conclusion that national policies and regulations, which are undergoing constant improvement, and resettlement has increasingly improved. The results provide a reference for hydropower reservoir resettlement in developing countries.
Effect of the size of an artificial neural network used as pattern identifier
Energy Technology Data Exchange (ETDEWEB)
Reynoso V, M.R.; Vega C, J.J. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)
2003-07-01
A novel way to extract relevant parameters associated with the outgoing ions from nuclear reactions, obtained by digitizing the signals provided by a Bragg curve spectrometer (BCS) is presented. This allowed the implementation of a more thorough pulse-shape analysis. Due to the complexity of this task, it was required to take advantage of new and more powerful computational paradigms. This was fulfilled using a back-propagation artificial neural network (ANN) as a pattern identifier. Over training of ANNs is a common problem during the training stage. In the performance of the ANN there is a compromise between its size and the size of the training set. Here, this effect will be illustrated in relation to the problem of Bragg Curve (BC) identification. (Author)
The Sustainable Development Assessment of Reservoir Resettlement Based on a BP Neural Network
Huang, Li; Huang, Jian
2018-01-01
Resettlement affects not only the resettlers’ production activities and life but also, directly or indirectly, the normal operation of power stations, the sustainable development of the resettlers, and regional social stability. Therefore, a scientific evaluation index system for the sustainable development of reservoir resettlement must be established that fits Chinese national conditions and not only promotes reservoir resettlement research but also improves resettlement practice. This essay builds an evaluation index system for resettlers’ sustainable development based on a back-propagation (BP) neural network, which can be adopted in China, taking the resettlement necessitated by step hydropower stations along the Wujiang River cascade as an example. The assessment results show that the resettlement caused by step power stations along the Wujiang River is sustainable, and this evaluation supports the conclusion that national policies and regulations, which are undergoing constant improvement, and resettlement has increasingly improved. The results provide a reference for hydropower reservoir resettlement in developing countries. PMID:29346305
Intelligent visual recognition and classification of cork tiles with neural networks.
Georgieva, Antoniya; Jordanov, Ivan
2009-04-01
An intelligent machine vision system is investigated and used for pattern recognition and classification of seven different types of cork tiles. The system includes image acquisition with a charge-coupled device (CCD) camera, texture feature generation (co-occurrence matrices and Laws' masks), analysis and processing of the feature vectors [linear discriminant analysis (LDA) and principal component analysis (PCA)], and cork tiles classification with feedforward neural networks (NN), employing our GLP(tau) S (genetic low-discrepancy search) hybrid global optimization method. In addition, the same NN are trained with backpropagation (BP) and the obtained results are compared with the ones from GLP(tau) S . The NN generalization abilities are discussed and assessed with respect to the NN architectures and the texture feature sets. The reported results are very encouraging with testing rate reaching up to 95%.
DEFF Research Database (Denmark)
Bhowmik, Subrata; Weber, Felix; Høgsberg, Jan Becker
2013-01-01
This paper presents a systematic design and training procedure for the feed-forward backpropagation neural network (NN) modeling of both forward and inverse behavior of a rotary magnetorheological (MR) damper based on experimental data. For the forward damper model, with damper force as output...... an optimization procedure demonstrates accurate training of the NN architecture with only current and velocity as input states. For the inverse damper model, with current as output, the absolute value of velocity and force are used as input states to avoid negative current spikes when tracking a desired damper...... force. The forward and inverse damper models are trained and validated experimentally, combining a limited number of harmonic displacement records, and constant and half-sinusoidal current records. In general the validation shows accurate results for both forward and inverse damper models, where...
Directory of Open Access Journals (Sweden)
Redi Kharisman
2014-03-01
Full Text Available Sistem navigasi banyak digunakan sebagai pengenal objek dalam radius tertentu, penunjuk arah tujuan, penunjuk posisi objek berada dan sebagainya. Adakalanya sistem navigasi juga memiliki noise atau ketidakakuratan informasi dalam implementasinya sehingga membuat interpretasi terhadap posisi objek menjadi berbeda-beda pula. Dengan menganggap quadcopter sebagai suatu objek terbang dengan warna dominan, penentuan posisi dapat dilakukan proses pencitraan dengan prinsip Stereo Vision yang menggunakan dua buah kamera. Dengan kamera, proses pencitraan dapat dilakukan dengan mengetahui setiap perubahan posisi objek melalui titik tengah objek dengan proses trackingnya. Namun kamera memiliki noise yang dapat menyebabkan perubahan data yang cukup sering dalam setiap cuplikan datanya. Metode Backpropagation Neural Network digunakan sebagai estimator yang ditujukan agar dapat mengikuti pola data sekaligus juga memberikan posisi yang lebih akurat dengan mengeliminasi noise yang terdapat dalam pola data yang terekam. Sehingga dengan menggunakan metode tersebut, maka sebagian besar noise yang terjadi, tereliminasi dan grafik pergerakan lebih tampak nyata.
Application of artificial neural network and genetic algorithm in flow and transport simulations
Morshed, Jahangir; Kaluarachchi, Jagath J.
Artificial neural network (ANN) is considered to be a powerful tool for solving groundwater problems which require a large number of flow and contaminant transport (GFCT) simulations. Often, GFCT models are nonlinear, and they are difficult to solve using traditional numerical methods to simulate specific input-output responses. In order to avoid these difficulties, ANN may be used to simulate the GFCT responses explicitly. In this manuscript, recent research related to the application of ANN in simulating GFCT responses is critically reviewed, and six research areas are identified. In order to study these areas, a one-dimensional unsaturated flow and transport scenario was developed, and ANN was used to simulate the effects of specific GFCT parameters on overall results. Using these results, ANN concepts related to architecture, sampling, training, and multiple function approximations are studied, and ANN training using back-propagation algorithm (BPA) and genetic algorithm (GA) are compared. These results are summarized, and appropriate conclusions are made.