Quick fuzzy backpropagation algorithm.
Nikov, A; Stoeva, S
2001-03-01
A modification of the fuzzy backpropagation (FBP) algorithm called QuickFBP algorithm is proposed, where the computation of the net function is significantly quicker. It is proved that the FBP algorithm is of exponential time complexity, while the QuickFBP algorithm is of polynomial time complexity. Convergence conditions of the QuickFBP, resp. the FBP algorithm are defined and proved for: (1) single output neural networks in case of training patterns with different targets; and (2) multiple output neural networks in case of training patterns with equivalued target vector. They support the automation of the weights training process (quasi-unsupervised learning) establishing the target value(s) depending on the network's input values. In these cases the simulation results confirm the convergence of both algorithms. An example with a large-sized neural network illustrates the significantly greater training speed of the QuickFBP rather than the FBP algorithm. The adaptation of an interactive web system to users on the basis of the QuickFBP algorithm is presented. Since the QuickFBP algorithm ensures quasi-unsupervised learning, this implies its broad applicability in areas of adaptive and adaptable interactive systems, data mining, etc. applications.
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
Rainfall prediction with backpropagation method
Wahyuni, E. G.; Fauzan, L. M. F.; Abriyani, F.; Muchlis, N. F.; Ulfa, M.
2018-03-01
Rainfall is an important factor in many fields, such as aviation and agriculture. Although it has been assisted by technology but the accuracy can not reach 100% and there is still the possibility of error. Though current rainfall prediction information is needed in various fields, such as agriculture and aviation fields. In the field of agriculture, to obtain abundant and quality yields, farmers are very dependent on weather conditions, especially rainfall. Rainfall is one of the factors that affect the safety of aircraft. To overcome the problems above, then it’s required a system that can accurately predict rainfall. In predicting rainfall, artificial neural network modeling is applied in this research. The method used in modeling this artificial neural network is backpropagation method. Backpropagation methods can result in better performance in repetitive exercises. This means that the weight of the ANN interconnection can approach the weight it should be. Another advantage of this method is the ability in the learning process adaptively and multilayer owned on this method there is a process of weight changes so as to minimize error (fault tolerance). Therefore, this method can guarantee good system resilience and consistently work well. The network is designed using 4 input variables, namely air temperature, air humidity, wind speed, and sunshine duration and 3 output variables ie low rainfall, medium rainfall, and high rainfall. Based on the research that has been done, the network can be used properly, as evidenced by the results of the prediction of the system precipitation is the same as the results of manual calculations.
Conjugate descent formulation of backpropagation error in ...
African Journals Online (AJOL)
nique of backpropagation was popularized in a paper by Rumelhart, et al. ... the training of a multilayer neural network using a gradient descent approach applied to a .... superior convergence of the conjugate descent method over a standard ...
Equivalence of Equilibrium Propagation and Recurrent Backpropagation
Scellier, Benjamin; Bengio, Yoshua
2017-01-01
Recurrent Backpropagation and Equilibrium Propagation are algorithms for fixed point recurrent neural networks which differ in their second phase. In the first phase, both algorithms converge to a fixed point which corresponds to the configuration where the prediction is made. In the second phase, Recurrent Backpropagation computes error derivatives whereas Equilibrium Propagation relaxes to another nearby fixed point. In this work we establish a close connection between these two algorithms....
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 ...
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.
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...
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.
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.
Impairment mitigation in superchannels with digital backpropagation and MLSD
DEFF Research Database (Denmark)
Porto da Silva, Edson; Larsen, Knud J.; Zibar, Darko
2015-01-01
We assess numerically the performance of single-carrier digital backpropagation (SC-DBP) and maximum-likelihood sequence detection (MLSD) for DP-QPSK and DP-16QAM superchannel transmission over dispersion uncompensated links for three different cases of spectral shaping: optical pre-filtering of ...
Adaptive digital back-propagation for optical communication systems
Lin, C.-Y.; Napoli, A.; Spinnler, B.; Sleiffer, V.A.J.M.; Rafique, D.; Kuschnerov, M.; Bohn, M.; Schmauss, B.
2014-01-01
We propose an adaptive digital back-propagation method (A-DBP) to selfdetermine unknown fiber nonlinear coefficient gamma. Performance is experimentally verified with 10224-Gb/s POLMUX-16QAM over 656km. Optimal DBP performance, without knowledge of gamma, is obtained by A-DBP.
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.
Error-backpropagation in temporally encoded networks of spiking neurons
S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)
2000-01-01
textabstractFor a network of spiking neurons that encodes information in the timing of individual spike-times, we derive a supervised learning rule, emph{SpikeProp, akin to traditional error-backpropagation and show how to overcome the discontinuities introduced by thresholding. With this algorithm,
Single-step digital backpropagation for nonlinearity mitigation
DEFF Research Database (Denmark)
Secondini, Marco; Rommel, Simon; Meloni, Gianluca
2015-01-01
Nonlinearity mitigation based on the enhanced split-step Fourier method (ESSFM) for the implementation of low-complexity digital backpropagation (DBP) is investigated and experimentally demonstrated. After reviewing the main computational aspects of DBP and of the conventional split-step Fourier...... in the computational complexity, power consumption, and latency with respect to a simple feed-forward equalizer for bulk dispersion compensation....
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...
Kontrol Kecepatan Motor Induksi menggunakan Algoritma Backpropagation Neural Network
Directory of Open Access Journals (Sweden)
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
Ethnomathematics elements in Batik Bali using backpropagation method
Lestari, Mei; Irawan, Ari; Rahayu, Wanti; Wayan Parwati, Ni
2018-05-01
Batik is one of traditional arts that has been established by the UNESCO as Indonesia’s cultural heritage. Batik has varieties and motifs, and each motifs has its own uniqueness but seems similar, that makes it difficult to identify. This study aims to develop an application that can identify typical batik Bali with etnomatematics elements on it. Etnomatematics is a study that shows relation between culture and mathematics concepts. Etnomatematics in Batik Bali is more to geometrical concept in line of strong Balinese culture element. The identification process is use backpropagation method. Steps of backpropagation methods are image processing (including scalling and tresholding image process). Next step is insert the processed image to an artificial neural network. This study resulted an accuracy of identification of batik Bali that has Etnomatematics elements on it.
Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, Timothy P.; Cownden, Daniel; Tweed, Douglas B.; Akerman, Colin J.
2016-01-01
The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning. PMID:27824044
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Shen, Li; Lin, Zhouchen; Huang, Qingming
2015-01-01
Learning deeper convolutional neural networks becomes a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be gained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, that encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015...
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%.
Premature saturation in backpropagation networks: Mechanism and necessary conditions
International Nuclear Information System (INIS)
Vitela, J.E.; Reifman, J.
1997-01-01
The mechanism that gives rise to the phenomenon of premature saturation of the output units of feedforward multilayer neural networks during training with the standard backpropagation algorithm is described. The entire process of premature saturation is characterized by three distinct stages and it is concluded that the momentum term plays the leading role in the occurrence of the phenomenon. The necessary conditions for the occurrence of premature saturation are presented and a new method is proposed, based on these conditions, that eliminates the occurrence of the phenomenon. Validity of the conditions and the proposed method are illustrated through simulation results. Three case studies are presented. The first two came from a training session for classification of three component failures in a nuclear power plant. The last case, comes from a training session for classification of welded fuel elements
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.
Enhanced backpropagation training algorithm for transient event identification
International Nuclear Information System (INIS)
Vitela, J.; Reifman, J.
1993-01-01
We present an enhanced backpropagation (BP) algorithm for training feedforward neural networks that avoids the undesirable premature saturation of the network output nodes and accelerates the training process even in cases where premature saturation is not present. When the standard BP algorithm is applied to train patterns of nuclear power plant (NPP) transients, the network output nodes often become prematurely saturated causing the already slow rate of convergence of the algorithm to become even slower. When premature saturation occurs, the gradient of the prediction error becomes very small, although the prediction error itself is still large, yielding negligible weight updates and hence no significant decrease in the prediction error until the eventual recovery of the output nodes from saturation. By defining the onset of premature saturation and systematically modifying the gradient of the prediction error at saturation, we developed an enhanced BP algorithm that is compared with the standard BP algorithm in training a network to identify NPP transients
Conjugate descent formulation of backpropagation error in feedforward neural networks
Directory of Open Access Journals (Sweden)
NK Sharma
2009-06-01
Full Text Available The feedforward neural network architecture uses backpropagation learning to determine optimal weights between different 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 minimise the error function for a given set of patterns, by adjusting the weights of the network. The first derivates of the error with respect to the weights identify the local error surface in the descent direction. Hence the network exhibits a different local error surface for every different pattern presented to it, and weights are iteratively modified in order to minimise the current local error. The determination of an optimal weight vector is possible only when the total minimum error (mean of the minimum local errors for all patterns from the training set may be minimised. In this paper, we present a general mathematical formulation for the second derivative of the error function with respect to the weights (which represents a conjugate descent for arbitrary feedforward neural network topologies, and we use this derivative information to obtain the optimal weight vector. The local error is backpropagated among the units of hidden layers via the second order derivative of the error with respect to the weights of the hidden and output layers independently and also in combination. The new total minimum error point may be evaluated with the help of the current total minimum error and the current minimised local error. The weight modification processes is performed twice: once with respect to the present local error and once more with respect to the current total or mean error. We present some numerical evidence that our proposed method yields better network weights than those determined via a conventional gradient descent approach.
Klasifikasi Varietas Cabai Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network
Directory of Open Access Journals (Sweden)
Kharis Syaban
2016-07-01
Full Text Available Compared with other methods of classifiers such as cellular and molecular biological methods, using the image of the leaves become the first choice in the classification of plants. The leaves can be characterized by shape, color, and texture; The leaves can have a color that varies depending on the season and geographical location. In addition, the same plant species also can have different leaf shapes. In this study, the morphological features of leaves used to identify varieties of pepper plants. The method used to perform feature extraction is a moment invariant and basic geometric features. For the process of recognition based on the features that have been extracted, used neural network methods with backpropagation learning algorithm. From the neural-network training, the best accuracy in classifying varieties of chili with minimum error 0.001 by providing learning rate 0.1, momentum of 0.7, and 15 neurons in the hidden layer foreach of various feature. To conduct cross-validation testing with k-fold tehcnique, obtained classification accuracy to be range of 80.75%±0.09% with k=4.
Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation
Zhou, XueFei
2018-04-01
With the development of computer technology, the applications of machine learning are more and more extensive. And machine learning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks (CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machine learning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.
Habarulema, J. B.; McKinnell, L.-A.
2012-05-01
In this work, results obtained by investigating the application of different neural network backpropagation training algorithms are presented. This was done to assess the performance accuracy of each training algorithm in total electron content (TEC) estimations using identical datasets in models development and verification processes. Investigated training algorithms are standard backpropagation (SBP), backpropagation with weight delay (BPWD), backpropagation with momentum (BPM) term, backpropagation with chunkwise weight update (BPC) and backpropagation for batch (BPB) training. These five algorithms are inbuilt functions within the Stuttgart Neural Network Simulator (SNNS) and the main objective was to find out the training algorithm that generates the minimum error between the TEC derived from Global Positioning System (GPS) observations and the modelled TEC data. Another investigated algorithm is the MatLab based Levenberg-Marquardt backpropagation (L-MBP), which achieves convergence after the least number of iterations during training. In this paper, neural network (NN) models were developed using hourly TEC data (for 8 years: 2000-2007) derived from GPS observations over a receiver station located at Sutherland (SUTH) (32.38° S, 20.81° E), South Africa. Verification of the NN models for all algorithms considered was performed on both "seen" and "unseen" data. Hourly TEC values over SUTH for 2003 formed the "seen" dataset. The "unseen" dataset consisted of hourly TEC data for 2002 and 2008 over Cape Town (CPTN) (33.95° S, 18.47° E) and SUTH, respectively. The models' verification showed that all algorithms investigated provide comparable results statistically, but differ significantly in terms of time required to achieve convergence during input-output data training/learning. This paper therefore provides a guide to neural network users for choosing appropriate algorithms based on the availability of computation capabilities used for research.
Directory of Open Access Journals (Sweden)
Yusuf Dwi Santoso
2017-06-01
Full Text Available Kepribadian merupakan gambaran tingkah laku dari individu. Penerimaan teman sebaya merupakan penilaian individu bahwa dirinya diterima, didengar, diperhatikan, dihargai, serta dapat merasa aman dan nyaman saat bersama dengan teman-teman dengan umur yang sama. Kepribadian dan penerimaan teman sebaya penting untuk diketahui agar dapat mengenal potensi diri. Tes kepribadian merupakan salah satu sarana untuk mengetahui dan mengklasifikasikan kepribadian seseorang ke tipe kepribadian tertentu. Jaringan syaraf tiruan Backpropagation dapat digunakan untuk melakukan klasifikasi sebuah pola berdasarkan permasalahan tertentu seperti halnya dalam mengklasifikasi tipe kepribadian dan penerimaan teman sebaya seseorang. Sistem klasifikasi tipe kepribadian dan penerimaan teman sebaya menggunakan jaringan syaraf tiruan Backpropagation dapat digunakan untuk mengklasifikasi tipe kepribadian dan penerimaan teman sebaya seseorang ke dalam beberapa tipe yaitu introvert diterima, introvert ditolak, ekstrovert diterima dan ekstrovert ditolak berdasarkan sejumlah set pertanyaan yang menjadi alat ukur dalam penentuan kepribadian. Sistem klasifikasi tipe kepribadian dan penerimaan teman sebaya menggunakan jaringan syaraf tiruan Backpropagation menghasilkan arsitektur Backpropagation terbaik untuk klasifikasi kepribadian dan penerimaan teman sebaya pada saat menggunakan 1 hidden layer dengan 7 neuron, 10000 epoch, nilai target error 0.01 dan laju pembelajaran 0.1. Hasil eksperimen jaringan syaraf tiruan Backpropagation pada sistem ini menghasilkan rata-rata tingkat akurasi 98.75% dan tingkat error 1.25%.
Fast Back-Propagation Learning Using Steep Activation Functions and Automatic Weight
Tai-Hoon Cho; Richard W. Conners; Philip A. Araman
1992-01-01
In this paper, several back-propagation (BP) learning speed-up algorithms that employ the Ã£gainÃ¤ parameter, i.e., steepness of the activation function, are examined. Simulations will show that increasing the gain seemingly increases the speed of convergence and that these algorithms can converge faster than the standard BP learning algorithm on some problems. However,...
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.
The interchangeability of learning rate and gain in backpropagation neural networks
Thimm, G.; Moerland, P.; Fiesler, E.
1996-01-01
The backpropagation algorithm is widely used for training multilayer neural networks. In this publication the gain of its activation function(s) is investigated. In specific, it is proven that changing the gain of the activation function is equivalent to changing the learning rate and the weights.
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.
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.
Convergence of Batch Split-Complex Backpropagation Algorithm for Complex-Valued Neural Networks
Directory of Open Access Journals (Sweden)
Huisheng Zhang
2009-01-01
Full Text Available The batch split-complex backpropagation (BSCBP algorithm for training complex-valued neural networks is considered. For constant learning rate, it is proved that the error function of BSCBP algorithm is monotone during the training iteration process, and the gradient of the error function tends to zero. By adding a moderate condition, the weights sequence itself is also proved to be convergent. A numerical example is given to support the theoretical analysis.
Sriyanto, -; Sutedi, -
2010-01-01
Diabetes Melitus (DM) is dangerous disease that affect many of the various layer of work society. This disease is not easy to accurately recognized by the general society. So we need to develop a system that can identify accurately. System is built using neural networks with backpropagation methods and the function activation sigmoid. Neural network architecture using 8 input layer, 2 output layer and 5 hidden layer. The results show that this methods succesfully clasifies data diabetics and ...
Sutedi, Sutedi
2018-01-01
Diabetes Melitus (DM) is dangerous disease that affect many of the various layer of work society. This disease is not easy to accurately recognized by the general society. So we need to develop a system that can identify accurately. System is built using neural networks with backpropagation methods and the function activation sigmoid. Neural network architecture using 8 input layer, 2 output layer and 5 hidden layer. The results show that this methods succesfully clasifies data diabetics and ...
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...
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.
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Hesham Mostafa
2017-09-01
Full Text Available Artificial neural networks (ANNs trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.
Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert
2017-01-01
Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.
Zhou, Wen-Liang; Yan, Ping; Wuskell, Joseph P; Loew, Leslie M; Antic, Srdjan D
2008-02-01
Basal dendrites of neocortical pyramidal neurons are relatively short and directly attached to the cell body. This allows electrical signals arising in basal dendrites to strongly influence the neuronal output. Likewise, somatic action potentials (APs) should readily propagate back into the basilar dendritic tree to influence synaptic plasticity. Two recent studies, however, determined that sodium APs are severely attenuated in basal dendrites of cortical pyramidal cells, so that they completely fail in distal dendritic segments. Here we used the latest improvements in the voltage-sensitive dye imaging technique (Zhou et al., 2007) to study AP backpropagation in basal dendrites of layer 5 pyramidal neurons of the rat prefrontal cortex. With a signal-to-noise ratio of > 15 and minimal temporal averaging (only four sweeps) we were able to sample AP waveforms from the very last segments of individual dendritic branches (dendritic tips). We found that in short- (< 150 microm) and medium (150-200 microm in length)-range basal dendrites APs backpropagated with modest changes in AP half-width or AP rise-time. The lack of substantial changes in AP shape and dynamics of rise is inconsistent with the AP-failure model. The lack of substantial amplitude boosting of the third AP in the high-frequency burst also suggests that in short- and medium-range basal dendrites backpropagating APs were not severely attenuated. Our results show that the AP-failure concept does not apply in all basal dendrites of the rat prefrontal cortex. The majority of synaptic contacts in the basilar dendritic tree actually received significant AP-associated electrical and calcium transients.
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.
Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation
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Benjamin Scellier
2017-05-01
Full Text Available We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made and the second phase of training (after the target or prediction error is revealed. Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function. Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point or stationary distribution toward a configuration that reduces prediction error. In the case of a recurrent multi-layer supervised network, the output units are slightly nudged toward their target in the second phase, and the perturbation introduced at the output layer propagates backward in the hidden layers. We show that the signal “back-propagated” during this second phase corresponds to the propagation of error derivatives and encodes the gradient of the objective function, when the synaptic update corresponds to a standard form of spike-timing dependent plasticity. This work makes it more plausible that a mechanism similar to Backpropagation could be implemented by brains, since leaky integrator neural computation performs both inference and error back-propagation in our model. The only local difference between the two phases is whether synaptic changes are allowed or not. We also show experimentally that multi-layer recurrently connected networks with 1, 2, and 3 hidden layers can be trained by Equilibrium Propagation on the permutation-invariant MNIST
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.
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.
Chromatic characterization of a three-channel colorimeter using back-propagation neural networks
Pardo, P. J.; Pérez, A. L.; Suero, M. I.
2004-09-01
This work describes a method for the chromatic characterization of a three-channel colorimeter of recent design and construction dedicated to color vision research. The colorimeter consists of two fixed monochromators and a third monochromator interchangeable with a cathode ray tube or any other external light source. Back-propagation neural networks were used for the chromatic characterization to establish the relationship between each monochromator's input parameters and the tristimulus values of each chromatic stimulus generated. The results showed the effectiveness of this type of neural-network-based system for the chromatic characterization of the stimuli produced by any monochromator.
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Junta Zeniarja
2015-05-01
Full Text Available Pelanggan merupakan bagian penting dalam memastikan keunggulan dan kelangsungan hidup perusahaan. Oleh karena itu perlu untuk memiliki sistem manajemen untuk memastikan pelanggan tetap setia dan tidak pindah ke pesaing lain, yang dikenal sebagai manajemen churn. Prediksi churn pelanggan adalah bagian dari manajemen churn, yang memprediksi perilaku pelanggan dengan klasifikasi pelanggan setia dan mana yang cenderung pindah ke kompetitor lain. Keakuratan prediksi ini mutlak diperlukan karena tingginya tingkat migrasi pelanggan ke perusahaan pesaing. Hal ini penting karena biaya yang digunakan untuk meraih pelanggan baru jauh lebih tinggi dibandingkan dengan mempertahankan loyalitas pelanggan yang sudah ada. Meskipun banyak studi tentang prediksi churn pelanggan yang telah dilakukan, penelitian lebih lanjut masih diperlukan untuk meningkatkan akurasi prediksi. Penelitian ini akan membahas penggunaan teknik data mining Backpropagation Neural Network (BPNN in hybrid dengan Strategi Evolution (ES untuk atribut bobot. Validasi model dilakukan dengan menggunakan validasi Palang 10-Fold dan evaluasi pengukuran dilakukan dengan menggunakan matriks kebingungan dan Area bawah ROC Curve (AUC. Hasil percobaan menunjukkan bahwa hibrida BPNN dengan ES mencapai kinerja yang lebih baik daripada Basic BPNN. Kata kunci: data mining, churn, prediksi, backpropagation neural network, strategi evolusi.
STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL
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Muhammad Athoillah
2015-03-01
Full Text Available Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years classification method has been proven to help many peopleâ€™s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem. This framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM, K-Nearest Neighbor (K-NN, and Backpropagation, especially in study cases of image retrieval with five category of image dataset. The result shows that K-NN has the best average result in accuracy with 82%. It is also the fastest in average computation time with 17,99 second during retrieve session for all categories class. The Backpropagation, however, is the slowest among three of them. In average it needed 883 second for training session and 41,7 second for retrieve session.
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.
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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
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.
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
Desain Sistem Pendeteksi untuk Citra Base Sub-assembly dengan Algoritma Backpropagation
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Kasdianto Kasdianto
2017-04-01
Full Text Available Object identification technique using machine vision has been implemented in industrial of electronic manufacturers for years. This technique is commonly used for reject detection (for disqualified product based on existing standard or defect detection. This research aims to build a reject detector of sub-assembly condition which is differed by two conditions that are missing screw and wrong position screw using neural network backpropagation. The image taken using camera will be converted into grayscale before it is processed in backpropagation methods to generate a weight value. The experiment result shows that the network architecture with two layers has the most excellent accuracy level. Using learning rate of 0.5, target error 0.015%, and the number of node 1 of 100 and node 2 of 50, the successive rate for sub-assembly detection in right condition reached 99.02% while no error occurs in detecting the wrong condition of Sub-assembly (missing screw and wrong position screw.
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.
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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mustamin hamid
2016-09-01
Full Text Available Abstract - This research proposed a system to identify Plasmodium falciparum on blood smear using the neural network backpropagation. Modified K-Means (MK-Means is used to separate between the object with the background image because that method was able to equalize the value of fitness at all Center cluster so there is no dead center and can also cope with the local minimum value. The extraction of the features used in this study consists of color features i.e. calculation of the mean, standard deviation, skewness, curtosis and entropy of co-occurent matrix with the purpose to get the values of all the trait value image, obtained are then used to train a neural network with the backpropagation training algorithm. Method of backpropagation networks capable of acquiring knowledge even though there is no certainty, able to perform a generalization and extraction of a specific data pattern. The image of the preparations blood smear are classified using the method of neural network Backpropagation. The test results obtained from Tropozoit with the accuracy 100%, scizon 80% and gametocytes 80%. Identification is then obtained outcomes the introduction with an average accuracy of 86,66%.
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.
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.
Autonomous path planning solution for industrial robot manipulator using backpropagation algorithm
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PeiJiang Yuan
2015-12-01
Full Text Available Here, we propose an autonomous path planning solution using backpropagation algorithm. The mechanism of movement used by humans in controlling their arms is analyzed and then applied to control a robot manipulator. Autonomous path planning solution is a numerical method. The model of industrial robot manipulator used in this article is a KUKA KR 210 R2700 EXTRA robot. In order to show the performance of the autonomous path planning solution, an experiment validation of path tracking is provided. Experiment validation consists of implementation of the autonomous path planning solution and the control of physical robot. The process of converging to target solution is provided. The mean absolute error of position for tool center point is also analyzed. Comparison between autonomous path planning solution and the numerical methods based on Newton–Raphson algorithm is provided to demonstrate the efficiency and accuracy of the autonomous path planning solution.
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.
Backpropagation architecture optimization and an application in nuclear power plant diagnostics
International Nuclear Information System (INIS)
Basu, A.; Bartlett, E.B.
1993-01-01
This paper presents a Dynamic Node Architecture (DNA) scheme to optimize the architecture of backpropagation Artificial Neural Networks (ANNs). This network scheme is used to develop an ANN based diagnostic adviser capable of identifying the operating status of a nuclear power plant. Specifically, a ''root'' network is trained to diagnose if the plant is in a normal operating condition or not. In the event of an abnormal condition, and other ''classifier'' network is trained to recognize the particular transient taking place. these networks are trained using plant instrumentation data gathered during simulations of the various transients and normal operating conditions at the Iowa Electric Light and Power Company's Duane Arnold Energy Center (DAEC) operator training simulator
Backpropagation architecture optimization and an application in nuclear power plant diagnostics
International Nuclear Information System (INIS)
Basu, A.; Bartlett, E.B.
1993-01-01
This paper presents a Dynamic Node Architecture (DNA) scheme to optimize the architecture of backpropagation Artificial Neural Networks (ANNs). This network scheme is used to develop an ANN based diagnostic adviser capable of identifying the operating status of a nuclear power plant. Specifically, a root network is trained to diagnose if the plant is in a normal operating condition or not. In the event of an abnormal condition, another classifier network is trained to recognize the particular transient taking place. These networks are trained using plant instrumentation data gathered during simulations of the various transients and normal operating conditions at, the Iowa Electric Light and Power Company's Duane Arnold Energy Center (DAEC) operator training simulator
Trainable hardware for dynamical computing using error backpropagation through physical media.
Hermans, Michiel; Burm, Michaël; Van Vaerenbergh, Thomas; Dambre, Joni; Bienstman, Peter
2015-03-24
Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.
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.
Class of backpropagation techniques for limited-angle reconstruction in microwave tomography
International Nuclear Information System (INIS)
Paladhi, P. Roy; Tayebi, A.; Udpa, L.; Udpa, S.; Sinha, A.
2015-01-01
Filtered backpropagation (FBPP) is a well-known technique used in Diffraction Tomography (DT). For accurate reconstruction using FBPP, full 360° angular coverage is necessary. However, it has been shown that using some inherent redundancies in the projection data in a tomographic setup, accurate reconstruction is still possible with 270° coverage which is called the minimal-scan angle range. This can be done by applying weighing functions (or filters) on projection data of the object to eliminate the redundancies and accurately reconstruct the image from 270° coverage. This paper demonstrates procedures to generate many general classes of these weighing filters. These are all equivalent at 270° coverage but vary in performance at lower angular coverages and in presence of noise. This paper does a comparative analysis of different filters when angular coverage is lower than minimal-scan angle of 270°. Simulation studies have been done to find optimum weight filters for sub-minimal angular coverage (<270°)
Backpropagating Action Potentials Enable Detection of Extrasynaptic Glutamate by NMDA Receptors
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Yu-Wei Wu
2012-05-01
Full Text Available Synaptic NMDA receptors (NMDARs are crucial for neural coding and plasticity. However, little is known about the adaptive function of extrasynaptic NMDARs occurring mainly on dendritic shafts. Here, we find that in CA1 pyramidal neurons, backpropagating action potentials (bAPs recruit shaft NMDARs exposed to ambient glutamate. In contrast, spine NMDARs are “protected,” under baseline conditions, from such glutamate influences by perisynaptic transporters: we detect bAP-evoked Ca2+ entry through these receptors upon local synaptic or photolytic glutamate release. During theta-burst firing, NMDAR-dependent Ca2+ entry either downregulates or upregulates an h-channel conductance (Gh of the cell depending on whether synaptic glutamate release is intact or blocked. Thus, the balance between activation of synaptic and extrasynaptic NMDARs can determine the sign of Gh plasticity. Gh plasticity in turn regulates dendritic input probed by local glutamate uncaging. These results uncover a metaplasticity mechanism potentially important for neural coding and memory formation.
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.
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)
Inversion of Density Interfaces Using the Pseudo-Backpropagation Neural Network Method
Chen, Xiaohong; Du, Yukun; Liu, Zhan; Zhao, Wenju; Chen, Xiaocheng
2018-05-01
This paper presents a new pseudo-backpropagation (BP) neural network method that can invert multi-density interfaces at one time. The new method is based on the conventional forward modeling and inverse modeling theories in addition to conventional pseudo-BP neural network arithmetic. A 3D inversion model for gravity anomalies of multi-density interfaces using the pseudo-BP neural network method is constructed after analyzing the structure and function of the artificial neural network. The corresponding iterative inverse formula of the space field is presented at the same time. Based on trials of gravity anomalies and density noise, the influence of the two kinds of noise on the inverse result is discussed and the scale of noise requested for the stability of the arithmetic is analyzed. The effects of the initial model on the reduction of the ambiguity of the result and improvement of the precision of inversion are discussed. The correctness and validity of the method were verified by the 3D model of the three interfaces. 3D inversion was performed on the observed gravity anomaly data of the Okinawa trough using the program presented herein. The Tertiary basement and Moho depth were obtained from the inversion results, which also testify the adaptability of the method. This study has made a useful attempt for the inversion of gravity density interfaces.
Directory of Open Access Journals (Sweden)
Anne-Kathrin Theis
2018-04-01
Full Text Available The majority of excitatory synapses are located on dendritic spines of cortical glutamatergic neurons. In spines, compartmentalized Ca2+ signals transduce electrical activity into specific long-term biochemical and structural changes. Action potentials (APs propagate back into the dendritic tree and activate voltage gated Ca2+ channels (VGCCs. For spines, this global mode of spine Ca2+ signaling is a direct biochemical feedback of suprathreshold neuronal activity. We previously demonstrated that backpropagating action potentials (bAPs result in long-term enhancement of spine VGCCs. This activity-dependent VGCC plasticity results in a large interspine variability of VGCC Ca2+ influx. Here, we investigate how spine VGCCs affect glutamatergic synaptic transmission. We combined electrophysiology, two-photon Ca2+ imaging and two-photon glutamate uncaging in acute brain slices from rats. T- and R-type VGCCs were the dominant depolarization-associated Ca2+conductances in dendritic spines of excitatory layer 2 neurons and do not affect synaptic excitatory postsynaptic potentials (EPSPs measured at the soma. Using two-photon glutamate uncaging, we compared the properties of glutamatergic synapses of single spines that express different levels of VGCCs. While VGCCs contributed to EPSP mediated Ca2+ influx, the amount of EPSP mediated Ca2+ influx is not determined by spine VGCC expression. On a longer timescale, the activation of VGCCs by bAP bursts results in downregulation of spine NMDAR function.
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
Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.
Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios
2017-01-01
An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.
Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines
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Emre O. Neftci
2017-06-01
Full Text Available An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.
Analisa Estimasi Penyeleksian Dosen Menggunakan Metode Backpropagation (Studi Kasus STMIK Amik Riau
Directory of Open Access Journals (Sweden)
Debi Setiawan
2016-12-01
Full Text Available Abstract- The purpose of this study was to estimate the number of lecturers who will be selected at the end of the year. Selection of lecturers is a way to determine the productivity of lecturers. In Act No. 14 of 2005, article 67, explained that an institution can undertake unilateral termination respectfully, when it ended joint working relationship between lecturers and education providers. At private colleges, the trigger of the selection of lecturers due to the productivity and the number of students. If the number of students is insufficient faculty ratio, while increasing productivity and excellent faculty, it still will do the selection of lecturers. The problem that arises is the imbalance for lecturers who have improved performance. It is necessary for analysis of estimates in the process of selecting lecturers, lecturers and institutions in order to be able to take a stand to solve this problem. Estimation using the design pattern of artificial neural networks (ANN and methods of propagation, with an error rate of 0.5%. Variables that will be used is the amount of students majoring in IT (Computer Science, MI (Management Information on campus STMIK Amik Riau, the number of Lecturer (MI and TI, and the final value of faculty productivity. These five variables will be processed on the system of selecting lecturers analysis using backpropagation method, so that the results to be obtained is the number of lecturers who will be affected by the selection of lecturers on campus STMIK Amik Riau.
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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
Huang, Daizheng; Wu, Zhihui
2017-01-01
Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.
Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, En; Deng, Kaiwen; Bondinuba, Francis Kwesi
2017-03-01
Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R 2 ) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model. Copyright © 2016 Elsevier Ltd. All rights reserved.
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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.
Energy Technology Data Exchange (ETDEWEB)
Li Yingwei [Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Peng Jinhui, E-mail: jhpeng@kmust.edu.c [Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Liu Bingguo [Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Li Wei [Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Huang Daifu [No. 272 Nuclear Industry Factory, China National Nuclear Corporation, Hengyang, Hunan Province 421002 (China); Zhang Libo [Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China)
2011-05-15
Research highlights: The incremental improved Back-Propagation neural network prediction model using the Levenberg-Marquardt algorithm based on optimizing theory is put forward. The prediction model of the nonlinear system is built, which can effectively predict the experiment of microwave calcining of ammonium uranyl carbonate (AUC). AUC can accept the microwave energy and microwave heating can quickly decompose AUC. In the experiment of microwave calcining of AUC, the contents of U and U{sup 4+} increased with increasing of microwave power and irradiation time, and decreased with increasing of the material average depth. - Abstract: The incremental improved Back-Propagation (BP) neural network prediction model was put forward, which was very useful in overcoming the problems, such as long testing cycle, high testing quantity, difficulty of optimization for process parameters, many training data probably were offered by the way of increment batch and the limitation of the system memory could make the training data infeasible, which existed in the process of calcinations for ammonium uranyl carbonate (AUC) by microwave heating. The prediction model of the nonlinear system was built, which could effectively predict the experiment of microwave calcining of AUC. The predicted results indicated that the contents of U and U{sup 4+} were increased with increasing of microwave power and irradiation time, and decreased with increasing of the material average depth.
International Nuclear Information System (INIS)
Li Yingwei; Peng Jinhui; Liu Bingguo; Li Wei; Huang Daifu; Zhang Libo
2011-01-01
Research highlights: → The incremental improved Back-Propagation neural network prediction model using the Levenberg-Marquardt algorithm based on optimizing theory is put forward. → The prediction model of the nonlinear system is built, which can effectively predict the experiment of microwave calcining of ammonium uranyl carbonate (AUC). → AUC can accept the microwave energy and microwave heating can quickly decompose AUC. → In the experiment of microwave calcining of AUC, the contents of U and U 4+ increased with increasing of microwave power and irradiation time, and decreased with increasing of the material average depth. - Abstract: The incremental improved Back-Propagation (BP) neural network prediction model was put forward, which was very useful in overcoming the problems, such as long testing cycle, high testing quantity, difficulty of optimization for process parameters, many training data probably were offered by the way of increment batch and the limitation of the system memory could make the training data infeasible, which existed in the process of calcinations for ammonium uranyl carbonate (AUC) by microwave heating. The prediction model of the nonlinear system was built, which could effectively predict the experiment of microwave calcining of AUC. The predicted results indicated that the contents of U and U 4+ were increased with increasing of microwave power and irradiation time, and decreased with increasing of the material average depth.
Directory of Open Access Journals (Sweden)
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.
DEFF Research Database (Denmark)
Porto da Silva, Edson; Yankov, Metodi Plamenov; Da Ros, Francesco
2016-01-01
Gains in achievable information rates from probabilistic shaping and digital backpropagation are compared for WDM transmission of 5 × 10 GBd DP-256QAM/1024QAM up to 1700 km of reach. The combination of both techniques its shown to provide gains of up to ∼0.5 bits/QAM symbol...
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.
Motta, Mario; Zhang, Shiwei
2017-11-14
We address the computation of ground-state properties of chemical systems and realistic materials within the auxiliary-field quantum Monte Carlo method. The phase constraint to control the Fermion phase problem requires the random walks in Slater determinant space to be open-ended with branching. This in turn makes it necessary to use back-propagation (BP) to compute averages and correlation functions of operators that do not commute with the Hamiltonian. Several BP schemes are investigated, and their optimization with respect to the phaseless constraint is considered. We propose a modified BP method for the computation of observables in electronic systems, discuss its numerical stability and computational complexity, and assess its performance by computing ground-state properties in several molecular systems, including small organic molecules.
Wavefield back-propagation in high-resolution X-ray holography with a movable field of view.
Guehrs, Erik; Günther, Christian M; Pfau, Bastian; Rander, Torbjörn; Schaffert, Stefan; Schlotter, William F; Eisebitt, Stefan
2010-08-30
Mask-based Fourier transform holography is used to record images of biological objects with 2.2 nm X-ray wavelength. The holography mask and the object are decoupled from each other which allows us to move the field of view over a large area over the sample. Due to the separation of the mask and the sample on different X-ray windows, a gap between both windows in the micrometer range typically exists. Using standard Fourier transform holography, focussed images of the sample can directly be reconstructed only for gap distances within the setup's depth of field. Here, we image diatoms as function of the gap distance and demonstrate the possibility to recover focussed images via a wavefield back-propagation technique. The limitations of our approach with respect to large separations are mainly associated with deviations from flat-field illumination of the object.
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.
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Wijayanti Nurul Khotimah
2017-01-01
Full Text Available Sign Language recognition was used to help people with normal hearing communicate effectively with the deaf and hearing-impaired. Based on survey that conducted by Multi-Center Study in Southeast Asia, Indonesia was on the top four position in number of patients with hearing disability (4.6%. Therefore, the existence of Sign Language recognition is important. Some research has been conducted on this field. Many neural network types had been used for recognizing many kinds of sign languages. However, their performance are need to be improved. This work focuses on the ASL (Alphabet Sign Language in SIBI (Sign System of Indonesian Language which uses one hand and 26 gestures. Here, thirty four features were extracted by using Leap Motion. Further, a new method, Rule Based-Backpropagation Genetic Al-gorithm Neural Network (RB-BPGANN, was used to recognize these Sign Languages. This method is combination of Rule and Back Propagation Neural Network (BPGANN. Based on experiment this pro-posed application can recognize Sign Language up to 93.8% accuracy. It was very good to recognize large multiclass instance and can be solution of overfitting problem in Neural Network algorithm.
Directory of Open Access Journals (Sweden)
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.
Ma, Jianshe; Cai, Jinzhang; Lin, Guanyang; Chen, Huilin; Wang, Xianqin; Wang, Xianchuan; Hu, Lufeng
2014-05-15
Corynoxeine(CX), isolated from the extract of Uncaria rhynchophylla, is a useful and prospective compound in the prevention and treatment for vascular diseases. A simple and selective liquid chromatography mass spectrometry (LC-MS) method was developed to determine the concentration of CX in rat plasma. The chromatographic separation was achieved on a Zorbax SB-C18 (2.1 mm × 150 mm, 5 μm) column with acetonitrile-0.1% formic acid in water as mobile phase. Selective ion monitoring (SIM) mode was used for quantification using target ions m/z 383 for CX and m/z 237 for the carbamazepine (IS). After the LC-MS method was validated, it was applied to a back-propagation artificial neural network (BP-ANN) pharmacokinetic model study of CX in rats. The results showed that after intravenous administration of CX, it was mainly distributed in blood and eliminated quickly, t1/2 was less than 1h. The predicted concentrations generated by BP-ANN model had a high correlation coefficient (R>0.99) with experimental values. The developed BP-ANN pharmacokinetic model can be used to predict the concentration of CX in rats. Copyright © 2014 Elsevier B.V. All rights reserved.
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Salim Lahmiri
2014-07-01
Full Text Available This paper presents a forecasting model that integrates the discrete wavelet transform (DWT and backpropagation neural networks (BPNN for predicting financial time series. The presented model first uses the DWT to decompose the financial time series data. Then, the obtained approximation (low-frequency and detail (high-frequency components after decomposition of the original time series are used as input variables to forecast future stock prices. Indeed, while high-frequency components can capture discontinuities, ruptures and singularities in the original data, low-frequency components characterize the coarse structure of the data, to identify the long-term trends in the original data. As a result, high-frequency components act as a complementary part of low-frequency components. The model was applied to seven datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model that uses only low-frequency components. In addition, the presented model outperforms both the well-known auto-regressive moving-average (ARMA model and the random walk (RW process.
Connelly, William M; Crunelli, Vincenzo; Errington, Adam C
2017-05-24
Backpropagating action potentials (bAPs) are indispensable in dendritic signaling. Conflicting Ca 2+ -imaging data and an absence of dendritic recording data means that the extent of backpropagation in thalamocortical (TC) and thalamic reticular nucleus (TRN) neurons remains unknown. Because TRN neurons signal electrically through dendrodendritic gap junctions and possibly via chemical dendritic GABAergic synapses, as well as classical axonal GABA release, this lack of knowledge is problematic. To address this issue, we made two-photon targeted patch-clamp recordings from rat TC and TRN neuron dendrites to measure bAPs directly. These recordings reveal that "tonic"' and low-threshold-spike (LTS) "burst" APs in both cell types are always recorded first at the soma before backpropagating into the dendrites while undergoing substantial distance-dependent dendritic amplitude attenuation. In TC neurons, bAP attenuation strength varies according to firing mode. During LTS bursts, somatic AP half-width increases progressively with increasing spike number, allowing late-burst spikes to propagate more efficiently into the dendritic tree compared with spikes occurring at burst onset. Tonic spikes have similar somatic half-widths to late burst spikes and undergo similar dendritic attenuation. In contrast, in TRN neurons, AP properties are unchanged between LTS bursts and tonic firing and, as a result, distance-dependent dendritic attenuation remains consistent across different firing modes. Therefore, unlike LTS-associated global electrical and calcium signals, the spatial influence of bAP signaling in TC and TRN neurons is more restricted, with potentially important behavioral-state-dependent consequences for synaptic integration and plasticity in thalamic neurons. SIGNIFICANCE STATEMENT In most neurons, action potentials (APs) initiate in the axosomatic region and propagate into the dendritic tree to provide a retrograde signal that conveys information about the level of
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Song Chul-Gyu
2011-08-01
Full Text Available Abstract Background Radiological scoring methods such as colon transit time (CTT have been widely used for the assessment of bowel motility. However, these radiograph-based methods need cumbersome radiological instruments and their frequent exposure to radiation. Therefore, a non-invasive estimation algorithm of bowel motility, based on a back-propagation neural network (BPNN model of bowel sounds (BS obtained by an auscultation, was devised. Methods Twelve healthy males (age: 24.8 ± 2.7 years and 6 patients with spinal cord injury (6 males, age: 55.3 ± 7.1 years were examined. BS signals generated during the digestive process were recorded from 3 colonic segments (ascending, descending and sigmoid colon, and then, the acoustical features (jitter and shimmer of the individual BS segment were obtained. Only 6 features (J1, 3, J3, 3, S1, 2, S2, 1, S2, 2, S3, 2, which are highly correlated to the CTTs measured by the conventional method, were used as the features of the input vector for the BPNN. Results As a results, both the jitters and shimmers of the normal subjects were relatively higher than those of the patients, whereas the CTTs of the normal subjects were relatively lower than those of the patients (p k-fold cross validation, the correlation coefficient and mean average error between the CTTs measured by a conventional radiograph and the values estimated by our algorithm were 0.89 and 10.6 hours, respectively. Conclusions The jitter and shimmer of the BS signals generated during the peristalsis could be clinically useful for the discriminative parameters of bowel motility. Also, the devised algorithm showed good potential for the continuous monitoring and estimation of bowel motility, instead of conventional radiography, and thus, it could be used as a complementary tool for the non-invasive measurement of bowel motility.
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
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Aleksius Madu
2016-10-01
Full Text Available The purpose of this study is to predict the number of traffic accident victims who died in Timor Tengah Regency with Trend Projection method and Backpropagation method, and compare the two methods based on the degree of guilt and predict the number traffic accident victims in the Timor Tengah Regency for the coming year. This research was conducted in Timor Tengah Regency where data used in this study was obtained from Police Unit in Timor Tengah Regency. The data is on the number of traffic accidents in Timor Tengah Regency from 2000 – 2013, which is obtained by a quantitative analysis with Trend Projection and Backpropagation method. The results of the data analysis predicting the number of traffic accidents victims using Trend Projection method obtained the best model which is the quadratic trend model with equation Yk = 39.786 + (3.297 X + (0.13 X2. Whereas by using back propagation method, it is obtained the optimum network that consists of 2 inputs, 3 hidden screens, and 1 output. Based on the error rates obtained, Back propagation method is better than the Trend Projection method which means that the predicting accuracy with Back propagation method is the best method to predict the number of traffic accidents victims in Timor Tengah Regency. Thus obtained predicting the numbers of traffic accident victims for the next 5 years (Years 2014-2018 respectively - are 106 person, 115 person, 115 person, 119 person and 120 person. Keywords: Trend Projection, Back propagation, Predicting.
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.
Blanco Iturralde, David Roberto; Chávez Sánchez, Juan Daniel
2012-01-01
Al desarrollar el sistema basados en las técnicas y métodos anteriormente descritos se considero el lenguaje de programación C Sharp para la creación de la interfaz del sistema por las características que brinda en el manejo de imágenes a través de sus librerías que son apropiadas para este tipo de trabajo y Matlab para la red neuronal backpropagation y cálculos matemáticos complejos por ser un lenguaje nativo en la resolución de problemas matemáticos obteniendo así un sistema robusto y de al...
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.
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.
Wisesty, Untari N.; Warastri, Riris S.; Puspitasari, Shinta Y.
2018-03-01
Cancer is one of the major causes of mordibility and mortality problems in the worldwide. Therefore, the need of a system that can analyze and identify a person suffering from a cancer by using microarray data derived from the patient’s Deoxyribonucleic Acid (DNA). But on microarray data has thousands of attributes, thus making the challenges in data processing. This is often referred to as the curse of dimensionality. Therefore, in this study built a system capable of detecting a patient whether contracted cancer or not. The algorithm used is Genetic Algorithm as feature selection and Momentum Backpropagation Neural Network as a classification method, with data used from the Kent Ridge Bio-medical Dataset. Based on system testing that has been done, the system can detect Leukemia and Colon Tumor with best accuracy equal to 98.33% for colon tumor data and 100% for leukimia data. Genetic Algorithm as feature selection algorithm can improve system accuracy, which is from 64.52% to 98.33% for colon tumor data and 65.28% to 100% for leukemia data, and the use of momentum parameters can accelerate the convergence of the system in the training process of Neural Network.
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.
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.
Directory of Open Access Journals (Sweden)
Hyung-Suk Han
2012-12-01
Full Text Available The indoor noise of a ship is usually determined using the A-weighted sound pressure level. However, in order to better understand this phenomenon, evaluation parameters that more accurately reflect the human sense of hearing are required. To find the level of the satisfaction index of the noise inside a naval vessel such as “Loudness” and “Annoyance”, psycho-acoustic evaluation of various sound recordings from the naval vessel was performed in a laboratory. The objective of this paper is to develop a single index of “Loudness” and “Annoyance” for noise inside a naval vessel according to a psycho-acoustic evaluation by using psychological responses such as Noise Rating (NR, Noise Criterion (NC, Room Criterion (RC, Preferred Speech Interference Level (PSIL and loudness level. Additionally, in order to determine a single index of satisfaction for noise such as “Loudness” and “Annoyance”, with respect to a human's sense of hearing, a back-propagation neural network is applied.
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.
Wang, Zheng; Mao, Zhihua; Xia, Junshi; Du, Peijun; Shi, Liangliang; Huang, Haiqing; Wang, Tianyu; Gong, Fang; Zhu, Qiankun
2018-06-01
The cloud cover for the South China Sea and its coastal area is relatively large throughout the year, which limits the potential application of optical remote sensing. A HJ-charge-coupled device (HJ-CCD) has the advantages of wide field, high temporal resolution, and short repeat cycle. However, this instrument suffers from its use of only four relatively low-quality bands which can't adequately resolve the features of long wavelengths. The Landsat Enhanced Thematic Mapper-plus (ETM+) provides high-quality data, however, the Scan Line Corrector (SLC) stopped working and caused striping of remote sensed images, which dramatically reduced the coverage of the ETM+ data. In order to combine the advantages of the HJ-CCD and Landsat ETM+ data, we adopted a back-propagation artificial neural network (BP-ANN) to fuse these two data types for this study. The results showed that the fused output data not only have the advantage of data intactness for the HJ-CCD, but also have the advantages of the multi-spectral and high radiometric resolution of the ETM+ data. Moreover, the fused data were analyzed qualitatively, quantitatively and from a practical application point of view. Experimental studies indicated that the fused data have a full spatial distribution, multi-spectral bands, high radiometric resolution, a small difference between the observed and fused output data, and a high correlation between the observed and fused data. The excellent performance in its practical application is a further demonstration that the fused data are of high quality.
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.
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.
Directory of Open Access Journals (Sweden)
Xianzhi Song
Full Text Available 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
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.
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 ... as seen from the recent food crisis demonstra- tion in most .... layers by using Geographical Information System.
Ocean wave parameters estimation using backpropagation neural networks
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; SubbaRao; Raju, D.H.
: the RPROP algorithm. San Francisco: ICNN; 1993. p. 586–591. [15] Demuth H, Beale M. Neural network toolbox for use with MATLAB, user guide. USA: The Math Works Inc.; 2000 (http://www.mathworks.com). [16] Baba M, Dattatri J. Ocean wave spectra off cochin...
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.
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.
Yuniarto, Budi; Kurniawan, Robert
2017-03-01
PLS Path Modeling (PLS-PM) is different from covariance based SEM, where PLS-PM use an approach based on variance or component, therefore, PLS-PM is also known as a component based SEM. Multiblock Partial Least Squares (MBPLS) is a method in PLS regression which can be used in PLS Path Modeling which known as Multiblock PLS Path Modeling (MBPLS-PM). This method uses an iterative procedure in its algorithm. This research aims to modify MBPLS-PM with Back Propagation Neural Network approach. The result is MBPLS-PM algorithm can be modified using the Back Propagation Neural Network approach to replace the iterative process in backward and forward step to get the matrix t and the matrix u in the algorithm. By modifying the MBPLS-PM algorithm using Back Propagation Neural Network approach, the model parameters obtained are relatively not significantly different compared to model parameters obtained by original MBPLS-PM algorithm.
Wiryadinata, Romi; Ratnawati, Dwi Ana
2005-01-01
Motor DC dan komputer banyak digunakan dalam kehidupan sehari-sehari, baik di rumah tangga,industri maupun lingkungan pendidikan yang sangat membutuhkan ketelitian dan penggunan yang serbaotomatis. Jaringan Syaraf Tiruan merupakan salah satu kendali motor DC yang dapat disimulasikanmenggunakan neural network toolbox pada software Matlab 6.5. Dengan menggunakan metode Backpropagationdan fungsi Gradient Descent Momentum diperoleh struktur jaringan yang terbaik, terdiri dari 5 sel neuronlapisan ...
Directory of Open Access Journals (Sweden)
Z Abidin
2013-07-01
Full Text Available Di dalam kehidupan sehari-hari, khususnya dalam komunikasi interpersonal, wajah sering digunakan untuk berekspresi. Melalui ekspresi wajah, maka dapat dipahami emosi yang sedang bergejolak pada diri individu. Ekspresi wajah merupakan salah satu karakteristik perilaku. Penggunaan sistem teknologi biometrika dengan karakteristik ekspresi wajah memungkinkan untuk mengenali mood atau emosi seseorang. Komponen dasar sistem analisis ekspresi wajah adalah deteksi wajah, ekstraksi data wajah, dan pengenalan ekspresi wajah. Sehingga untuk membangun sebuah sistem pengenal ekspersi wajah, maka perlu dirancang tiga buah sub sistem yaitu sistem deteksi wajah, sistem pembelajaran jaringan syaraf tiruan. Prinsipnya data wajah yang telah dideteksi, diolah menggunakan fisherface, yang selanjutnya hasilnya digunakan sebagai input untuk jaringan syaraf tiruan. Bobot yang dihasilkan pada saat proses pembelajaran jaringan syaraf tiruan inilah yang akan digunakan untuk pengenalan ekspresi wajah.Â In daily life, especially in interpersonal communication, face often used for express of emotions. Facial expressions are the facial changes in response to a personâ€™s internal emotional states. A facial expression is one of the behavioral characteristics. The use of facial expression characteristics enables to recognize of personâ€™s mood. Basic components of a facial expression analysis system are face detection, face data extraction, and facial expression recognition. So that, to build a facial expression recognition system, it should be designed three subsystems, namely face detection system, learning of neural network system, and facial expression recognition system itself. In principle, face data that has been successfully detected, then it will be constructed by fisherface, and the results of it will be used as an input of neural network. Afterwards, the weights of neural network learning will be used to recognize facial expression.
Axon-somatic back-propagation in detailed models of spinal alpha motoneurons
Directory of Open Access Journals (Sweden)
Pietro eBalbi
2015-02-01
Full Text Available Antidromic action potentials following distal stimulation of motor axons occasionally fail to invade the soma of alpha motoneurons in spinal cord, due to their passing through regions of high non-uniformity.Morphologically detailed conductance-based models of cat spinal alpha motoneurons have been developed, with the aim to reproduce and clarify some aspects of the electrophysiological behavior of the antidromic axon-somatic spike propagation. Fourteen 3D morphologically detailed somata and dendrites of cat spinal alpha motoneurons have been imported from an open-access web-based database of neuronal morphologies, NeuroMorpho.org, and instantiated in neurocomputational models. An axon hillock, an axonal initial segment and a myelinated axon are added to each model.By sweeping the diameter of the axonal initial segment (AIS and the axon hillock, as well as the maximal conductances of sodium channels at the AIS and at the soma, the developed models are able to show the relationships between different geometric and electrophysiological configurations and the voltage attenuation of the antidromically travelling wave.In particular, a greater than usually admitted sodium conductance at AIS is necessary and sufficient to overcome the dramatic voltage attenuation occurring during antidromic spike propagation both at the myelinated axon-AIS and at the AIS-soma transitions.
The performance of the backpropagation algorithm with varying slope of the activation function
International Nuclear Information System (INIS)
Bai Yanping; Zhang Haixia; Hao Yilong
2009-01-01
Some adaptations are proposed to the basic BP algorithm in order to provide an efficient method to non-linear data learning and prediction. In this paper, an adopted BP algorithm with varying slope of activation function and different learning rates is put forward. The results of experiment indicated that this algorithm can get very good performance of training. We also test the prediction performance of our adopted BP algorithm on 16 instances. We compared the test results to the ones of the BP algorithm with gradient descent momentum and an adaptive learning rate. The results indicate this adopted BP algorithm gives best performance (100%) for test example, which conclude this adopted BP algorithm produces a smoothed reconstruction that learns better to new prediction function values than the BP algorithm improved with momentum.
Directory of Open Access Journals (Sweden)
Yi-Qing Wang
2015-09-01
Full Text Available Recent years have seen a surge of interest in multilayer neural networks fueled by their successful applications in numerous image processing and computer vision tasks. In this article, we describe a C++ implementation of the stochastic gradient descent to train a multilayer neural network, where a fast and accurate acceleration of tanh(· is achieved with linear interpolation. As an example of application, we present a neural network able to deliver state-of-the-art performance in image demosaicing.
Predicting carbonate permeabilities from wireline logs using a back-propagation neural network
International Nuclear Information System (INIS)
Wiener, J.M.; Moll, R.F.; Rogers, J.A.
1991-01-01
This paper explores the applicability of using Neural Networks to aid in the determination of carbonate permeability from wireline logs. Resistivity, interval transit time, neutron porosity, and bulk density logs form Texaco's Stockyard Creek Oil field were used as input to a specially designed neural network to predict core permeabilities in this carbonate reservoir. Also of interest was the comparison of the neural network's results to those of standard statistical techniques. The process of developing the neural network for this problem has shown that a good understanding of the data is required when creating the training set from which the network learns. This network was trained to learn core permeabilities from raw and transformed log data using a hyperbolic tangent transfer function and a sum of squares global error function. Also, it required two hidden layers to solve this particular problem
Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei
2014-09-01
In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Infilling streamflow data using feed-forward back-propagation (BP ...
African Journals Online (AJOL)
Hydrological data (e.g. rainfall, river flow data) are used in water resource planning and management. Sometimes hydrological time series have gaps or are incomplete, or are not of good quality or are not of sufficient length. This problem seems to be more prevalent in developing countries than in developed countries.
Forecasting Urban Air Quality via a Back-Propagation Neural Network and a Selection Sample Rule
Directory of Open Access Journals (Sweden)
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.
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...
Reduced complexity digital back-propagation methods for optical communication systems
Napoli, A.; Maalej, Z.; Sleiffer, V.A.J.M.; Kuschnerov, M.; Rafique, D.; Timmers, E.; Spinnler, B.; Rahman, T.; Coelho, L.D.; Hanik, N.
2014-01-01
Next-generation optical communication systems will continue to push the ( bandwidth $cdot$ distance) product towards its physical limit. To address this enormous demand, the usage of digital signal processing together with advanced modulation formats and coherent detection has been proposed to
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...
Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines
Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios
2017-01-01
An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of net...
Matching algorithm of missile tail flame based on back-propagation neural network
Huang, Da; Huang, Shucai; Tang, Yidong; Zhao, Wei; Cao, Wenhuan
2018-02-01
This work presents a spectral matching algorithm of missile plume detection that based on neural network. The radiation value of the characteristic spectrum of the missile tail flame is taken as the input of the network. The network's structure including the number of nodes and layers is determined according to the number of characteristic spectral bands and missile types. We can get the network weight matrixes and threshold vectors through training the network using training samples, and we can determine the performance of the network through testing the network using the test samples. A small amount of data cause the network has the advantages of simple structure and practicality. Network structure composed of weight matrix and threshold vector can complete task of spectrum matching without large database support. Network can achieve real-time requirements with a small quantity of data. Experiment results show that the algorithm has the ability to match the precise spectrum and strong robustness.
International Nuclear Information System (INIS)
Taraglio, S.; Massaioli, F.
1995-08-01
A parallel implementation of a library to build and train Multi Layer Perceptrons via the Back Propagation algorithm is presented. The target machine is the SIMD massively parallel supercomputer Quadrics. Performance measures are provided on three different machines with different number of processors, for two network examples. A sample source code is given
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%.
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.
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…
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.
Yang, Tsung-Ming; Fan, Shu-Kai; Fan, Chihhao; Hsu, Nien-Sheng
2014-08-01
The purpose of this study is to establish a turbidity forecasting model as well as an early-warning system for turbidity management using rainfall records as the input variables. The Taipei Water Source Domain was employed as the study area, and ANOVA analysis showed that the accumulative rainfall records of 1-day Ping-lin, 2-day Ping-lin, 2-day Fei-tsui, 2-day Shi-san-gu, 2-day Tai-pin and 2-day Tong-hou were the six most significant parameters for downstream turbidity development. The artificial neural network model was developed and proven capable of predicting the turbidity concentration in the investigated catchment downstream area. The observed and model-calculated turbidity data were applied to developing the turbidity early-warning system. Using a previously determined turbidity as the threshold, the rainfall criterion, above which the downstream turbidity would possibly exceed this respective threshold turbidity, for the investigated rain gauge stations was determined. An exemplary illustration demonstrated the effectiveness of the proposed turbidity early-warning system as a precautionary alarm of possible significant increase of downstream turbidity. This study is the first report of the establishment of the turbidity early-warning system. Hopefully, this system can be applied to source water turbidity forecasting during storm events and provide a useful reference for subsequent adjustment of drinking water treatment operation.
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.
relevance to the practicing engineer. Such a scheme of updating parameters (connection weights in the case of neu- ral network) is not employed in the suggested ANN model; thus, the ANN model, where the parameters are estimated from only one-day data... term hourly tidal records. However, there are a number of points to be clarified in order to better understand the paper. This discussion focuses on the following four areas: (1) terminology used in the paper; (2) practical utility; (3) reliability...
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.
Kuniyil Ajith Singh, M.; Jaeger, M.; Frenz, M.; Steenbergen, Wiendelt
2017-01-01
Reflection artifacts caused by acoustic inhomogeneities constitute a major problem in epi-mode biomedical photoacoustic imaging. Photoacoustic transients from the skin and superficial optical absorbers traverse into the tissue and reflect off echogenic structures to generate reflection artifacts.
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.
Mason, Cindi; Twomey, Janet; Wright, David; Whitman, Lawrence
2018-01-01
As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field of study. As a result of this concentration on student retention, numerous studies have been conducted to identify, understand, and confirm…
Han, Hyung-Suk
2012-01-01
The indoor noise of a ship is usually determined using the A-weighted sound pressure level. However, in order to better understand this phenomenon, evaluation parameters that more accurately reflect the human sense of hearing are required. To find the level of the satisfaction index of the noise inside a naval vessel such as “Loudness” and “Annoyance”, psycho-acoustic evaluation of various sound recordings from the naval vessel was performed in a laboratory. The objective of this paper is to ...
DEFF Research Database (Denmark)
Kobayashi, T.; Takara, H.; Sano, A.
2013-01-01
We demonstrate 12-core fiber bidirectional long-haul transmission with sub-petabit-class capacity (2 × 344 Tb/s). Inter-core crosstalk management and multicarrier nonlinear compensation enabled the longest distance of 1500 km in SDM transmission with unidirectional capacity over 300 Tb/s....
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 es una de las areas de investigacion de mas rapido crecimiento en el campo de la IA y el reconocimiento de patrones. Este trabajo explora la posible aplicabilidad de una tecnica de reconocimiento de patrones basada en redes neuronales para la deteccion y la clasificacion de fallas en un SEP. Las tecnicas convencionales de deteccion en los relevadores modernos se basan en un procesamiento digital de senales y requieren de cierto tiempo (alrededor de 1 ciclo) para enviar una senal de disparo, ademas de ser propensas a tomar decisiones incorrectas si las senales se encuentran contaminadas por ruido. Es deseable entonces desarrollar tecnicas que sean rapidas, exactas y robustas y que tengan un buen desempeno ante las condiciones cambiantes del sistema (como variaciones de carga y resistencia de falla, por ejemplo). El objetivo de este trabajo es desarrollar una tecnica novedosa basada en Redes Neuronales Atificales (RNA), la cual explora la aplicabilidad de la propuesta de reconocimiento de patrones para el diagnostico y deteccion de fallas. La tecnica sugerida se basa en el hecho de que cuando ocurre una falla, toma lugar un cambio de impedancia en el sistema y como consecuencia, cambios en la amplitud y fase de las senales de voltajes y corrientes de linea toman lugar. Se desarrolla un discriminador de fallas basado en redes neuronales que es entrenado para detectar estos cambios como indicadores del instante de ocurrencia de la falla. Este detector utiliza valores instantaneos de esas senales para tomar decisiones. Se describe a detalle la aplicabilidad de las redes neuronales como clasificadores de patrones para el diagnostico de fallas en sistemas de transmision y ademas, se presenta un diseno basado en redes neuronales y su ambiente de simulacion para la deteccion y clasificacion de fallas en tiempo real. Se presentan resultados del desempeno de esta tecnica que muestran que es rapida, segura y suficientemente exacta e indican su aplicabilidad dentro de esquemas de deteccion y clasificacion de fallas a muy alta velocidad.
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, ...
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.
A study on the performance advancement of teat algorithm for defects in semiconductor packages
Energy Technology Data Exchange (ETDEWEB)
Kim, Jae Yeol; Kim, Chang Hyun; Yang, Dong Jo; Ko, Myung Soo [Chosun University, Gwangju (Korea, Republic of); You, Sin [Computer Added Mechanical Engineering, Mokpo Science College, Mokpo (Korea, Republic of)
2002-11-15
In this study, researchers classifying the artificial flaws in semiconductor packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method for entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, tile pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.
Mitigation of Linear and Nonlinear Impairments in Spectrally Efficient Superchannels
DEFF Research Database (Denmark)
Porto da Silva, Edson; Larsen, Knud J.; Zibar, Darko
2015-01-01
We assess numerically the performance of single-carrier digital backpropagation and maximum-likelihood sequence detection (MLSD) for DP-QPSK superchannel transmission. It is shown that MLSD is advantageous only against inter-carrier linear crosstalk....
A Hierarchical Approach to the Classification of Digital Modulation Types in Multipath Environments
National Research Council Canada - National Science Library
Fargues, M
2001-01-01
.... Back-propagation neural network units are adopted at each tree node because they offer the flexibility needed to cope with varying propagation environments, as is the case in real-world communications...
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.
Recurrent networks for wave forecasting
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Prabaharan, N.
, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper presents an application of the Artificial Neural Network, namely Backpropagation Recurrent Neural Network (BRNN) with rprop update algorithm for wave forecasting...
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.
High-resolution and super stacking of time-reversal mirrors in locating seismic sources
Cao, Weiping; Hanafy, Sherif M.; Schuster, Gerard T.; Zhan, Ge; Boonyasiriwat, Chaiwoot
2011-01-01
Time reversal mirrors can be used to backpropagate and refocus incident wavefields to their actual source location, with the subsequent benefits of imaging with high-resolution and super-stacking properties. These benefits of time reversal mirrors
Comparative performance of some popular artificial neural network ...
Indian Academy of Sciences (India)
tificial neural network domain (viz., local search algorithms, global search ... branches of astronomy for automated data analysis and other applications like ...... such as standard backpropagation, fuzzy logic, genetic algorithms, fractals etc.,.
Digital Repository Service at National Institute of Oceanography (India)
Patil, S.G.; Mandal, S.; Hegde, A.V.; Alavandar, S.
System (ANFIS), an implementation of a representative fuzzy inference system using a back-propagation neural network like structure, with limited mathematical representation of the system, is developed. An ANFIS is trained on the data set obtained from...
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....
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)
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
A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network
Mahmoudi, Fariborz; Mirzashaeri, Mohsen; Shahamatnia, Ehsan; Faridnia, Saed
This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. Evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.
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.
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.
Hybrid system prediction for the stock market: The case of transitional markets
Directory of Open Access Journals (Sweden)
Ralević Nebojša
2017-01-01
Full Text Available The subject of this paper is the creation and testing of an enhanced fuzzy neural network backpropagation model for the prediction of stock market indexes, including the comparison with the traditional neural network backpropagation model. The objective of the research is to gather information concerning the possibilities of using the enhanced fuzzy neural network backpropagation model for the prediction of stock market indexes focusing on transitional markets. The methodology used involves the integration of fuzzified weights into the neural network. The research results will be beneficial both for the broader investment community and the academia, in terms of the application of the enhanced model in the investment decision-making, as well as in improving the knowledge in this subject matter.
Using Genetic Algorithms in Secured Business Intelligence Mobile Applications
Directory of Open Access Journals (Sweden)
Silvia TRIF
2011-01-01
Full Text Available The paper aims to assess the use of genetic algorithms for training neural networks used in secured Business Intelligence Mobile Applications. A comparison is made between classic back-propagation method and a genetic algorithm based training. The design of these algorithms is presented. A comparative study is realized for determining the better way of training neural networks, from the point of view of time and memory usage. The results show that genetic algorithms based training offer better performance and memory usage than back-propagation and they are fit to be implemented on mobile devices.
DEFF Research Database (Denmark)
Prato, Carlo Giacomo; Gitelman, Victoria; Bekhor, Shlomo
2011-01-01
on 1,793 fatal traffic accidents occurred during the period between 2003 and 2006 and applies Kohonen and feed-forward back-propagation neural networks with the objective of extracting from the data typical patterns and relevant factors. Kohonen neural networks reveal five compelling accident patterns....... Feed-forward back-propagation neural networks indicate that sociodemographic characteristics of drivers and victims, accident location, and period of the day are extremely relevant factors. Accident patterns suggest that countermeasures are necessary for identified problems concerning mainly vulnerable...
DEFF Research Database (Denmark)
Asif, Rameez
2014-01-01
We evaluated that in-line non-linear compensation schemes decrease the com- plexity of digital back-propagation and enhance the perfor mance of 40/112/224Gbit/s mixed line rate network. Both grouped and un-grouped spectral all ocation schemes are investigated.......We evaluated that in-line non-linear compensation schemes decrease the com- plexity of digital back-propagation and enhance the perfor mance of 40/112/224Gbit/s mixed line rate network. Both grouped and un-grouped spectral all ocation schemes are investigated....
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 framework for predicting three-dimensional prostate deformation in real time
Jahya, Alex; Herink, Mark; Misra, Sarthak
2013-01-01
Background Surgical simulation systems can be used to estimate soft tissue deformation during pre- and intra-operative planning. Such systems require a model that can accurately predict the deformation in real time. In this study, we present a back-propagation neural network for predicting
Analog design of a new neural network for optical character recognition.
Morns, I P; Dlay, S S
1999-01-01
An electronic circuit is presented for a new type of neural network, which gives a recognition rate of over 100 kHz. The network is used to classify handwritten numerals, presented as Fourier and wavelet descriptors, and has been shown to train far quicker than the popular backpropagation network while maintaining classification accuracy.
Application of the clinical matrix to the diagnosis of leukemia
Pakkala, Sampath Y.; Lin, Frank C.
1992-07-01
A system for diagnosing leukemia subtypes has been formulated using neural networks. The statistical data of the symptoms collected by hematologists is fed into a single training set using a neural network, where the network is trained by using fast backpropagation algorithm, which when done can help the general practitioners for making diagnoses on the basis of signs and symptoms alone.
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.
NMDA receptors induce somatodendritic secretion in hypothalamic neurones of lactating female rats
de Kock, C.P.J.; Burnashev, N.; Lodder, J.C.; Mansvelder, H.D.; Brussaard, A.B.
2004-01-01
Many neurones in the mammalian brain are known to release the content of their vesicles from somatodendritic locations. These vesicles usually contain retrograde messengers that modulate network properties. The back-propagating action potential is thought to be the principal physiological stimulus
A Multievidence Approach for Crop Discrimination Using Multitemporal WorldView-2 Imagery
DEFF Research Database (Denmark)
Chellasamy, Menaka; Zielinski, Rafal Tomasz; Greve, Mogens Humlekrog
2014-01-01
summer) WorldView-2 multispectral imagery. A multilayer perceptron classifier is trained with the multitemporal datasets separately using a backpropagation learning algorithm, and prediction probabilities are produced for each pixel as evidence against each crop class. An integration rule based...
Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers
Daniel L. Schmoldt; Jing He; A. Lynn Abbott
1998-01-01
Our current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this type of...
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)
RANTAI PASOK BERAS PADA BULOG BERBASIS NEURAL NETWORK
Directory of Open Access Journals (Sweden)
Muhammad Imam Ghozali
2016-11-01
Full Text Available Sebagai lembaga terpenting dalam menjaga ketahanan pangan di Indonesia, perusahaan umum (Perum Badan urusan Logistik (BULOG sejak didirikannya memiliki tugas memasok bahan pangan, sehingga pengetahuan dan pengalaman BULOG dalam manajemen rantai pasok pangan dan hasil pertanian lainnya seyogianya dapat diandalkan. Namun BULOG belum teruji dalam perspektif masih menghadapi berbagai permasalahan yang sangat kompleks, yang muncul mulai dari masalah pasokan gabah di level petani, proses penggilingan gabah di level industri penggilingan (miller, hingga proses distribusi beras ke level konsumen. Dengan demikian, sebagai komoditas pangan utama, permasalahan beras bukan hanya merupakan permasalahan ekonomi saja tetapi juga bersifat politis. Data mining dapat membantu dalam memprediksi suatu sistem, sehingga dapat dilakukan pada penelitian ini agar prediksi lebih tepat dan akurat. Penelitian ini teknik yang dipakai ialah neural network backpropagation, ada beberapa tahap dalam peneilitian ini yaitu tahap pengumpulan data historik, pengolahan data, model atau metode yang diusulkan, eksperimen pada model tersebut, evaluasi dan validasi hasil. Pada hasil analisa menunjukan bahwa model ini mempunyai tingkat kesalahan atau error yang kecil atau didalam backpropagation sering disebut dengan mean square erorr (MSE. Disimpulkan bahwa teknik data mining menggunakan neural network backpropagation dapat menghasilkan suatu nilai error yang minimal sehingga tepat dan akurat untuk menentukan jumlah pasokan beras pada tahun berikutnya. Kata kunci: pasok beras, supply chain, data mining, neural network backpropagation, mean square erorr.
artificial neural network (ann) approach to electrical load
African Journals Online (AJOL)
2004-08-18
Aug 18, 2004 ... self organizing feature map; which is back-propagating in nature. ... distribution scheduling. ... electricity demand with lead times that range from ... become increasingly vital since the rise of the ... implemented for advanced control, data and sensor ... inspired methods of computing are thought to be the.
Internal measuring models in trained neural networks for parameter estimation from images
Feng, Tian-Jin; Feng, T.J.; Houkes, Z.; Korsten, Maarten J.; Spreeuwers, Lieuwe Jan
1992-01-01
The internal representations of 'learned' knowledge in neural networks are still poorly understood, even for backpropagation networks. The paper discusses a possible interpretation of learned knowledge of a network trained for parameter estimation from images. The outputs of the hidden layer are the
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
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
Experimental Study of Nonlinear Phase Noise and its Impact on WDM Systems with DP-256QAM
DEFF Research Database (Denmark)
Yankov, Metodi Plamenov; Da Ros, Francesco; Porto da Silva, Edson
2016-01-01
A probabilistic method for mitigating the phase noise component of the non-linear interference in WDM systems with Raman amplification is experimentally demonstrated. The achieved gains increase with distance and are comparable to the gains of single-channel digital back-propagation....
Emergence of heterogeneity in an agent-based model
Abdullah, Wan Ahmad Tajuddin Wan
2002-01-01
We study an interacting agent model of a game-theoretical economy. The agents play a minority-subsequently-majority game and they learn, using backpropagation networks, to obtain higher payoffs. We study the relevance of heterogeneity to performance, and how heterogeneity emerges.
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...
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…
A transfer learning framework for traffic video using neuro-fuzzy ...
Indian Academy of Sciences (India)
P M Ashok Kumar
2017-08-04
Aug 4, 2017 ... applied for each set of temporal transaction to extract latent sequential topics. (4) ANFIS training is done with the back-propagation gradient descent method. The proposed ANFIS model framework is tested on standard dataset and performance is evaluated in terms of training performance and classification ...
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...
O'Connor, P.; Welling, M.
2016-01-01
We introduce an algorithm to do backpropagation on a spiking network. Our network is "spiking" in the sense that our neurons accumulate their activation into a potential over time, and only send out a signal (a "spike") when this potential crosses a threshold and the neuron is reset. Neurons only
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...
Tests of track segment and vertex finding with neural networks
International Nuclear Information System (INIS)
Denby, B.; Lessner, E.; Lindsey, C.S.
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
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…
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
Second-Order Learning Methods for a Multilayer Perceptron
International Nuclear Information System (INIS)
Ivanov, V.V.; Purehvdorzh, B.; Puzynin, I.V.
1994-01-01
First- and second-order learning methods for feed-forward multilayer neural networks are studied. Newton-type and quasi-Newton algorithms are considered and compared with commonly used back-propagation algorithm. It is shown that, although second-order algorithms require enhanced computer facilities, they provide better convergence and simplicity in usage. 13 refs., 2 figs., 2 tabs
2016-09-15
Requirements for the Degree of Doctor of Philosophy in Operations Research Michael P. Gibb, B.S., M.S. Captain, USAF September 2016 DISTRIBUTION...Bidstrup, P. Kohl, and G. May. Modeling the properties of PECVD silicon dioxide films using optimized back-propagation neural networks. IEEE Trans
C.P.J. de Kock (Christiaan); B. Sakmann (Bert)
2008-01-01
textabstractHigh frequency (≥ 100 Hz) bursts of action potentials (APs) generated by neocortical neurons are thought to increase information content and, through back-propagation, to influence synaptic integration and efficacy in distal dendritic compartments. It was recently shown in acute slice
Parameter estimation of an aeroelastic aircraft using neural networks
Indian Academy of Sciences (India)
Many proposed model reduction procedures rely on numerical techniques andaor ... The capacity to act as general function approximator presents FFNNs as an alternative tool ... This paper investigates the aerodynamic modelling of an aeroelastic aircraft using ... the learning (training) process ± backpropagation of error.
Self-organizing networks for extracting jet features
International Nuclear Information System (INIS)
Loennblad, L.; Peterson, C.; Pi, H.; Roegnvaldsson, T.
1991-01-01
Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b.c. and light quarks. (orig.)
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.)
Optimization of multi-response dynamic systems integrating multiple ...
African Journals Online (AJOL)
It also results in better optimization performance than back-propagation neural network-based approach and data mining-based approach reported by the past researchers. Keywords: multiple responses, multiple regression, weighted dynamic signal-to-noise ratio, performance measure modelling, response function ...
Energy Technology Data Exchange (ETDEWEB)
Lin, Whei-Min; Hong, Chih-Ming [Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424 (China)
2010-06-15
To achieve maximum power point tracking (MPPT) for wind power generation systems, the rotational speed of wind turbines should be adjusted in real time according to wind speed. In this paper, a Wilcoxon radial basis function network (WRBFN) with hill-climb searching (HCS) MPPT strategy is proposed for a permanent magnet synchronous generator (PMSG) with a variable-speed wind turbine. A high-performance online training WRBFN using a back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller is designed for a PMSG. The MPSO is adopted in this study to adapt to the learning rates in the back-propagation process of the WRBFN to improve the learning capability. The MPPT strategy locates the system operation points along the maximum power curves based on the dc-link voltage of the inverter, thus avoiding the generator speed detection. (author)
The development of intelligent expert system with SAT for semiconductor
International Nuclear Information System (INIS)
Kim, Jae Yeol; Shim, Jae Gi; Jeong, Hyun Jo; Cho, Young Tae; Kim, Chang Hyun; Ko, Myung Soo
2001-01-01
In this study, the researches classifying the artificial flaws in semiconductor packages are performed using pattern recognition technology. For this purposes image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtering, binary processing, edge detection and classifier selection is treated by BP(backpropagation). Specially, it is compared IP(image processing) and SOM(self-organizing map) as preprocessing method for dimensionality reduction for entrance into multi-layer perceptron(backpropagation). Also, the pattern recognition techniques is applied to the classification problem of semiconductor flaws as crack, delamination. According to this results, it is possible to acquire the recognition rate of 83.4% about delamination, 75.7% about crack for SOM, and to acquire the recognition rate of 100% for BP.
A Learning Method for Neural Networks Based on a Pseudoinverse Technique
Directory of Open Access Journals (Sweden)
Chinmoy Pal
1996-01-01
Full Text Available A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. The efficiency and robustness of the method are verified with the help of an Exclusive OR problem and a dynamic system identification of a linear single degree of freedom mass–spring problem. It is observed that, compared with the conventional backpropagation method, the proposed method has a better convergence rate and a higher degree of learning accuracy with a lower equivalent learning coefficient. It is also found that unlike the steepest descent method, the learning capability of which is dependent on the value of the learning coefficient ν, the proposed pseudoinverse based backpropagation algorithm is comparatively robust with respect to its equivalent variable learning coefficient. A combination of the pseudoinverse method and the steepest descent method is proposed for a faster, more accurate learning capability.
Study on MPGA-BP of Gravity Dam Deformation Prediction
Directory of Open Access Journals (Sweden)
Xiaoyu Wang
2017-01-01
Full Text Available Displacement is an important physical quantity of hydraulic structures deformation monitoring, and its prediction accuracy is the premise of ensuring the safe operation. Most existing metaheuristic methods have three problems: (1 falling into local minimum easily, (2 slowing convergence, and (3 the initial value’s sensitivity. Resolving these three problems and improving the prediction accuracy necessitate the application of genetic algorithm-based backpropagation (GA-BP neural network and multiple population genetic algorithm (MPGA. A hybrid multiple population genetic algorithm backpropagation (MPGA-BP neural network algorithm is put forward to optimize deformation prediction from periodic monitoring surveys of hydraulic structures. This hybrid model is employed for analyzing the displacement of a gravity dam in China. The results show the proposed model is superior to an ordinary BP neural network and statistical regression model in the aspect of global search, convergence speed, and prediction accuracy.
Sinha, S K; Karray, F
2002-01-01
Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.
International Nuclear Information System (INIS)
Schierle, C.; Otto, M.
1992-01-01
A two layer perceptron with backpropagation of error is used for quantitative analysis in ICP-AES. The network was trained by emission spectra of two interfering lines of Cd and As and the concentrations of both elements were subsequently estimated from mixture spectra. The spectra of the Cd and As lines were also used to perform multiple linear regression (MLR) via the calculation of the pseudoinverse S + of the sensitivity matrix S. In the present paper it is shown that there exist close relations between the operation of the perceptron and the MLR procedure. These are most clearly apparent in the correlation between the weights of the backpropagation network and the elements of the pseudoinverse. Using MLR, the confidence intervals over the predictions are exploited to correct for the optical device of the wavelength shift. (orig.)
Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
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.
Stochastic sensitivity analysis and Langevin simulation for neural network learning
International Nuclear Information System (INIS)
Koda, Masato
1997-01-01
A comprehensive theoretical framework is proposed for the learning of a class of gradient-type neural networks with an additive Gaussian white noise process. The study is based on stochastic sensitivity analysis techniques, and formal expressions are obtained for stochastic learning laws in terms of functional derivative sensitivity coefficients. The present method, based on Langevin simulation techniques, uses only the internal states of the network and ubiquitous noise to compute the learning information inherent in the stochastic correlation between noise signals and the performance functional. In particular, the method does not require the solution of adjoint equations of the back-propagation type. Thus, the present algorithm has the potential for efficiently learning network weights with significantly fewer computations. Application to an unfolded multi-layered network is described, and the results are compared with those obtained by using a back-propagation method
Developing energy forecasting model using hybrid artificial intelligence method
Institute of Scientific and Technical Information of China (English)
Shahram Mollaiy-Berneti
2015-01-01
An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.
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.
Choi, Yun Seok
2017-05-26
Full waveform inversion (FWI) using an energy-based objective function has the potential to provide long wavelength model information even without low frequency in the data. However, without the back-propagation method (adjoint-state method), its implementation is impractical for the model size of general seismic survey. We derive the gradient of the energy-based objective function using the back-propagation method to make its FWI feasible. We also raise the energy signal to the power of a small positive number to properly handle the energy signal imbalance as a function of offset. Examples demonstrate that the proposed FWI algorithm provides a convergent long wavelength structure model even without low-frequency information, which can be used as a good starting model for the subsequent conventional FWI.
Initial Investigation of Software-Based Bone-Suppressed Imaging
International Nuclear Information System (INIS)
Park, Eunpyeong; Youn, Hanbean; Kim, Ho Kyung
2015-01-01
Chest radiography is the most widely used imaging modality in medicine. However, the diagnostic performance of chest radiography is deteriorated by the anatomical background of the patient. So, dual energy imaging (DEI) has recently been emerged and demonstrated an improved. However, the typical DEI requires more than two projections, hence causing additional patient dose. The motion artifact is another concern in the DEI. In this study, we investigate DEI-like bone-suppressed imaging based on the post processing of a single radiograph. To obtain bone-only images, we use the artificial neural network (ANN) method with the error backpropagation-based machine learning approach. The computational load of learning process of the ANN is too heavy for a practical implementation because we use the gradient descent method for the error backpropagation. We will use a more advanced error propagation method for the learning process
Directory of Open Access Journals (Sweden)
Farahnaz SADOUGHI
2014-03-01
Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.
Kondo, Shuhei; Shibata, Tadashi; Ohmi, Tadahiro
1995-02-01
We have investigated the learning performance of the hardware backpropagation (HBP) algorithm, a hardware-oriented learning algorithm developed for the self-learning architecture of neural networks constructed using neuron MOS (metal-oxide-semiconductor) transistors. The solution to finding a mirror symmetry axis in a 4×4 binary pixel array was tested by computer simulation based on the HBP algorithm. Despite the inherent restrictions imposed on the hardware-learning algorithm, HBP exhibits equivalent learning performance to that of the original backpropagation (BP) algorithm when all the pertinent parameters are optimized. Very importantly, we have found that HBP has a superior generalization capability over BP; namely, HBP exhibits higher performance in solving problems that the network has not yet learnt.
Xu, Tianhua; Shevchenko, Nikita A; Lavery, Domaniç; Semrau, Daniel; Liga, Gabriele; Alvarado, Alex; Killey, Robert I; Bayvel, Polina
2017-02-20
The relationship between modulation format and the performance of multi-channel digital back-propagation (MC-DBP) in ideal Nyquist-spaced optical communication systems is investigated. It is found that the nonlinear distortions behave independent of modulation format in the case of full-field DBP, in contrast to the cases of electronic dispersion compensation and partial-bandwidth DBP. It is shown that the minimum number of steps per span required for MC-DBP depends on the chosen modulation format. For any given target information rate, there exists a possible trade-off between modulation format and back-propagated bandwidth, which could be used to reduce the computational complexity requirement of MC-DBP.
Liao, Pei-Hung; Hsu, Pei-Ti; Chu, William; Chu, Woei-Chyn
2015-06-01
This study applied artificial intelligence to help nurses address problems and receive instructions through information technology. Nurses make diagnoses according to professional knowledge, clinical experience, and even instinct. Without comprehensive knowledge and thinking, diagnostic accuracy can be compromised and decisions may be delayed. We used a back-propagation neural network and other tools for data mining and statistical analysis. We further compared the prediction accuracy of the previous methods with an adaptive-network-based fuzzy inference system and the back-propagation neural network, identifying differences in the questions and in nurse satisfaction levels before and after using the nursing information system. This study investigated the use of artificial intelligence to generate nursing diagnoses. The percentage of agreement between diagnoses suggested by the information system and those made by nurses was as much as 87 percent. When patients are hospitalized, we can calculate the probability of various nursing diagnoses based on certain characteristics. © The Author(s) 2013.
Nonlinear Time Series Prediction Using Chaotic Neural Networks
Li, Ke-Ping; Chen, Tian-Lun
2001-06-01
A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm. The project supported by National Basic Research Project "Nonlinear Science" and National Natural Science Foundation of China under Grant No. 60074020
State and location dependence of action potential metabolic cost in cortical pyramidal neurons.
Hallermann, Stefan; de Kock, Christiaan P J; Stuart, Greg J; Kole, Maarten H P
2012-06-03
Action potential generation and conduction requires large quantities of energy to restore Na(+) and K(+) ion gradients. We investigated the subcellular location and voltage dependence of this metabolic cost in rat neocortical pyramidal neurons. Using Na(+)/K(+) charge overlap as a measure of action potential energy efficiency, we found that action potential initiation in the axon initial segment (AIS) and forward propagation into the axon were energetically inefficient, depending on the resting membrane potential. In contrast, action potential backpropagation into dendrites was efficient. Computer simulations predicted that, although the AIS and nodes of Ranvier had the highest metabolic cost per membrane area, action potential backpropagation into the dendrites and forward propagation into axon collaterals dominated energy consumption in cortical pyramidal neurons. Finally, we found that the high metabolic cost of action potential initiation and propagation down the axon is a trade-off between energy minimization and maximization of the conduction reliability of high-frequency action potentials.
Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks
Shuhui, L.; Fu, X.; Jaithwa, I.; Alonso, E.; Fairbank, M.; Wunsch, D. C.
2015-01-01
A microgrid consists of a variety of inverter-interfaced distributed energy resources (DERs). A key issue is how to control DERs within the microgrid and how to connect them to or disconnect them from the microgrid quickly. This paper presents a strategy for controlling inverter-interfaced DERs within a microgrid using an artificial neural network, which implements a dynamic programming algorithm and is trained with a new Levenberg-Marquardt backpropagation algorithm. Compared to conventional...
Tripathy, Manoj
2012-01-01
This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to disc...
Alternating optimization method based on nonnegative matrix factorizations for deep neural networks
Sakurai, Tetsuya; Imakura, Akira; Inoue, Yuto; Futamura, Yasunori
2016-01-01
The backpropagation algorithm for calculating gradients has been widely used in computation of weights for deep neural networks (DNNs). This method requires derivatives of objective functions and has some difficulties finding appropriate parameters such as learning rate. In this paper, we propose a novel approach for computing weight matrices of fully-connected DNNs by using two types of semi-nonnegative matrix factorizations (semi-NMFs). In this method, optimization processes are performed b...
A novel approach to error function minimization for feedforward neural networks
International Nuclear Information System (INIS)
Sinkus, R.
1995-01-01
Feedforward neural networks with error backpropagation are widely applied to pattern recognition. One general problem encountered with this type of neural networks is the uncertainty, whether the minimization procedure has converged to a global minimum of the cost function. To overcome this problem a novel approach to minimize the error function is presented. It allows to monitor the approach to the global minimum and as an outcome several ambiguities related to the choice of free parameters of the minimization procedure are removed. (orig.)
Direct adaptive control using feedforward neural networks
Cajueiro, Daniel Oliveira; Hemerly, Elder Moreira
2003-01-01
ABSTRACT: This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the conver...
Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature
Lai, Songxuan; Jin, Lianwen; Yang, Weixin
2017-01-01
Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification. The training objective is to directly minimize intra-class variations and to push the distances between skilled forgeries and genuine samples above a given threshold. By back-propagating the training signals, our RNN network produced discriminative features with desired metrics. Additionally, we propose a novel d...
Invariant object recognition based on the generalized discrete radon transform
Easley, Glenn R.; Colonna, Flavia
2004-04-01
We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.
Tracking and vertex finding with drift chambers and neural networks
International Nuclear Information System (INIS)
Lindsey, C.
1991-09-01
Finding tracks, track vertices and event vertices with neural networks from drift chamber signals is discussed. Simulated feed-forward neural networks have been trained with back-propagation to give track parameters using Monte Carlo simulated tracks in one case and actual experimental data in another. Effects on network performance of limited weight resolution, noise and drift chamber resolution are given. Possible implementations in hardware are discussed. 7 refs., 10 figs
2009-06-01
des ign a c lassifier f or t he differentiation of the abnormal from the normal structures . In this project, we have also investigated the...were trained: one with the current m ammograms and the other with the prior mammograms. A feed- forward backpropagation artificial neural network...Sahin er, B., Helvie, M. A., Petri ck, N., Roubidou x, M. A., Wi lson, T. E., Adler, D. D., Pa ramagul, C., Ne wman, J . S . a nd G opal , S. S
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.
wind speed. Interestingly the PKF model is a two layered network (input and output) without hidden layer. Also it is a fact that numerical or physical models have restrictions by certain assumptions and conditions, whereas artificial neural network... is shown by Tsai et al (2002). They have carried out forecasting of significant wave heights and periods at a desired location directly from the observed wave records using a supervised artificial neural network with error back-propagation procedures...
Pengenalan Wajah dengan Jaringan Saraf Tiruan Back Propogation
Nugroho, Fx. Henry
2009-01-01
In this paper we describe face pattern recognition with neural network. Neural network is one ofprincipal brand intellegence method of its job look like with the human being brain. Neural network also can becoupled with another science discipline to solve assorted of problem, one of them is with the image processing.Application designed with the artificial neural network back propogation and to get the its face pattern isused a Prewitt operator method.Keywords: Neural Network, backpropagation...
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
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
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...
Generalized Hough Transform for Object Classification in the Maritime Domain
2015-12-01
Aziz, “Detecting mango fruits by using Randomized Hough Transform and backpropagation neural network,” in 18th Int. Conf. Inform. Visualisation...Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY 2. REPORT DATE December 2015 3. REPORT TYPE AND... used to generate a representation of the object as a Hough coordinate table by using the GHT algorithm. The table is then reformatted to a contour map
Control of a hybrid compensator in a power network by an artificial neural network
Directory of Open Access Journals (Sweden)
I. S. Shaw
1998-07-01
Full Text Available Increased interest in the elimination of distortion in electrical power networks has led to the development of various compensator topologies. The increasing cost of electrical energy necessitates the cost-effective operation of any of these topologies. This paper considers the development of an artificial neural network based controller, trained by means of the backpropagation method, that ensures the cost-effective operation of the hybrid compensator consisting of various converters and filters.
Bhaya, Amit; Kaszkurewicz, Eugenius
2004-01-01
It is pointed out that the so called momentum method, much used in the neural network literature as an acceleration of the backpropagation method, is a stationary version of the conjugate gradient method. Connections with the continuous optimization method known as heavy ball with friction are also made. In both cases, adaptive (dynamic) choices of the so called learning rate and momentum parameters are obtained using a control Liapunov function analysis of the system.
Quaternionic Multilayer Perceptron with Local Analyticity
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Nobuyuki Matsui
2012-11-01
Full Text Available A multi-layered perceptron type neural network is presented and analyzed in this paper. All neuronal parameters such as input, output, action potential and connection weight are encoded by quaternions, which are a class of hypercomplex number system. Local analytic condition is imposed on the activation function in updating neurons’ states in order to construct learning algorithm for this network. An error back-propagation algorithm is introduced for modifying the connection weights of the network.
Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices
Lim, Suhwan; Bae, Jong-Ho; Eum, Jai-Ho; Lee, Sungtae; Kim, Chul-Heung; Kwon, Dongseok; Park, Byung-Gook; Lee, Jong-Ho
2017-01-01
In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron net...
Control of GMA Butt Joint Welding Based on Neural Networks
DEFF Research Database (Denmark)
Christensen, Kim Hardam; Sørensen, Torben
2004-01-01
This paper presents results from an experimentally based research on Gas Metal Arc Welding (GMAW), controlled by the artificial neural network (ANN) technology. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a high degree of quality......-linear least square error minimization, 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....
Liu, Xun; Xue, Wei; Xiao, Lei; Zhang, Bo
2017-01-01
We describe a parallel bayesian online deep learning framework (PBODL) for click-through rate (CTR) prediction within today's Tencent advertising system, which provides quick and accurate learning of user preferences. We first explain the framework with a deep probit regression model, which is trained with probabilistic back-propagation in the mode of assumed Gaussian density filtering. Then we extend the model family to a variety of bayesian online models with increasing feature embedding ca...
Application of neural networks in the ARGUS-experiment for the analysis of B- and Tau-physics
International Nuclear Information System (INIS)
Joswig, M.; Kolanoski, H.; Kublun, S.; Thurn, H.; Wegener, D.; Westerhoff, S.
1993-11-01
We present analyses of e + e - annihilation data taken with the ARGUS detector in the energy range of the Y resonances. The selection of Y(4s) decays and leptonic τ decays was studied with feed-forward multilayer networks using the backpropagation algorithm for the training. The correlated decay of τ + τ - pairs was analysed with a feed-forward network to determine a weak coupling constant. (orig.)
Active action potential propagation but not initiation in thalamic interneuron dendrites
Casale, Amanda E.; McCormick, David A.
2012-01-01
Inhibitory interneurons of the dorsal lateral geniculate nucleus of the thalamus modulate the activity of thalamocortical cells in response to excitatory input through the release of inhibitory neurotransmitter from both axons and dendrites. The exact mechanisms by which release can occur from dendrites are, however, not well understood. Recent experiments using calcium imaging have suggested that Na/K based action potentials can evoke calcium transients in dendrites via local active conductances, making the back-propagating action potential a candidate for dendritic neurotransmitter release. In this study, we employed high temporal and spatial resolution voltage-sensitive dye imaging to assess the characteristics of dendritic voltage deflections in response to Na/K action potentials in interneurons of the mouse dorsal lateral geniculate nucleus. We found that trains or single action potentials elicited by somatic current injection or local synaptic stimulation led to action potentials that rapidly and actively back-propagated throughout the entire dendritic arbor and into the fine filiform dendritic appendages known to release GABAergic vesicles. Action potentials always appeared first in the soma or proximal dendrite in response to somatic current injection or local synaptic stimulation, and the rapid back-propagation into the dendritic arbor depended upon voltage-gated sodium and TEA-sensitive potassium channels. Our results indicate that thalamic interneuron dendrites integrate synaptic inputs that initiate action potentials, most likely in the axon initial segment, that then back-propagate with high-fidelity into the dendrites, resulting in a nearly synchronous release of GABA from both axonal and dendritic compartments. PMID:22171033
High-Performance Neural Networks for Visual Object Classification
Cireşan, Dan C.; Meier, Ueli; Masci, Jonathan; Gambardella, Luca M.; Schmidhuber, Jürgen
2011-01-01
We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better ...
Conrprop: un algoritmo para la optimización de funciones no lineales con restricciones
Directory of Open Access Journals (Sweden)
Fernán Villa
2009-01-01
Full Text Available Resilent backpropagation (RPROP es una poderosa técnica de optimización basada en gradientes que ha sido comúnmente usada para el entrenamiento de redes neuronales artificiales, la cual usa una velocidad por cada parámetro en el modelo. Aunque esta técnica es capaz de resolver problemas de optimización multivariada sin restricciones, no hay referencias sobre su uso en la literatura de investigación de operaciones. En este artículo, se propone una modificación de resilent backpropagation que permite resolver problemas no lineales de optimización sujetos a restricciones generales no lineales. El algoritmo propuesto fue probado usando seis problemas comunes de prueba; para todos los casos, el algoritmo de resilent backpropagation restringido encontró la solución óptima, y para algunos casos encontró un punto óptimo mejor que el reportado en la literatura.
ODTbrain: a Python library for full-view, dense diffraction tomography.
Müller, Paul; Schürmann, Mirjam; Guck, Jochen
2015-11-04
Analyzing the three-dimensional (3D) refractive index distribution of a single cell makes it possible to describe and characterize its inner structure in a marker-free manner. A dense, full-view tomographic data set is a set of images of a cell acquired for multiple rotational positions, densely distributed from 0 to 360 degrees. The reconstruction is commonly realized by projection tomography, which is based on the inversion of the Radon transform. The reconstruction quality of projection tomography is greatly improved when first order scattering, which becomes relevant when the imaging wavelength is comparable to the characteristic object size, is taken into account. This advanced reconstruction technique is called diffraction tomography. While many implementations of projection tomography are available today, there is no publicly available implementation of diffraction tomography so far. We present a Python library that implements the backpropagation algorithm for diffraction tomography in 3D. By establishing benchmarks based on finite-difference time-domain (FDTD) simulations, we showcase the superiority of the backpropagation algorithm over the backprojection algorithm. Furthermore, we discuss how measurment parameters influence the reconstructed refractive index distribution and we also give insights into the applicability of diffraction tomography to biological cells. The present software library contains a robust implementation of the backpropagation algorithm. The algorithm is ideally suited for the application to biological cells. Furthermore, the implementation is a drop-in replacement for the classical backprojection algorithm and is made available to the large user community of the Python programming language.
Experimental Damage Identification of a Model Reticulated Shell
Directory of Open Access Journals (Sweden)
Jing Xu
2017-04-01
Full Text Available The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs, the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs were then established and were trained using the Levenberg–Marquardt algorithm (L–M algorithm. The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.
Experiments in Neural-Network Control of a Free-Flying Space Robot
Wilson, Edward
1995-01-01
Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype. The first issue concerns the importance of true system level design of the control system. A new hybrid strategy is developed here, in depth, for the beneficial integration of neural networks into the total control system. A second important issue in neural network control concerns incorporating a priori knowledge into the neural network. In many applications, it is possible to get a reasonably accurate controller using conventional means. If this prior information is used purposefully to provide a starting point for the optimizing capabilities of the neural network, it can provide much faster initial learning. In a step towards addressing this issue, a new generic Fully Connected Architecture (FCA) is developed for use with backpropagation. A third issue is that neural networks are commonly trained using a gradient based optimization method such as backpropagation; but many real world systems have Discrete Valued Functions (DVFs) that do not permit gradient based optimization. One example is the on-off thrusters that are common on spacecraft. A new technique is developed here that now extends backpropagation learning for use with DVFs. The fourth issue is that the speed of adaptation is often a limiting factor in the implementation of a neural network control system. This issue has been strongly resolved in the research by drawing on the above new contributions.
Directory of Open Access Journals (Sweden)
Arief Ramadhan
2012-05-01
Full Text Available Sistem Pengaturan Lampu Lalu Lintas Terdistribusi adalah sebuah sistem lampu lalu lintas yang ditujukan untuk memenuhi kebutuhan akan kinerja pengaturan lampu lalu lintas yang cerdas dengan pengambilan data secara real-time. Sistem ini dapat melakukan penjadwalan dan pengaturan jaringan banyakpersimpangan secarareal-time yang tidak bisa dilakukan oleh sistem pengaturan lampu lalu lintas konvensional. Penerapan klasifikasi di dalam sistem ini digunakan untuk meningkatkan akurasi dari pengenalan mobil. Proses klasifikasi diimplementasikan menggunakan tiga algoritma Jaringan Syaraf Tiruan, yakni Backpropagation, FLVQ, dan FLVQ-PSO. Berdasarkan hasil ujicoba, dapat ditunjukkan bahwa algoritma Backpropagationmemiliki performa akurasi yang lebih baik dibandingkan dua algoritma JST yang lainnya. Distributed Traffic Light Control System is a traffic light system intended to meet the need for setting the performance of intelligent traffic lights with real-time data capturing. The system can perform scheduling and network settings of multi-junction in real time that can not be done by a conventional traffic light settings system. Application of classification within this system is used to improve the accuracy of the car recognition. Classification process is implemented using three neural network algorithms, namely Backpropagation, FLVQ, and FLVQ-PSO. Based on the test results, it can be shown that the Backpropagation algorithm performs better accuracy than the other two algorithms.
Directory of Open Access Journals (Sweden)
Qian Wang
2016-01-01
Full Text Available Spectroscopy is an efficient and widely used quantitative analysis method. In this paper, a spectral quantitative analysis model with combining wavelength selection and topology structure optimization is proposed. For the proposed method, backpropagation neural network is adopted for building the component prediction model, and the simultaneousness optimization of the wavelength selection and the topology structure of neural network is realized by nonlinear adaptive evolutionary programming (NAEP. The hybrid chromosome in binary scheme of NAEP has three parts. The first part represents the topology structure of neural network, the second part represents the selection of wavelengths in the spectral data, and the third part represents the parameters of mutation of NAEP. Two real flue gas datasets are used in the experiments. In order to present the effectiveness of the methods, the partial least squares with full spectrum, the partial least squares combined with genetic algorithm, the uninformative variable elimination method, the backpropagation neural network with full spectrum, the backpropagation neural network combined with genetic algorithm, and the proposed method are performed for building the component prediction model. Experimental results verify that the proposed method has the ability to predict more accurately and robustly as a practical spectral analysis tool.
Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods
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Gregorius Satia Budhi
2015-07-01
Full Text Available Javanese characters are traditional characters that are used to write the Javanese language. The Javanese language is a language used by many people on the island of Java, Indonesia. The use of Javanese characters is diminishing more and more because of the difficulty of studying the Javanese characters themselves. The Javanese character set consists of basic characters, numbers, complementary characters, and so on. In this research we have developed a system to recognize Javanese characters. Input for the system is a digital image containing several handwritten Javanese characters. Preprocessing and segmentation are performed on the input image to get each character. For each character, feature extraction is done using the ICZ-ZCZ method. The output from feature extraction will become input for an artificial neural network. We used several artificial neural networks, namely a bidirectional associative memory network, a counterpropagation network, an evolutionary network, a backpropagation network, and a backpropagation network combined with chi2. From the experimental results it can be seen that the combination of chi2 and backpropagation achieved better recognition accuracy than the other methods.
Optimasi Bobot Jaringan Syaraf Tiruan Mengunakan Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Harry Ganda Nugraha
2014-01-01
Abstract Forecasting problem is common problem that easily found in decision making process. The popular tool to handle that problem is artificial neural network. Artificial neural network have been widely use because its ability to forecast nonlinear time series data. The learning method that have been widely use to train artificial neural network weight is backpropagation. Otherwise backpropagation learning process sometimes find problem such as over fiting so it can’t generalized the problem. Particle swarm optimization method had been proposed to train artificial neural network weigth. Mean square error, mean absolute percentage error, normalized mean square error, prediction of change in direction, average relative variance had been use to measures the model performance. Indonesia inflation time series data had been use to analyzed the model. The proposed method show that hybrid system could handle the time series forecasting problem as good as backpropagation artificial neural network Keywords—artificial neural network, particle swarm optimization, prediction of change in direction, average relative variance.
Hardware implementation of on -chip learning using re configurable FPGAS
International Nuclear Information System (INIS)
Kelash, H.M.; Sorour, H.S; Mahmoud, I.I.; Zaki, M; Haggag, S.S.
2009-01-01
The multilayer perceptron (MLP) is a neural network model that is being widely applied in the solving of diverse problems. A supervised training is necessary before the use of the neural network.A highly popular learning algorithm called back-propagation is used to train this neural network model. Once trained, the MLP can be used to solve classification problems. An interesting method to increase the performance of the model is by using hardware implementations. The hardware can do the arithmetical operations much faster than software. In this paper, a design and implementation of the sequential mode (stochastic mode) of backpropagation algorithm with on-chip learning using field programmable gate arrays (FPGA) is presented, a pipelined adaptation of the on-line back propagation algorithm (BP) is shown.The hardware implementation of forward stage, backward stage and update weight of backpropagation algorithm is also presented. This implementation is based on a SIMD parallel architecture of the forward propagation the diagnosis of the multi-purpose research reactor of Egypt accidents is used to test the proposed system
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
PEMBUATAN PERANGKAT LUNAK PENGENALAN WAJAH MENGGUNAKAN PRINCIPAL COMPONENTS ANALYSIS
Directory of Open Access Journals (Sweden)
Kartika Gunadi
2001-01-01
Full Text Available Face recognition is one of many important researches, and today, many applications have implemented it. Through development of techniques like Principal Components Analysis (PCA, computers can now outperform human in many face recognition tasks, particularly those in which large database of faces must be searched. Principal Components Analysis was used to reduce facial image dimension into fewer variables, which are easier to observe and handle. Those variables then fed into artificial neural networks using backpropagation method to recognise the given facial image. The test results show that PCA can provide high face recognition accuracy. For the training faces, a correct identification of 100% could be obtained. From some of network combinations that have been tested, a best average correct identification of 91,11% could be obtained for the test faces while the worst average result is 46,67 % correct identification Abstract in Bahasa Indonesia : Pengenalan wajah manusia merupakan salah satu bidang penelitian yang penting, dan dewasa ini banyak aplikasi yang dapat menerapkannya. Melalui pengembangan suatu teknik seperti Principal Components Analysis (PCA, komputer sekarang dapat melebihi kemampuan otak manusia dalam berbagai tugas pengenalan wajah, terutama tugas-tugas yang membutuhkan pencarian pada database wajah yang besar. Principal Components Analysis digunakan untuk mereduksi dimensi gambar wajah sehingga menghasilkan variabel yang lebih sedikit yang lebih mudah untuk diobsevasi dan ditangani. Hasil yang diperoleh kemudian akan dimasukkan ke suatu jaringan saraf tiruan dengan metode Backpropagation untuk mengenali gambar wajah yang telah diinputkan ke dalam sistem. Hasil pengujian sistem menunjukkan bahwa penggunaan PCA untuk pengenalan wajah dapat memberikan tingkat akurasi yang cukup tinggi. Untuk gambar wajah yang diikutsertakankan dalam latihan, dapat diperoleh 100% identifikasi yang benar. Dari beberapa kombinasi jaringan yang
Denervation-induced homeostatic dendritic plasticity in morphological granule cell models
Directory of Open Access Journals (Sweden)
Hermann Cuntz
2014-03-01
Full Text Available Neuronal death and subsequent denervation of target areas are major consequences of several neurological conditions such asischemia or neurodegeneration (Alzheimer's disease. The denervation-induced axonal loss results in reorganization of the dendritic tree of denervated neurons. The dendritic reorganization has been previously studied using entorhinal cortex lesion (ECL. ECL leads to shortening and loss of dendritic segments in the denervated outer molecular layer of the dentate gyrus. However, the functional importance of these long-term dendritic alterations is not yet understood and their impact on neuronal electrical properties remains unclear. Here we analyzed what happens to the electrotonic structure and excitability of dentate granule cells after lesion-induced alterations of their dendritic morphology, assuming all other parameters remain equal. We performed comparative electrotonic analysis in anatomically and biophysically realistic compartmental models of 3D-reconstructed healthy and denervated granule cells. Using the method of morphological modeling based on optimization principles minimizing the amount of wiring and maximizing synaptic democracy, we built artificial granule cells which replicate morphological features of their real counterparts. Our results show that somatofugal and somatopetal voltage attenuation in the passive cable model are strongly reduced in denervated granule cells. In line with these predictions, the attenuation both of simulated backpropagating action potentials and forward propagating EPSPs was significantly reduced in dendrites of denervated neurons. Intriguingly, the enhancement of action potential backpropagation occurred specifically in the denervated dendritic layers. Furthermore, simulations of synaptic f-I curves revealed a homeostatic increase of excitability in denervated granule cells. In summary, our morphological and compartmental modeling indicates that unless modified by changes of
Directory of Open Access Journals (Sweden)
R. Rajesh Sharma
2015-01-01
algorithm (RGSA. Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002. The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.
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.
The DSFPN, a new neural network for optical character recognition.
Morns, L P; Dlay, S S
1999-01-01
A new type of neural network for recognition tasks is presented in this paper. The network, called the dynamic supervised forward-propagation network (DSFPN), is based on the forward only version of the counterpropagation network (CPN). The DSFPN, trains using a supervised algorithm and can grow dynamically during training, allowing subclasses in the training data to be learnt in an unsupervised manner. It is shown to train in times comparable to the CPN while giving better classification accuracies than the popular backpropagation network. Both Fourier descriptors and wavelet descriptors are used for image preprocessing and the wavelets are proven to give a far better performance.
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.
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.·...
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)
On-Line Condition Monitoring System for High Level Trip Water in Steam Boiler’s Drum
Directory of Open Access Journals (Sweden)
Ismail Alnaimi Firas B.
2014-07-01
Full Text Available This paper presents a monitoring technique using Artificial Neural Networks (ANN with four different training algorithms for high level water in steam boiler’s drum. Four Back-Propagations neural networks multidimensional minimization algorithms have been utilized. Real time data were recorded from power plant located in Malaysia. The developed relevant variables were selected based on a combination of theory, experience and execution phases of the model. The Root Mean Square (RMS Error has been used to compare the results of one and two hidden layer (1HL, (2HL ANN structures
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 network error correction for solving coupled ordinary differential equations
Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.
1992-01-01
A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.
Neural network-based run-to-run controller using exposure and resist thickness adjustment
Geary, Shane; Barry, Ronan
2003-06-01
This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.
Troudet, T.; Garg, S.; Merrill, W.
1992-01-01
The design of a dynamic neurocontroller with good robustness properties is presented for a multivariable aircraft control problem. The internal dynamics of the neurocontroller are synthesized by a state estimator feedback loop. The neurocontrol is generated by a multilayer feedforward neural network which is trained through backpropagation to minimize an objective function that is a weighted sum of tracking errors, and control input commands and rates. The neurocontroller exhibits good robustness through stability margins in phase and vehicle output gains. By maintaining performance and stability in the presence of sensor failures in the error loops, the structure of the neurocontroller is also consistent with the classical approach of flight control design.
Neural networks for sensor validation and plant monitoring
International Nuclear Information System (INIS)
Upadhyaya, B.R.; Eryurek, E.; Mathai, G.
1990-01-01
Sensor and process monitoring in power plants require the estimation of one or more process variables. Neural network paradigms are suitable for establishing general nonlinear relationships among a set of plant variables. Multiple-input multiple-output autoassociative networks can follow changes in plant-wide behavior. The backpropagation algorithm has been applied for training feedforward networks. A new and enhanced algorithm for training neural networks (BPN) has been developed and implemented in a VAX workstation. Operational data from the Experimental Breeder Reactor-II (EBR-II) have been used to study the performance of BPN. Several results of application to the EBR-II are presented
Gradient descent learning algorithm overview: a general dynamical systems perspective.
Baldi, P
1995-01-01
Gives a unified treatment of gradient descent learning algorithms for neural networks using a general framework of dynamical systems. This general approach organizes and simplifies all the known algorithms and results which have been originally derived for different problems (fixed point/trajectory learning), for different models (discrete/continuous), for different architectures (forward/recurrent), and using different techniques (backpropagation, variational calculus, adjoint methods, etc.). The general approach can also be applied to derive new algorithms. The author then briefly examines some of the complexity issues and limitations intrinsic to gradient descent learning. Throughout the paper, the author focuses on the problem of trajectory learning.
Marcano Cedeño, Alexis Enrique
2010-01-01
El Algoritmo de Retropropagación (Algoritmo Backpropagation, ABP), es uno de los algoritmos más conocidos y utilizados para el entrenamiento de las Redes Neuronales Artificiales, RNAs. El ABP ha sido empleado con éxito en problemas de clasificación de patrones en áreas como: Medicina, Bioinformática, Telecomunicaciones, Banca, Predicciones Climatológicas, etc. Sin embargo el ABP tiene algunas limitaciones que le impiden alcanzar un nivel óptimo de eficiencia (problemas de lentitud, convergenc...
Rutile nanopowders for pigment production: Formation mechanism and particle size prediction
Zhang, Wu; Tang, Hongxin
2018-01-01
Formation mechanism and particle size prediction of rutile nanoparticles for pigment production were investigated. Anatase nanoparticles were observed by oriented attachment with parallel lattice fringe spaces of 0.2419 nm. Upon increasing the calcination temperature, the (1 1 0) plane of rutile was gradually observed, suggesting that the anatase (1 0 3) planes undergo internal structural rearrangement of oxygen and titanium ions into rutile phase due to ionic diffusion. Backpropagation neural network was used to predict particle size of rutile nanopowders, the prediction errors were all smaller than 2%, providing an efficient method to control particle size in pigment production.
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
Robust Bioinformatics Recognition with VLSI Biochip Microsystem
Lue, Jaw-Chyng L.; Fang, Wai-Chi
2006-01-01
A microsystem architecture for real-time, on-site, robust bioinformatic patterns recognition and analysis has been proposed. This system is compatible with on-chip DNA analysis means such as polymerase chain reaction (PCR)amplification. A corresponding novel artificial neural network (ANN) learning algorithm using new sigmoid-logarithmic transfer function based on error backpropagation (EBP) algorithm is invented. Our results show the trained new ANN can recognize low fluorescence patterns better than the conventional sigmoidal ANN does. A differential logarithmic imaging chip is designed for calculating logarithm of relative intensities of fluorescence signals. The single-rail logarithmic circuit and a prototype ANN chip are designed, fabricated and characterized.
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.
Intelligent Surveillance Robot with Obstacle Avoidance Capabilities Using Neural Network
Directory of Open Access Journals (Sweden)
Widodo Budiharto
2015-01-01
Full Text Available For specific purpose, vision-based surveillance robot that can be run autonomously and able to acquire images from its dynamic environment is very important, for example, in rescuing disaster victims in Indonesia. In this paper, we propose architecture for intelligent surveillance robot that is able to avoid obstacles using 3 ultrasonic distance sensors based on backpropagation neural network and a camera for face recognition. 2.4 GHz transmitter for transmitting video is used by the operator/user to direct the robot to the desired area. Results show the effectiveness of our method and we evaluate the performance of the system.
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.
International Nuclear Information System (INIS)
Koenig, R.W.T.; Von Hellermann, M.
1994-01-01
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 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
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.
A neural method for determining electromagnetic shower positions in laterally segmented calorimeters
International Nuclear Information System (INIS)
Roy, A.; Ray, A.; Mitra, T.; Roy, A.
1995-01-01
A method based on a neural network technique is proposed to calculate the coordinates of an incident photon striking a laterally segmented calorimeter and depositing shower energies in different segments. The technique uses a multilayer perceptron trained by back-propagation implemented through standard gradient descent followed by conjugate gradient algorithms and has been demonstrated with GEANT simulations of a BAF2 detector array. The position resolution results obtained by using this method are found to be substantially better than the first moment method with logarithmic weighting. (orig.)
Fault Tolerant Neural Network for ECG Signal Classification Systems
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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.
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.
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
Energy Technology Data Exchange (ETDEWEB)
Hong, Y.-Y.; Chen, Y.-C. [Chung Yuan University (China). Dept. of Electrical Engineering
1999-05-01
A new method is proposed for locating multiple harmonic sources in distribution systems. The proposed method first determines the proper locations for metering measurement using fuzzy clustering. Next, an artificial neural network based on the back-propagation approach is used to identify the most likely location for multiple harmonic sources. A set of systematic algorithmic steps is developed until all harmonic locations are identified. The simulation results for an 18-busbar system show that the proposed method is very efficient in locating the multiple harmonics in a distribution system. (author)
Using neural networks with jet shapes to identify b jets in e+e- interactions
International Nuclear Information System (INIS)
Bellantoni, L.; Conway, J.S.; Jacobsen, J.E.; Pan, Y.B.; Wu Saulan
1991-01-01
A feed-forward neural network trained using backpropagation was used to discriminate between b and light quark jets in e + e - → Z 0 → qanti q events. The information presented to the network consisted of 25 jet shape variables. The network successfully identified b jets in two- and three-jet events modeled using a detector simulation. The jet identification efficiency for two-jet events was 61% and the probability to call a light quark jet a b jet equal to 20%. (orig.)
Heat transfer prediction in a square porous medium using artificial neural network
Ahamad, N. Ameer; Athani, Abdulgaphur; Badruddin, Irfan Anjum
2018-05-01
Heat transfer in porous media has been investigated extensively because of its applications in various important fields. Neural network approach is applied to analyze steady two dimensional free convection flows through a porous medium fixed in a square cavity. The backpropagation neural network is trained and used to predict the heat transfer. The results are compared with available information in the literature. It is found that the heat transfer increases with increase in Rayleigh number. It is further found that the local Nusselt number decreases along the height of cavity. The neural network is found to predict the heat transfer behavior accurately for given parameters.
Application of artificial neural network for heat transfer in porous cone
Athani, Abdulgaphur; Ahamad, N. Ameer; Badruddin, Irfan Anjum
2018-05-01
Heat transfer in porous medium is one of the classical areas of research that has been active for many decades. The heat transfer in porous medium is generally studied by using numerical methods such as finite element method; finite difference method etc. that solves coupled partial differential equations by converting them into simpler forms. The current work utilizes an alternate method known as artificial neural network that mimics the learning characteristics of neurons. The heat transfer in porous medium fixed in a cone is predicted using backpropagation neural network. The artificial neural network is able to predict this behavior quite accurately.
Generative Adversarial Networks: An Overview
Creswell, Antonia; White, Tom; Dumoulin, Vincent; Arulkumaran, Kai; Sengupta, Biswa; Bharath, Anil A.
2018-01-01
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.
Digital detection system of surface defects for large aperture optical elements
International Nuclear Information System (INIS)
Fan Yong; Chen Niannian; Gao Lingling; Jia Yuan; Wang Junbo; Cheng Xiaofeng
2009-01-01
Based on the light defect images against the dark background in a scattering imaging system, a digital detection system of surface defects for large aperture optical elements has been presented. In the system, the image is segmented by a multi-area self-adaptive threshold segmentation method, then a pixel labeling method based on replacing arrays is adopted to extract defect features quickly, and at last the defects are classified through back-propagation neural networks. Experiment results show that the system can achieve real-time detection and classification. (authors)
Multilayer Perceptron: Architecture Optimization and Training
Directory of Open Access Journals (Sweden)
Hassan Ramchoun
2016-09-01
Full Text Available The multilayer perceptron has a large wide of classification and regression applications in many fields: pattern recognition, voice and classification problems. But the architecture choice has a great impact on the convergence of these networks. In the present paper we introduce a new approach to optimize the network architecture, for solving the obtained model we use the genetic algorithm and we train the network with a back-propagation algorithm. The numerical results assess the effectiveness of the theoretical results shown in this paper, and the advantages of the new modeling compared to the previous model in the literature.
Neural Network Compensation for Frequency Cross-Talk in Laser Interferometry
Lee, Wooram; Heo, Gunhaeng; You, Kwanho
The heterodyne laser interferometer acts as an ultra-precise measurement apparatus in semiconductor manufacture. However the periodical nonlinearity property caused from frequency cross-talk is an obstacle to improve the high measurement accuracy in nanometer scale. In order to minimize the nonlinearity error of the heterodyne interferometer, we propose a frequency cross-talk compensation algorithm using an artificial intelligence method. The feedforward neural network trained by back-propagation compensates the nonlinearity error and regulates to minimize the difference with the reference signal. With some experimental results, the improved accuracy is proved through comparison with the position value from a capacitive displacement sensor.
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.
Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data
Directory of Open Access Journals (Sweden)
George Rumbe
2010-12-01
Full Text Available Accurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and other Artificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood on the Wisconsin breast cancer classification problem.
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.
Directory of Open Access Journals (Sweden)
Rudiati Evi Masithoh
2013-03-01
Full Text Available Artificial neural networks (ANN was used to predict the quality parameters of tomato, i.e. Brix, citric acid, total carotene, and vitamin C. ANN was developed from Red Green Blue (RGB image data of tomatoes measured using a developed computer vision system (CVS. Qualitative analysis of tomato compositions were obtained from laboratory experiments. ANN model was based on a feedforward backpropagation network with different training functions, namely gradient descent (traingd, gradient descent with the resilient backpropagation (trainrp, Broyden, Fletcher, Goldfrab and Shanno (BFGS quasi-Newton (trainbfg, as well as Levenberg Marquardt (trainlm. The network structure using logsig and linear (purelin activation function at the hidden and output layer, respectively, and using the trainlm as a training function resulted in the best performance. Correlation coefficient (r of training and validation process were 0.97 - 0.99 and 0.92 - 0.99, whereas the MAE values ranged from 0.01 to 0.23 and 0.03 to 0.59, respectively. Keywords: Artificial neural network, trainlm, tomato, RGB Jaringan syaraf tiruan (JST digunakan untuk memprediksi parameter kualitas tomat, yaitu Brix, asam sitrat, karoten total, dan vitamin C. JST dikembangkan dari data Red Green Blue (RGB citra tomat yang diukur menggunakan computer vision system. Data kualitas tomat diperoleh dari analisis di laboratorium. Struktur model JST didasarkan pada jaringan feedforward backpropagation dengan berbagai fungsi pelatihan, yaitu gradient descent (traingd, gradient descent dengan resilient backpropagation (trainrp, Broyden, Fletcher, Goldfrab dan Shanno (BFGS quasi-Newton (trainbfg, serta Levenberg Marquardt (trainlm. Fungsi pelatihan yang terbaik adalah menggunakan trainlm, serta pada struktur jaringan digunakan fungsi aktivasi logsig pada lapisan tersembunyi dan linier (purelin pada lapisan keluaran. dengan 1000 epoch. Nilai koefisien korelasi (r pada tahap pelatihan dan validasi
Application of neural computing paradigms for signal validation
International Nuclear Information System (INIS)
Upadhyaya, B.R.; Eryurek, E.; Mathai, G.
1989-01-01
Signal validation and process monitoring problems often require the prediction of one or more process variables in a system. The feasibility of applying neural network paradigms to relate one variable with a set of other related variables is studied. The backpropagation network (BPN) is applied to develop models of signals from both a commercial power plant and the EBR-II. Modification of the BPN algorithm is studied with emphasis on the speed of network training and the accuracy of prediction. The prediction of process variables in a Westinghouse PWR is presented in this paper
Enhanced online convolutional neural networks for object tracking
Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen
2018-04-01
In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.
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.
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)
Time-reversed absorbing condition: application to inverse problems
International Nuclear Information System (INIS)
Assous, F; Kray, M; Nataf, F; Turkel, E
2011-01-01
The aim of this paper is to introduce time-reversed absorbing conditions in time-reversal methods. They enable one to 'recreate the past' without knowing the source which has emitted the signals that are back-propagated. We present two applications in inverse problems: the reduction of the size of the computational domain and the determination, from boundary measurements, of the location and volume of an unknown inclusion. The method does not rely on any a priori knowledge of the physical properties of the inclusion. Numerical tests with the wave and Helmholtz equations illustrate the efficiency of the method. This technique is fairly insensitive to noise in the data
Frequency domain, waveform inversion of laboratory crosswell radar data
Ellefsen, Karl J.; Mazzella, Aldo T.; Horton, Robert J.; McKenna, Jason R.
2010-01-01
A new waveform inversion for crosswell radar is formulated in the frequency-domain for a 2.5D model. The inversion simulates radar waves using the vector Helmholtz equation for electromagnetic waves. The objective function is minimized using a backpropagation method suitable for a 2.5D model. The inversion is tested by processing crosswell radar data collected in a laboratory tank. The estimated model is consistent with the known electromagnetic properties of the tank. The formulation for the 2.5D model can be extended to inversions of acoustic and elastic 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
Hramov, Alexander E.; Frolov, Nikita S.; Musatov, Vyachaslav Yu.
2018-02-01
In present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.
1992-08-01
W. Hubbard and LD. lackel. Backpropagation applied to handwritm zip code recognitio Neral Comptaion. vol. I pages 541-551.1989 I LFh*udaima. Neual...vision system onto one face of the wheel as a 3 cm x 3 cm image. This face of the wheel is finished as a fine-grain ground-glass diffusing surface...sensor system described above actually computes 3 sets of features per wheel rotation as shown in Figure 5. A third of the wheel’s image input face is
Using Neural Networks to Predict the Hardness of Aluminum Alloys
Directory of Open Access Journals (Sweden)
B. Zahran
2015-02-01
Full Text Available Aluminum alloys have gained significant industrial importance being involved in many of the light and heavy industries and especially in aerospace engineering. The mechanical properties of aluminum alloys are defined by a number of principal microstructural features. Conventional mathematical models of these properties are sometimes very complex to be analytically calculated. In this paper, a neural network model is used to predict the correlations between the hardness of aluminum alloys in relation to certain alloying elements. A backpropagation neural network is trained using a thorough dataset. The impact of certain elements is documented and an optimum structure is proposed.
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.
Reliability analysis of C-130 turboprop engine components using artificial neural network
Qattan, Nizar A.
In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine
International Nuclear Information System (INIS)
Hong, Chih-Ming; Chen, Chiung-Hsing; Tu, Chia-Sheng
2013-01-01
Highlights: ► This paper presents MPPT based control for optimal wind energy capture using RBFN. ► MPSO is adopted to adjust the learning rates to improve the learning capability. ► This technique can maintain the system stability and reach the desired performance. ► The EMF in the rotating reference frame is utilized in order to estimate speed. - Abstract: This paper presents maximum-power-point-tracking (MPPT) based control algorithms for optimal wind energy capture using radial basis function network (RBFN) and a proposed torque observer MPPT algorithm. The design of a high-performance on-line training RBFN using back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller for the sensorless control of a permanent magnet synchronous generator (PMSG). The MPSO is adopted in this study to adapt the learning rates in the back-propagation process of the RBFN to improve the learning capability. The PMSG is controlled by the loss-minimization control with MPPT below the base speed, which corresponds to low and high wind speed, and the maximum energy can be captured from the wind. Then the observed disturbance torque is feed-forward to increase the robustness of the PMSG system
Directory of Open Access Journals (Sweden)
Hing Wah LEE
2009-03-01
Full Text Available In this study, a general purpose Artificial Neural Network (ANN model based on the feed-forward back-propagation (FFBP algorithm has been used to predict the deflections of a micromachined structures actuated electrostatically under different loadings and geometrical parameters. A limited range of simulation results obtained via CoventorWare™ numerical software will be used initially to train the neural network via back-propagation algorithm. The micromachined structures considered in the analyses are diaphragm, fixed-fixed beams and cantilevers. ANN simulation results are compared with results obtained via CoventorWare™ simulations and existing analytical work for validation purpose. The proposed ANN model accurately predicts the deflections of the micromachined structures with great reduction of simulation efforts, establishing the method superiority. This method can be extended for applications in other sensors particularly for modeling sensors applying electrostatic actuation which are difficult in nature due to the inherent non-linearity of the electro-mechanical coupling response.
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.
Directory of Open Access Journals (Sweden)
Edin TERZIC
2010-03-01
Full Text Available A measurement system has been developed using a single tube capacitive sensor to accurately determine the fluid level in vehicular fuel tanks. A novel approach based on artificial neural networks based signal pre-processing and classification has been described in this article. A broad investigation on the Backpropagation neural network and some selected signal pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet Filter has also been presented. An on field drive trial was conducted under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire training samples from the capacitive sensor. A second field trial was conducted to obtain test samples to verify the performance of the neural network. The neural network was trained and verified with 50 % of the training and test samples. The results obtained using the neural network approach having different filtration methods are compared with the results obtained using simple Moving Mean and Moving Median functions. It is demonstrated that the Backpropagation neural network with Moving Median filter produced the most accurate outcome compared with the other signal filtration methods.
Selective interferometric imaging of internal multiples
Zuberi, M. A H
2013-01-01
Internal multiples deteriorate the image when the imaging procedure assumes only single scattering, especially if the velocity model does not reproduce such scattering in the Green’s function. If properly imaged, internal multiples (and internally-scattered energy) can enhance the seismic image and illuminate areas otherwise neglected or poorly imaged by conventional single-scattering approaches. Conventionally, in order to image internal multiples, accurate, sharp contrasts in the velocity model are required to construct a Green’s function with all the scattered energy. As an alternative, we develop a three-step procedure, which images the first-order internal scattering using the background Green’s function (from the surface to each image point), constructed from a smooth velocity model: We first back-propagate the recorded surface data using the background Green’s function, then cross-correlate the back-propagated data with the recorded data and finally cross-correlate the result with the original background Green’s function. This procedure images the contribution of the recorded first-order internal multiples and is almost free of the single-scattering recorded energy. This image can be added to the conventional single-scattering image, obtained e.g. from Kirchhoff migration, to enhance the image. Application to synthetic data with reflectors illuminated by multiple scattering only demonstrates the effectiveness of the approach.
Method and system for training dynamic nonlinear adaptive filters which have embedded memory
Rabinowitz, Matthew (Inventor)
2002-01-01
Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.
Kwon, Chung-Jin; Kim, Sung-Joong; Han, Woo-Young; Min, Won-Kyoung
2005-12-01
The rotor position and speed estimation of permanent-magnet synchronous motor(PMSM) was dealt with. By measuring the phase voltages and currents of the PMSM drive, two diagonally recurrent neural network(DRNN) based observers, a neural current observer and a neural velocity observer were developed. DRNN which has self-feedback of the hidden neurons ensures that the outputs of DRNN contain the whole past information of the system even if the inputs of DRNN are only the present states and inputs of the system. Thus the structure of DRNN may be simpler than that of feedforward and fully recurrent neural networks. If the backpropagation method was used for the training of the DRNN the problem of slow convergence arise. In order to reduce this problem, recursive prediction error(RPE) based learning method for the DRNN was presented. The simulation results show that the proposed approach gives a good estimation of rotor speed and position, and RPE based training has requires a shorter computation time compared to backpropagation based training.
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.
Laser ultrasound and simulated time reversal on bulk waves for non destructive control
International Nuclear Information System (INIS)
Diot, G; Walaszek, H; Kouadri-David, A; Guégan, S; Flifla, J
2014-01-01
Laser welding of aluminium generally creates embedded welding defects, such as porosities or cracks. Non Destructive Inspection (NDI) after processing may ensure an acceptable weld quality by defect detection. Nowadays, NDI techniques used to control the inside of a weld are mainly limited to X-Rays or ultrasonics. The current paper describes the use of a Laser Ultrasound (LU) technique to inspect porosities in 2 and 4-mm thick sheet lap welds. First experimentations resulted in the detection of 0.5-mm drilled holes in bulk aluminium sheets. The measurement of the depth of these defects is demonstrated too. Further experimentations shows the applicability of the LU technique to detect porosities in aluminium laser welds. However, as the interpretation of raw measures is limiting the detection capacity of this technique, we developed a signal processing using Time-Reversal capabilities to enhance detection capacities. Furthermore, the signal processing output is a geometrical image of the material's inner state, increasing the ease of interpretation. It is based on a mass-spring simulation which enables the back-propagation of the acquired ultrasound signal. The spring-mass simulation allows the natural generation of all the different sound waves and thus enables the back-propagation of a raw signal without any need of filtering or wave identification and extraction. Therefore the signal processing uses the information contained in the compression wave as well as in the shear wave
Early tube leak detection system for steam boiler at KEV power plant
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Ismail Firas B.
2016-01-01
Full Text Available Tube leakage in boilers has been a major contribution to trips which eventually leads to power plant shut downs. Training of network and developing artificial neural network (ANN models are essential in fault detection in critically large systems. This research focusses on the ANN modelling through training and validation of real data acquired from a sub-critical boiler unit. The artificial neural network (ANN was used to develop a compatible model and to evaluate the working properties and behaviour of boiler. The training and validation of real data has been applied using the feed-forward with back-propagation (BP. The right combination of number of neurons, number of hidden layers, training algorithms and training functions was run to achieve the best ANN model with lowest error. The ANN was trained and validated using real site data acquired from a coal fired power plant in Malaysia. The results showed that the Neural Network (NN with one hidden layers performed better than two hidden layer using feed-forward back-propagation network. The outcome from this study give us the best ANN model which eventually allows for early detection of boiler tube leakages, and forecast of a trip before the real shutdown. This will eventually reduce shutdowns in power plants.
International Nuclear Information System (INIS)
Bueno, Elaine Inacio; Ting, Daniel Kao Sun; Goncalves, Iraci M.P.
2005-01-01
The purpose of this paper is to develop a system to monitor the nuclear power of a reactor using Artificial Neural Networks. The database used in this work was developed using a theoretical model of IEA-R1 Research Reactor. The IEA-R1 is a pool type reactor of 5 MW, cooled and moderated by light water, and uses graphite and beryllium as reflector. To monitor the nuclear power the following variables were chosen: T3 . temperature above the reactor core, T4 . outlet core temperature, FE01 . primary loop flow rate and the nuclear power. The inputs are T3, T4 and FE01 and the output is the nuclear power. It was used several networks using the backpropagation algorithm. The conclusion is that the multiplayer perceptrons networks (MLPs), training by the backpropagation algorithm, can be used to solve this problem. The results obtained with the MLPs networks are satisfactory and the mean square error was in the order of 10 -4 during the network training and in the order of 10 -2 during the network testing. We intend to monitor the other variables of this model using the same methodology, and after this we will use the real database from the system to compare the results obtained with the model. The monitoring of the reactor variables is part of the development of a fault detection and isolation system which is underway and which is, by its turn, part of a comprehensive ageing management program. (author)
Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures
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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.
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.
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Chung-Ta Li
2014-01-01
Full Text Available We propose a species-based hybrid of the electromagnetism-like mechanism (EM and back-propagation algorithms (SEMBP for an interval type-2 fuzzy neural system with asymmetric membership functions (AIT2FNS design. The interval type-2 asymmetric fuzzy membership functions (IT2 AFMFs and the TSK-type consequent part are adopted to implement the network structure in AIT2FNS. In addition, the type reduction procedure is integrated into an adaptive network structure to reduce computational complexity. Hence, the AIT2FNS can enhance the approximation accuracy effectively by using less fuzzy rules. The AIT2FNS is trained by the SEMBP algorithm, which contains the steps of uniform initialization, species determination, local search, total force calculation, movement, and evaluation. It combines the advantages of EM and back-propagation (BP algorithms to attain a faster convergence and a lower computational complexity. The proposed SEMBP algorithm adopts the uniform method (which evenly scatters solution agents over the feasible solution region and the species technique to improve the algorithm’s ability to find the global optimum. Finally, two illustrative examples of nonlinear systems control are presented to demonstrate the performance and the effectiveness of the proposed AIT2FNS with the SEMBP algorithm.
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.
Time Reversal Migration for Passive Sources Using a Maximum Variance Imaging Condition
Wang, H.; Alkhalifah, Tariq Ali
2017-01-01
The conventional time-reversal imaging approach for micro-seismic or passive source location is based on focusing the back-propagated wavefields from each recorded trace in a source image. It suffers from strong background noise and limited acquisition aperture, which may create unexpected artifacts and cause error in the source location. To overcome such a problem, we propose a new imaging condition for microseismic imaging, which is based on comparing the amplitude variance in certain windows, and use it to suppress the artifacts as well as find the right location for passive sources. Instead of simply searching for the maximum energy point in the back-propagated wavefield, we calculate the amplitude variances over a window moving in both space and time axis to create a highly resolved passive event image. The variance operation has negligible cost compared with the forward/backward modeling operations, which reveals that the maximum variance imaging condition is efficient and effective. We test our approach numerically on a simple three-layer model and on a piece of the Marmousi model as well, both of which have shown reasonably good results.
Prediction of geomagnetic storms from solar wind data with the use of a neural network
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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)
Baklacioglu, Tolga; Aydin, Hakan; Turan, Onder
2016-01-01
An aircraft is a complex system that requires methodologies for an efficient thermodynamic design process. So, it is important to gain a deeper understanding of energy and exergy use throughout an aircraft. The aim of this study is to propose a topology improving NE (neuro-evolution) algorithm modeling for assessing energy and exergy efficiency of a cargo aircraft for the phases of a flight. In this regard, energy and exergy data of the aircraft achieved from several engine runs at different power settings have been utilized to derive the ANN (artificial neural network) models optimized by a GA (genetic algorithm). NE of feed-forward networks trained by a BP (backpropagation) algorithm with momentum has assured the accomplishment of optimum initial network weights as well as the improvement of the network topology. The linear correlation coefficients very close to unity obtained for the derived ANN models have proved the tight fitting of the real data and the estimated values of the efficiencies provided by the models. Finally, compared to the trial-and-error case, evolving the networks by GAs has enhanced the accuracy of the modeling simply further as the reduction in the MSE (mean squared errors) for the energy and exergy efficiencies indicates. - Highlights: • Optimization using neuro-evolution algorithm. • Improved backpropagation algorithm using momentum factor. • Turboprop parameters as independent variables. • Energy and exergy efficiency modeling of a cargo aircraft.
Prediction of thermal conductivity of rock through physico-mechanical properties
Energy Technology Data Exchange (ETDEWEB)
Singh, T.N. [Department of Earth Sciences, Indian Institute of Technology, Bombay 400 076 (India); Sinha, S.; Singh, V.K. [Institute of Technology, Banaras Hindu University, Varanasi 221 005 (India)
2007-01-15
The transfer of energy between two adjacent parts of rock mainly depends on its thermal conductivity. Present study supports the use of artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) in the study of thermal conductivity along with other intrinsic properties of rock due to its increasing importance in many areas of rock engineering, agronomy and geo environmental engineering field. In recent years, considerable effort has been made to develop techniques to determine these properties. Comparative analysis is made to analyze the capabilities among six different models of ANN and ANFIS. ANN models are based on feedforward backpropagation network with training functions resilient backpropagation (RP), one step secant (OSS) and Powell-Beale restarts (CGB) and radial basis with training functions generalized regression neural network (GRNN) and more efficient design radial basis network (NEWRB). A data set of 136 has been used for training different models and 15 were used for testing purposes. A statistical analysis is made to show the consistency among them. ANFIS is proved to be the best among all the networks tried in this case with average absolute percentage error of 0.03% and regression coefficient of 1, whereas best performance shown by the FFBP (RP) with average absolute error of 2.26%. Thermal conductivity is predicted using P-wave velocity, porosity, bulk density, uniaxial compressive strength of rock as input parameters. (author)
Mofavvaz, Shirin; Sohrabi, Mahmoud Reza; Nezamzadeh-Ejhieh, Alireza
2017-07-01
In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300 nm have been used for determination of antihistamine decongestant contents. In the first step, one type of network (feed-forward back-propagation) from the artificial neural network with two different training algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back-propagation (GDX) algorithm, were employed and their performance was evaluated. The performance of the LM algorithm was better than the GDX algorithm. In the second one, the radial basis network was utilized and results compared with the previous network. In the last one, the other intelligent method named least squares support vector machine was proposed to construct the antihistamine decongestant prediction model and the results were compared with two of the aforementioned networks. The values of the statistical parameters mean square error (MSE), Regression coefficient (R2), correlation coefficient (r) and also mean recovery (%), relative standard deviation (RSD) used for selecting the best model between these methods. Moreover, the proposed methods were compared to the high- performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them.
PREDIKSI BISNIS FOREX MENGGUNAKAN MODEL NEURAL NETWORK BERBASIS ADA BOOST MENGGUNAKAN 2047 DATA
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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.
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.
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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.
Khellal, Atmane; Ma, Hongbin; Fei, Qing
2018-05-09
The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.
Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights.
Samadi, Arash; Lillicrap, Timothy P; Tweed, Douglas B
2017-03-01
Recent work in computer science has shown the power of deep learning driven by the backpropagation algorithm in networks of artificial neurons. But real neurons in the brain are different from most of these artificial ones in at least three crucial ways: they emit spikes rather than graded outputs, their inputs and outputs are related dynamically rather than by piecewise-smooth functions, and they have no known way to coordinate arrays of synapses in separate forward and feedback pathways so that they change simultaneously and identically, as they do in backpropagation. Given these differences, it is unlikely that current deep learning algorithms can operate in the brain, but we that show these problems can be solved by two simple devices: learning rules can approximate dynamic input-output relations with piecewise-smooth functions, and a variation on the feedback alignment algorithm can train deep networks without having to coordinate forward and feedback synapses. Our results also show that deep spiking networks learn much better if each neuron computes an intracellular teaching signal that reflects that cell's nonlinearity. With this mechanism, networks of spiking neurons show useful learning in synapses at least nine layers upstream from the output cells and perform well compared to other spiking networks in the literature on the MNIST digit recognition task.
An approach to unfold the response of a multi-element system using an artificial neural network
International Nuclear Information System (INIS)
Cordes, E.; Fehrenbacher, G.; Schuetz, R.; Sprunck, M.; Hahn, K.; Hofmann, R.; Wahl, W.
1998-01-01
An unfolding procedure is proposed which aims at obtaining spectral information of a neutron radiation field by the analysis of the response of a multi-element system consisting of converter type semiconductors. For the unfolding procedure an artificial neural network (feed forward network), trained by the back-propagation method, was used. The response functions of the single elements to neutron radiation were calculated by application of a computational model for an energy range from 10 -2 eV to 10 MeV. The training of the artificial neural network was based on the computation of responses of a six-element system for a set of 300 neutron spectra and the application of the back-propagation method. The validation was performed by the unfolding of 100 computed responses. Two unfolding examples were pointed out for the determination of the neutron spectra. The spectra resulting from the unfolding procedure agree well with the original spectra used for the response computation
Kuniyil Ajith Singh, Mithun; Jaeger, Michael; Frenz, Martin; Steenbergen, Wiendelt
2016-03-01
Reflection artifacts caused by acoustic inhomogeneities are a main challenge to deep-tissue photoacoustic imaging. Photoacoustic transients generated by the skin surface and superficial vasculature will propagate into the tissue and reflect back from echogenic structures to generate reflection artifacts. These artifacts can cause problems in image interpretation and limit imaging depth. In its basic version, PAFUSion mimics the inward travelling wave-field from blood vessel-like PA sources by applying focused ultrasound pulses, and thus provides a way to identify reflection artifacts. In this work, we demonstrate reflection artifact correction in addition to identification, towards obtaining an artifact-free photoacoustic image. In view of clinical applications, we implemented an improved version of PAFUSion in which photoacoustic data is backpropagated to imitate the inward travelling wave-field and thus the reflection artifacts of a more arbitrary distribution of PA sources that also includes the skin melanin layer. The backpropagation is performed in a synthetic way based on the pulse-echo acquisitions after transmission on each single element of the transducer array. We present a phantom experiment and initial in vivo measurements on human volunteers where we demonstrate significant reflection artifact reduction using our technique. The results provide a direct confirmation that reflection artifacts are prominent in clinical epi-photoacoustic imaging, and that PAFUSion can reduce these artifacts significantly to improve the deep-tissue photoacoustic imaging.
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. PMID:25140345
Analysis of the experimental positron lifetime spectra by neural networks
International Nuclear Information System (INIS)
Avdic, S.; Chakarova, R.; Pazsit, I.
2003-01-01
This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pazsit et al., Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposition of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved. (author)
Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed
2017-11-01
The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.
Implementation of a multi-layer perception for a non-linear control problem
International Nuclear Information System (INIS)
Lister, J.B.; Schnurrenberger, H.; Marmillod, P.
1990-12-01
We present the practical application of a 1-hidden-layer multilayer perception (MLP) to provide a non-linear continuous multi-variable mapping with 42 inputs and 13 outputs. The problem resolved is one of extracting information from experimental signals with a bandwidth of many kilohertz. We have an exact model of the inverse mapping of this problem, but we have no explicit form of the required forward mapping. This is the typical situation in data interpretation. The MLP was trained to provide this mapping by learning on 500 input-output examples. The success of the off-line solution to this problem using an MLP led us to examine the robustness of the MLP to different noise sources. We found that the MLP is more robust than an approximate linear mapping of the same problem. 12 bits of resolution in the weights are necessary to avoid a significant loss of precision. The practical implementation of large analog weight matrices using DAS-multipliers and a simple segmented sigmoid is also presented. A General Adaptive Recipe (GAR) for improving the performance of conventional back-propagation was developed. The GAR uses an adaptive step length and both the bias terms and the initial weight seeding are determined by the network size. The GAR was found to be robust and much faster than conventional back-propagation. (author) 20 figs., 1 tab., 15 refs
Jaringan Syaraf Tiruan Perambatan Balik Untuk Pengenalan Wajah
Directory of Open Access Journals (Sweden)
Nahdi Sabuari
2016-11-01
Full Text Available This research discusses about face detection and face recognition in an image. Face detection has only two classifications, i.e face and not face. Face recognition is compatible with some classifications of a number individuals who want to be recognized. Face detection and face recognition in thi study using Haar-Like Feature method and Artificial Neural Network Backpropagation. A method Haar-Like Feature used for detection and extraction in an image, because the clasification on this method showed success at used to detect image of the face. Artificial Neural Network Backpropagation is a training algorithm that is used to do training simulated on facial image data training stored in a database. This study uses Ms. Excel 2007 as database with 10 individual sample image, every image in each individuals having three distance with every range has four defferent light intensities, so that the data training stored in the database reached 120 data training. The results shows that the face detection and face recognition which is developed can recognize a face image with an average accuracy rate reaches 80,8% for each distance.
Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.
Al-Jarrah, Mohammad A; Shatnawi, Hadeel
2017-08-01
Diabetic retinopathy (DR) causes blindness in the working age for people with diabetes in most countries. The increasing number of people with diabetes worldwide suggests that DR will continue to be major contributors to vision loss. Early detection of retinopathy progress in individuals with diabetes is critical for preventing visual loss. Non-proliferative DR (NPDR) is an early stage of DR. Moreover, NPDR can be classified into mild, moderate and severe. This paper proposes a novel morphology-based algorithm for detecting retinal lesions and classifying each case. First, the proposed algorithm detects the three DR lesions, namely haemorrhages, microaneurysms and exudates. Second, we defined and extracted a set of features from detected lesions. The set of selected feature emulates what physicians looked for in classifying NPDR case. Finally, we designed an artificial neural network (ANN) classifier with three layers to classify NPDR to normal, mild, moderate and severe. Bayesian regularisation and resilient backpropagation algorithms are used to train ANN. The accuracy for the proposed classifiers based on Bayesian regularisation and resilient backpropagation algorithms are 96.6 and 89.9, respectively. The obtained results are compared with results of the recent published classifier. Our proposed classifier outperforms the best in terms of sensitivity and specificity.
Earthquake-induced landslide-susceptibility mapping using an artificial neural network
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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%.
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. PMID:26977450
Time Reversal Migration for Passive Sources Using a Maximum Variance Imaging Condition
Wang, H.
2017-05-26
The conventional time-reversal imaging approach for micro-seismic or passive source location is based on focusing the back-propagated wavefields from each recorded trace in a source image. It suffers from strong background noise and limited acquisition aperture, which may create unexpected artifacts and cause error in the source location. To overcome such a problem, we propose a new imaging condition for microseismic imaging, which is based on comparing the amplitude variance in certain windows, and use it to suppress the artifacts as well as find the right location for passive sources. Instead of simply searching for the maximum energy point in the back-propagated wavefield, we calculate the amplitude variances over a window moving in both space and time axis to create a highly resolved passive event image. The variance operation has negligible cost compared with the forward/backward modeling operations, which reveals that the maximum variance imaging condition is efficient and effective. We test our approach numerically on a simple three-layer model and on a piece of the Marmousi model as well, both of which have shown reasonably good results.
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
Damayanti, A.; Werdiningsih, I.
2018-03-01
The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.
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.
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
A Malaysian Vehicle License Plate Localization and Recognition System
Directory of Open Access Journals (Sweden)
Ganapathy Velappa
2008-02-01
Full Text Available Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services, secure usage of parking houses and also to prevent car theft issues. The proposed license plate localization algorithm is based on a combination of morphological processes with a modified Hough Transform approach and the recognition of the license plates is achieved by the implementation of the feed-forward backpropagation artificial neural network. Experimental results show an average of 95% successful license plate localization and recognition in a total of 589 images captured from a complex outdoor environment.
A novel single neuron perceptron with universal approximation and XOR computation properties.
Lotfi, Ehsan; Akbarzadeh-T, M-R
2014-01-01
We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine) pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP) with gradient decent backpropagation (GDBP) learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.
Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.
2017-02-01
An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.
Neural Network Prediction of Disruptions Caused by Locked Modes on J-TEXT Tokamak
International Nuclear Information System (INIS)
Ding Yonghua; Jin Xuesong; Chen Zhenzhen; Zhuang Ge
2013-01-01
Prediction of disruptions caused by locked modes using the Back-Propagation (BP) neural network is completed on J-TEXT tokamak. The network, which is based on the BP neural network, uses Mirnov coils and locked mode coils signals as input data, and outputs a signal including information of prediction of locked mode. The rate of successful prediction of locked modes is more than 90%. For intrinsic locked mode disruptions, the network can give a prewarning signal about 1 ms ahead of the locking-time. For the disruption caused by resonant magnetic perturbation (RMPs) locked modes, the network can give a prewarning signal about 10 ms ahead of the locking-time
International Nuclear Information System (INIS)
Djukic, S.; Melnikoff, S.; Genant, H.K.
1990-01-01
Artificial neural networks (ANNs) have been shown to be useful in the area of pattern recognition for medical imaging. This paper investigates whether an ANN system can accurately classify meniscal tears on MR images of the knee. Thirty-five patients were selected for either definite meniscal tear (grade III) or normal meniscus (grade O) on MR images. The images were digitized using a high-resolution image scanner (Truvel) and then archived on disk. A two-dimensional fast Fourier transform of the image was computed, and a 15-band wedge-ring filter was applied to the output. The results were used as input to a three-layer back-propagation network. After successfully training the ANN on a portion of the data base (16 cases) with an accuracy error of less than 1%, the remainder of the data was applied
International Nuclear Information System (INIS)
Asada, N.; Eiho, S.; Doi, K.; MacMahon, H.; Montner, S.M.; Giger, M.L.
1989-01-01
An artificial neural network has been applied for pattern recognition and used as a tool in an expert system. The purpose of this study is to examine the potential usefulness of the neural network approach in medical applications for image recognition and decision making. The authors designed multilayer feedforward neural networks with a back-propagation algorithm for our study. Using first-pass radionuclide ventriculograms, we attempted to identify the right and left ventricles of the heart and the lungs by training the neural network from patterns of time-activity curves. In a preliminary study, the neural network enabled identification of the lungs and heart chambers once the network was trained sufficiently by means of repeated entries of data from the same case
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.
A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle
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Kuo-Yi Huang
2015-06-01
Full Text Available In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI algorithm. The gray level co-occurrence matrix (GLCM was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy, color features (mean and variance of gray level and geometric features (distance variance, mean diameter and diameter ratio were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.
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.
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
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
Neural net prediction of tokamak plasma disruptions
International Nuclear Information System (INIS)
Hernandez, J.V.; Lin, Z.; Horton, W.; McCool, S.C.
1994-10-01
The computation based on neural net algorithms in predicting minor and major disruptions in TEXT tokamak discharges has been performed. Future values of the fluctuating magnetic signal are predicted based on L past values of the magnetic fluctuation signal, measured by a single Mirnov coil. The time step used (= 0.04ms) corresponds to the experimental data sampling rate. Two kinds of approaches are adopted for the task, the contiguous future prediction and the multi-timescale prediction. Results are shown for comparison. Both networks are trained through the back-propagation algorithm with inertial terms. The degree of this success indicates that the magnetic fluctuations associated with tokamak disruptions may be characterized by a relatively low-dimensional dynamical system
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.
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)
Artificial neural networks in NDT
International Nuclear Information System (INIS)
Abdul Aziz Mohamed
2001-01-01
Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)
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.
The fundamentals of fuzzy neural network and application in nuclear monitoring
International Nuclear Information System (INIS)
Feng Diqing; Lei Ming
1995-01-01
The authors presents a fuzzy modeling method using fuzzy neural network with the back-propagation algorithm. The new method can identify the fuzzy model of a nonlinear system automatically. Fuzzy neural network is used to generate fuzzy rules and membership functions. The feasibility and inferential statistic of the method is examined by using numerical data and XOR problem. The FNN improves accuracy and reliability, reduces design time and minimizes system cost of fuzzy design. The FNN can be used for estimation of human injury in nuclear explosions and can be simplified to a rule neural network (RNN), which is used for pole extraction of signal. Preliminary simulation show that FNN has vest vistas in nuclear monitoring
Application of neural networks to multiple alarm processing and diagnosis in nuclear power plants
International Nuclear Information System (INIS)
Cheon, Se Woo; Chang Soon Heung; Chung, Hak Yeong
1992-01-01
This paper presents feasibility studies of multiple alarm processing and diagnosis using neural networks. The back-propagation neural network model is applied to the training of multiple alarm patterns for the identification of failure in a reactor coolant pump (RCP) system. The general mapping capability of the neural network enables to identify a fault easily. The case studies are performed with emphasis on the applicability of the neural network to pattern recognition problems. It is revealed that the neural network model can identify the cause of multiple alarms properly, even when untrained or sensor-failed alarm symptoms are given. It is also shown that multiple failures are easily identified using the symptoms of multiple alarms
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.
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.
Theory of impossible worlds: Toward a physics of information.
Buscema, Paolo Massimo; Sacco, Pier Luigi; Della Torre, Francesca; Massini, Giulia; Breda, Marco; Ferilli, Guido
2018-05-01
In this paper, we introduce an innovative approach to the fusion between datasets in terms of attributes and observations, even when they are not related at all. With our technique, starting from datasets representing independent worlds, it is possible to analyze a single global dataset, and transferring each dataset onto the others is always possible. This procedure allows a deeper perspective in the study of a problem, by offering the chance of looking into it from other, independent points of view. Even unrelated datasets create a metaphoric representation of the problem, useful in terms of speed of convergence and predictive results, preserving the fundamental relationships in the data. In order to extract such knowledge, we propose a new learning rule named double backpropagation, by which an auto-encoder concurrently codifies all the different worlds. We test our methodology on different datasets and different issues, to underline the power and flexibility of the Theory of Impossible Worlds.
Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network.
Adak, M Fatih; Yumusak, Nejat
2016-02-27
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.
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.
International Nuclear Information System (INIS)
Nabeshima Kunihiko; Suzuki Katsuo; Nose, Shoichi; Kudo, Kazuhiko
1996-01-01
The purpose of this paper is to demonstrate a nuclear power plant monitoring system using artificial neural network (ANN). The major advantages of the monitoring system are that a multi-output process system can be modelled using measurement information without establishing any mathematical expressions. The dynamics model of reactor plant was constructed by using three layered auto-associative neural network with backpropagation learning algorithm. The basic idea of anomaly detection method is to monitor the deviation between process signals measured from actual plant and corresponding output signals from the ANN plant model. The simulator used is a self contained system designed for training. Four kinds of simulated malfunction caused by equipment failure during steady state operation were used to evaluate the capability of the neural network monitoring system. The results showed that this monitoring system detected the symptom of small anomaly earlier than the prevailing alarm system. (author). 7 refs, 7 figs, 2 tabs
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.
Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure
Directory of Open Access Journals (Sweden)
Ashraf Ahmed Fahmy
2014-03-01
Full Text Available This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the input-output space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation. Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm.
Okumura, Hiroshi; Suezaki, Masashi; Sueyasu, Hideki; Arai, Kohei
2003-03-01
An automated method that can select corresponding point candidates is developed. This method has the following three features: 1) employment of the RIN-net for corresponding point candidate selection; 2) employment of multi resolution analysis with Haar wavelet transformation for improvement of selection accuracy and noise tolerance; 3) employment of context information about corresponding point candidates for screening of selected candidates. Here, the 'RIN-net' means the back-propagation trained feed-forward 3-layer artificial neural network that feeds rotation invariants as input data. In our system, pseudo Zernike moments are employed as the rotation invariants. The RIN-net has N x N pixels field of view (FOV). Some experiments are conducted to evaluate corresponding point candidate selection capability of the proposed method by using various kinds of remotely sensed images. The experimental results show the proposed method achieves fewer training patterns, less training time, and higher selection accuracy than conventional method.
Target recognition of ladar range images using even-order Zernike moments.
Liu, Zheng-Jun; Li, Qi; Xia, Zhi-Wei; Wang, Qi
2012-11-01
Ladar range images have attracted considerable attention in automatic target recognition fields. In this paper, Zernike moments (ZMs) are applied to classify the target of the range image from an arbitrary azimuth angle. However, ZMs suffer from high computational costs. To improve the performance of target recognition based on small samples, even-order ZMs with serial-parallel backpropagation neural networks (BPNNs) are applied to recognize the target of the range image. It is found that the rotation invariance and classified performance of the even-order ZMs are both better than for odd-order moments and for moments compressed by principal component analysis. The experimental results demonstrate that combining the even-order ZMs with serial-parallel BPNNs can significantly improve the recognition rate for small samples.
Fractional-order gradient descent learning of BP neural networks with Caputo derivative.
Wang, Jian; Wen, Yanqing; Gou, Yida; Ye, Zhenyun; Chen, Hua
2017-05-01
Fractional calculus has been found to be a promising area of research for information processing and modeling of some physical systems. In this paper, we propose a fractional gradient descent method for the backpropagation (BP) training of neural networks. In particular, the Caputo derivative is employed to evaluate the fractional-order gradient of the error defined as the traditional quadratic energy function. The monotonicity and weak (strong) convergence of the proposed approach are proved in detail. Two simulations have been implemented to illustrate the performance of presented fractional-order BP algorithm on three small datasets and one large dataset. The numerical simulations effectively verify the theoretical observations of this paper as well. Copyright © 2017 Elsevier Ltd. All rights reserved.
Nawi, Nazri Mohd.; Khan, Abdullah; Rehman, M. Z.
2015-05-01
A nature inspired behavior metaheuristic techniques which provide derivative-free solutions to solve complex problems. One of the latest additions to the group of nature inspired optimization procedure is Cuckoo Search (CS) algorithm. Artificial Neural Network (ANN) training is an optimization task since it is desired to find optimal weight set of a neural network in training process. Traditional training algorithms have some limitation such as getting trapped in local minima and slow convergence rate. This study proposed a new technique CSLM by combining the best features of two known algorithms back-propagation (BP) and Levenberg Marquardt algorithm (LM) for improving the convergence speed of ANN training and avoiding local minima problem by training this network. Some selected benchmark classification datasets are used for simulation. The experiment result show that the proposed cuckoo search with Levenberg Marquardt algorithm has better performance than other algorithm used in this study.
Hybrid intelligent control of PMSG wind generation system using pitch angle control with RBFN
International Nuclear Information System (INIS)
Lin, Whei-Min; Hong, Chih-Ming; Ou, Ting-Chia; Chiu, Tai-Ming
2011-01-01
This paper presents the design of a fuzzy sliding mode loss-minimization control for the speed of a permanent magnet synchronous generator (PMSG) and a high-performance on-line training radial basis function network (RBFN) for the turbine pitch angle control. The back-propagation learning algorithm is used to regulate the RBFN controller. The PMSG speed uses maximum power point tracking below the rated speed, which corresponds to low and high wind speed, and the maximum energy can be captured from the wind. A sliding mode controller with an integral-operation switching surface is designed, in which a fuzzy inference mechanism is utilized to estimate the upper bound of uncertainties. Furthermore, the fuzzy inference mechanism with center adaptation is investigated to estimate the optimal bound of uncertainties.
Prediction of the heat transfer rate of a single layer wire-on-tube type heat exchanger using ANFIS
Energy Technology Data Exchange (ETDEWEB)
Hayati, Mohsen [Electrical Engineering Department, Faculty of Engineering, Razi University, Tagh-E-Bostan, Kermanshah 67149 (Iran); Computational Intelligence Research Center, Razi University, Tagh-E-Bostan, Kermanshah 67149 (Iran); Rezaei, Abbas; Seifi, Majid [Electrical Engineering Department, Faculty of Engineering, Razi University, Tagh-E-Bostan, Kermanshah 67149 (Iran)
2009-12-15
In this paper, we applied an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for prediction of the heat transfer rate of the wire-on-tube type heat exchanger. Limited experimental data was used for training and testing ANFIS configuration with the help of hybrid learning algorithm consisting of backpropagation and least-squares estimation. The predicted values are found to be in good agreement with the actual values from the experiments with mean relative error less than 2.55%. Also, we compared the proposed ANFIS model to an ANN approach. Results show that the ANFIS model has more accuracy in comparison to ANN approach. Therefore, we can use ANFIS model to predict the performances of thermal systems in engineering applications, such as modeling heat exchangers for heat transfer analysis. (author)
Hybrid intelligent control of PMSG wind generation system using pitch angle control with RBFN
Energy Technology Data Exchange (ETDEWEB)
Lin, Whei-Min; Hong, Chih-Ming [Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 804 (China); Ou, Ting-Chia; Chiu, Tai-Ming [Institute of Nuclear Energy Research, Atomic Energy Council, Taoyuan 325 (China)
2011-02-15
This paper presents the design of a fuzzy sliding mode loss-minimization control for the speed of a permanent magnet synchronous generator (PMSG) and a high-performance on-line training radial basis function network (RBFN) for the turbine pitch angle control. The back-propagation learning algorithm is used to regulate the RBFN controller. The PMSG speed uses maximum power point tracking below the rated speed, which corresponds to low and high wind speed, and the maximum energy can be captured from the wind. A sliding mode controller with an integral-operation switching surface is designed, in which a fuzzy inference mechanism is utilized to estimate the upper bound of uncertainties. Furthermore, the fuzzy inference mechanism with center adaptation is investigated to estimate the optimal bound of uncertainties. (author)
Neural PID Control Strategy for Networked Process Control
Directory of Open Access Journals (Sweden)
Jianhua Zhang
2013-01-01
Full Text Available A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.
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.
Signal Processing and Neural Network Simulator
Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.
1995-04-01
The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.
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)
International Nuclear Information System (INIS)
Reifman, J.; Vitela, E.J.; Lee, J.C.
1993-01-01
Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network
Simulating Neutronic Core Parameters in a Research and Test Reactor
International Nuclear Information System (INIS)
Selim, H.K.; Amin, E.A.; Koutb, M.E.
2011-01-01
The present study proposes an Artificial Neural Network (ANN) modeling technique that predicts the control rods positions in a nuclear research reactor. The neutron, flux in the core of the reactor is used as the training data for the neural network model. The data used to train and validate the network are obtained by modeling the reactor core with the neutronic calculation code: CITVAP. The type of the network used in this study is the feed forward multilayer neural network with the backpropagation algorithm. The results show that the proposed ANN has good generalization capability to estimate the control rods positions knowing neutron flux for a research and test reactor. This method can be used to predict critical control rods positions to be used for reactor operation after reload
An enhanced radial basis function network for short-term electricity price forecasting
International Nuclear Information System (INIS)
Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang
2010-01-01
This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the ''spikes'' could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment. (author)
Directory of Open Access Journals (Sweden)
Nandkumar Wagh
2014-01-01
Full Text Available Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP, radial basis function (RBF neural network, and adaptive neurofuzzy inference system (ANFIS has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.
Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks
International Nuclear Information System (INIS)
Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel; Ceylan, Halim
2009-01-01
Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (author)
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.)
Optimization of a neural network model for signal-to-background prediction in gamma-ray spectrometry
International Nuclear Information System (INIS)
Dragovic, S.; Onjia, A. . E-mail address of corresponding author: sdragovic@inep.co.yu; Dragovic, S.)
2005-01-01
The artificial neural network (ANN) model was optimized for the prediction of signal-to-background (SBR) ratio as a function of the measurement time in gamma-ray spectrometry. The network parameters: learning rate (α), momentum (μ), number of epochs (E) and number of nodes in hidden layer (N) were optimized simultaneously employing variable-size simplex method. The most accurate model with the root mean square (RMS) error of 0.073 was obtained using ANN with online backpropagation randomized (OBPR) algorithm with α = 0.27, μ 0.36, E = 14800 and N = 9. Most of the predicted and experimental SBR values for the eight radionuclides ( 226 Ra, 214 Bi, 235 U, 40 K, 232 Th, 134 Cs, 137 Cs and 7 Be), studied in this work, reasonably agreed to within 15 %, which was satisfactory accuracy. (author)
Born iterative reconstruction using perturbed-phase field estimates.
Astheimer, Jeffrey P; Waag, Robert C
2008-10-01
A method of image reconstruction from scattering measurements for use in ultrasonic imaging is presented. The method employs distorted-wave Born iteration but does not require using a forward-problem solver or solving large systems of equations. These calculations are avoided by limiting intermediate estimates of medium variations to smooth functions in which the propagated fields can be approximated by phase perturbations derived from variations in a geometric path along rays. The reconstruction itself is formed by a modification of the filtered-backpropagation formula that includes correction terms to account for propagation through an estimated background. Numerical studies that validate the method for parameter ranges of interest in medical applications are presented. The efficiency of this method offers the possibility of real-time imaging from scattering measurements.
Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor
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Chang Xu
2018-05-01
Full Text Available This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs. Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.
Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor.
Xu, Chang; Wang, Yingguan; Bao, Xinghe; Li, Fengrong
2018-05-24
This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.
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.
SPATIAL DATA MINING TOOLBOX FOR MAPPING SUITABILITY OF LANDFILL SITES USING NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
S. K. M. Abujayyab
2016-09-01
Full Text Available 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.
Estimation of scattered photons using a neural network in SPECT
International Nuclear Information System (INIS)
Hasegawa, Wataru; Ogawa, Koichi
1994-01-01
In single photon emission CT (SPECT), measured projection data involve scattered photons. This causes degradation of spatial resolution and contrast in reconstructed images. The purpose of this study is to estimate the scattered photons, and eliminate them from measured data. To estimate the scattered photons, we used an artificial neural network which consists of five input units, five hidden units, and two output units. The inputs of the network are the ratios of the counts acquired by five narrow energy windows and their sum. The outputs are the ratios of the count of scattered photons and that of primary photons to the total count. The neural network was trained with a back-propagation algorithm using count data obtained by a Monte Carlo simulation. The results of simulation showed improvement of contrast and spatial resolution in reconstructed images. (author)
Condition Parameter Modeling for Anomaly Detection in Wind Turbines
Directory of Open Access Journals (Sweden)
Yonglong Yan
2014-05-01
Full Text Available Data collected from the supervisory control and data acquisition (SCADA system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs, is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN for automatic selection of the condition parameters. The SCADA data sets are determined through analysis of the cumulative probability distribution of wind speed and the relationship between output power and wind speed. The automatic BPNN-based parameter selection is for reduction of redundant parameters for anomaly detection in wind turbines. Through investigation of cases of WT faults, the validity of the automatic parameter selection-based model for WT anomaly detection is verified.
Directory of Open Access Journals (Sweden)
Yuyang Gao
2016-09-01
Full Text Available With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.
Magnetotelluric inversion via reverse time migration algorithm of seismic data
International Nuclear Information System (INIS)
Ha, Taeyoung; Shin, Changsoo
2007-01-01
We propose a new algorithm for two-dimensional magnetotelluric (MT) inversion. Our algorithm is an MT inversion based on the steepest descent method, borrowed from the backpropagation technique of seismic inversion or reverse time migration, introduced in the middle 1980s by Lailly and Tarantola. The steepest descent direction can be calculated efficiently by using the symmetry of numerical Green's function derived from a mixed finite element method proposed by Nedelec for Maxwell's equation, without calculating the Jacobian matrix explicitly. We construct three different objective functions by taking the logarithm of the complex apparent resistivity as introduced in the recent waveform inversion algorithm by Shin and Min. These objective functions can be naturally separated into amplitude inversion, phase inversion and simultaneous inversion. We demonstrate our algorithm by showing three inversion results for synthetic data
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
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
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.
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
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.
Bidding strategy based on artificial intelligence for a competitive electric market
International Nuclear Information System (INIS)
Hong, Y.-Y.; Tsai, S.-W.; Weng, M.-T.
2001-01-01
A bidding strategy using a fuzzy-c-mean (FCM) algorithm and the artificial neural network (ANN) was developed for competitive electric markets. The nodal price information was assumed to be released into the market. The FCM was used, first, to classify the daily load pattern into peak, medium-peak and off-peak levels and, secondly, to classify the competitive generation companies (gencos) into less-menacing, possible-menacing and menacing gencos. The back-propagation ANN was used for determining the bidding price for a genco. The FCM results aided in lessening the training data and reducing the ANN input nodes. The IEEE 30-busbar system was used for illustrating the applicability of the proposed method. (Author)
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.
Hayati, M.; Rashidi, A. M.; Rezaei, A.
2012-10-01
In this paper, the applicability of ANFIS as an accurate model for the prediction of the mass gain during high temperature oxidation using experimental data obtained for aluminized nanostructured (NS) nickel is presented. For developing the model, exposure time and temperature are taken as input and the mass gain as output. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the network. We have compared the proposed ANFIS model with experimental data. The predicted data are found to be in good agreement with the experimental data with mean relative error less than 1.1%. Therefore, we can use ANFIS model to predict the performances of thermal systems in engineering applications, such as modeling the mass gain for NS materials.
Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.
2013-09-01
This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.
Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood
2015-10-01
Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.
Directory of Open Access Journals (Sweden)
Wu Wan'e
2012-01-01
Full Text Available 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 range (hydroxyl-terminated polybutadiene 28%–32%, ammonium perchlorate 30%–35%, magnalium alloy 4%–8%, catocene 0%–5%, and boron 30%, the variation of the calculation data is consistent with the experimental results.
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
Directory of Open Access Journals (Sweden)
Gang Xie
2013-01-01
Full Text Available This paper proposes a novel ensemble learning approach based on logistic regression (LR and artificial intelligence tool, that is, support vector machine (SVM and back-propagation neural networks (BPNN, for corporate financial distress forecasting in fashion and textiles supply chains. Firstly, related concepts of LR, SVM, and BPNN are introduced. Then, the forecasting results by LR are introduced into the SVM and BPNN techniques which can recognize the forecasting errors in fitness by LR. Moreover, empirical analysis of Chinese listed companies in fashion and textile sector is implemented for the comparison of the methods, and some related issues are discussed. The results suggest that the proposed novel ensemble learning approach can achieve higher forecasting performance than those of individual models.
Real-time determination of fringe pattern frequencies: An application to pressure measurement
Sciammarella, Cesar A.; Piroozan, Parham
2007-05-01
Retrieving information in real time from fringe patterns is a topic of a great deal of interest in scientific and engineering applications of optical methods. This paper presents a method for fringe frequency determination based on the capability of neural networks to recognize signals that are similar but not identical to signals used to train the neural network. Sampled patterns are generated by calibration and stored in memory. Incoming patterns are analyzed by a back-propagation neural network at the speed of the recording device, a CCD camera. This method of information retrieval is utilized to measure pressures on a boundary layer flow. The sensor combines optics and electronics to analyze dynamic pressure distributions and to feed information to a control system that is capable to preserve the stability of the flow.
Chattopadhyay, Goutami; Chattopadhyay, Surajit; Chakraborthy, Parthasarathi
2012-07-01
The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.
Joint optimization of algorithmic suites for EEG analysis.
Santana, Eder; Brockmeier, Austin J; Principe, Jose C
2014-01-01
Electroencephalogram (EEG) data analysis algorithms consist of multiple processing steps each with a number of free parameters. A joint optimization methodology can be used as a wrapper to fine-tune these parameters for the patient or application. This approach is inspired by deep learning neural network models, but differs because the processing layers for EEG are heterogeneous with different approaches used for processing space and time. Nonetheless, we treat the processing stages as a neural network and apply backpropagation to jointly optimize the parameters. This approach outperforms previous results on the BCI Competition II - dataset IV; additionally, it outperforms the common spatial patterns (CSP) algorithm on the BCI Competition III dataset IV. In addition, the optimized parameters in the architecture are still interpretable.
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.
Data matching for free-surface multiple attenuation by multidimensional deconvolution
van der Neut, Joost; Frijlink, Martijn; van Borselen, Roald
2012-09-01
A common strategy for surface-related multiple elimination of seismic data is to predict multiples by a convolutional model and subtract these adaptively from the input gathers. Problems can be posed by interfering multiples and primaries. Removing multiples by multidimensional deconvolution (MDD) (inversion) does not suffer from these problems. However, this approach requires data to be consistent, which is often not the case, especially not at interpolated near-offsets. A novel method is proposed to improve data consistency prior to inversion. This is done by backpropagating first-order multiples with a time-gated reference primary event and matching these with early primaries in the input gather. After data matching, multiple elimination by MDD can be applied with a deterministic inversion scheme.
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.
Real-time electricity pricing in a deregulated environment using artificial intelligence
Energy Technology Data Exchange (ETDEWEB)
Dondo, M.G.
1998-12-31
The challenge of implementing real-time pricing of electricity was discussed. Several electric utilities want to incorporate real-time pricing into their rate policies. Conventional programming methods are not fast enough to process and distribute information in real time. Therefore, a new method that would match the current advances in communication speeds is needed. Also, conventional programming methods do not incorporate the uncertainties that are inherent in the lives of humans. Therefore, it is necessary to incorporate this fuzziness into the model. This study showed that the elements of speed and uncertainties can be readily incorporated into the determination of spot-pricing based electricity rates. A unique computational intelligence model was designed which consists of a feedforward neural network based on back-propagation training and a fuzzy logic model. The work has been demonstrated on the IEEE test systems and the Nova Scotia Power Corporation`s system.
Bidding strategy based on artificial intelligence for a competitive electric market
Energy Technology Data Exchange (ETDEWEB)
Hong, Y.-Y.; Tsai, S.-W.; Weng, M.-T. [Chung Yuan Univ., Dept. of Electrical Engineering, Chung Li (China)
2001-03-01
A bidding strategy using a fuzzy-c-mean (FCM) algorithm and the artificial neural network (ANN) was developed for competitive electric markets. The nodal price information was assumed to be released into the market. The FCM was used, first, to classify the daily load pattern into peak, medium-peak and off-peak levels and, secondly, to classify the competitive generation companies (gencos) into less-menacing, possible-menacing and menacing gencos. The back-propagation ANN was used for determining the bidding price for a genco. The FCM results aided in lessening the training data and reducing the ANN input nodes. The IEEE 30-busbar system was used for illustrating the applicability of the proposed method. (Author)
Kwon, Min-Woo; Baek, Myung-Hyun; Hwang, Sungmin; Kim, Sungjun; Park, Byung-Gook
2018-09-01
We designed the CMOS analog integrate and fire (I&F) neuron circuit can drive resistive synaptic device. The neuron circuit consists of a current mirror for spatial integration, a capacitor for temporal integration, asymmetric negative and positive pulse generation part, a refractory part, and finally a back-propagation pulse generation part for learning of the synaptic devices. The resistive synaptic devices were fabricated using HfOx switching layer by atomic layer deposition (ALD). The resistive synaptic device had gradual set and reset characteristics and the conductance was adjusted by spike-timing-dependent-plasticity (STDP) learning rule. We carried out circuit simulation of synaptic device and CMOS neuron circuit. And we have developed an unsupervised spiking neural networks (SNNs) for 5 × 5 pattern recognition and classification using the neuron circuit and synaptic devices. The hardware-based SNNs can autonomously and efficiently control the weight updates of the synapses between neurons, without the aid of software calculations.
Yu, Jiajia; He, Yong
Mango is a kind of popular tropical fruit, and the soluble solid content is an important in this study visible and short-wave near-infrared spectroscopy (VIS/SWNIR) technique was applied. For sake of investigating the feasibility of using VIS/SWNIR spectroscopy to measure the soluble solid content in mango, and validating the performance of selected sensitive bands, for the calibration set was formed by 135 mango samples, while the remaining 45 mango samples for the prediction set. The combination of partial least squares and backpropagation artificial neural networks (PLS-BP) was used to calculate the prediction model based on raw spectrum data. Based on PLS-BP, the determination coefficient for prediction (Rp) was 0.757 and root mean square and the process is simple and easy to operate. Compared with the Partial least squares (PLS) result, the performance of PLS-BP is better.
Neural network classification of quark and gluon jets
International Nuclear Information System (INIS)
Graham, M.A.; Jones, L.M.; Herbin, S.
1995-01-01
We demonstrate that there are characteristics common to quark jets and to gluon jets regardless of the interaction that produced them. The classification technique we use depends on the mass of the jet as well as the center-of-mass energy of the hard subprocess that produces the jet. In addition, we present the quark-gluon separability results of an artificial neural network trained on three-jet e + e - events at the Z 0 mass, using a back-propagation algorithm. The inputs to the network are the longitudinal momenta of the leading hadrons in the jet. We tested the network with quark and gluon jets from both e + e - →3 jets and bar pp→2 jets. Finally, we compare the performance of the artificial neural network with the results of making well chosen physical cuts
The fundamental of fuzzy neutral network and application in nuclear monitoring
International Nuclear Information System (INIS)
Feng Diqing; Lei Ming
1996-01-01
The authors present a fuzzy modeling method using fuzzy neural network with the back-propagation algorithm. The new method can identify the fuzzy model of a nonlinear system automatically. Fuzzy neural network is used to generate fuzzy rules and membership functions. The feasibility and inferential statistics of the method is examined by using numerical data and XOR problem. As an experimental result, the FNN improves accuracy and reliability, saves design time and minimizes system cost of fuzzy design. The FNN can be used for estimation of human injury in nuclear explosions and can be simplified to a rule neural network (RNN), which is used for pole extraction of signal. Preliminary simulation shows that FNN has vast vistas in nuclear monitoring
Prediction of toxic metals concentration using artificial intelligence techniques
Gholami, R.; Kamkar-Rouhani, A.; Doulati Ardejani, F.; Maleki, Sh.
2011-12-01
Groundwater and soil pollution are noted to be the worst environmental problem related to the mining industry because of the pyrite oxidation, and hence acid mine drainage generation, release and transport of the toxic metals. The aim of this paper is to predict the concentration of Ni and Fe using a robust algorithm named support vector machine (SVM). Comparison of the obtained results of SVM with those of the back-propagation neural network (BPNN) indicates that the SVM can be regarded as a proper algorithm for the prediction of toxic metals concentration due to its relative high correlation coefficient and the associated running time. As a matter of fact, the SVM method has provided a better prediction of the toxic metals Fe and Ni and resulted the running time faster compared with that of the BPNN.
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
Afdala, Adfal; Nuryani, Nuryani; Satrio Nugroho, Anto
2017-01-01
Atrial fibrillation (AF) is a disorder of the heart with fairly high mortality in adults. AF is a common heart arrythmia which is characterized by a missing or irregular contraction of atria. Therefore, finding a method to detect atrial fibrillation is necessary. In this article a system to detect atrial fibrillation has been proposed. Detection system utilized backpropagation artifical neural network. Data input in this method includes power spectrum density of R-peaks interval of electrocardiogram which is selected by wrapping method. This research uses parameter learning rate, momentum, epoch and hidden layer. System produces good performance with accuracy, sensitivity, and specificity of 83.55%, 86.72 % and 81.47 %, respectively.
Tian, Wenliang; Meng, Fandi; Liu, Li; Li, Ying; Wang, Fuhui
2017-01-01
A concept for prediction of organic coatings, based on the alternating hydrostatic pressure (AHP) accelerated tests, has been presented. An AHP accelerated test with different pressure values has been employed to evaluate coating degradation. And a back-propagation artificial neural network (BP-ANN) has been established to predict the service property and the service lifetime of coatings. The pressure value (P), immersion time (t) and service property (impedance modulus |Z|) are utilized as the parameters of the network. The average accuracies of the predicted service property and immersion time by the established network are 98.6% and 84.8%, respectively. The combination of accelerated test and prediction method by BP-ANN is promising to evaluate and predict coating property used in deep sea. PMID:28094340
Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN
Directory of Open Access Journals (Sweden)
Yuan Pu
2015-01-01
Full Text Available BP neural network (Back-Propagation Neural Network, BP-NN is one of the most widely neural network models and is applied to fault diagnosis of power system currently. BP neural network has good self-learning and adaptive ability and generalization ability, but the operation process is easy to fall into local minima. Genetic algorithm has global optimization features, and crossover is the most important operation of the Genetic Algorithm. In this paper, we can modify the crossover of traditional Genetic Algorithm, using improved genetic algorithm optimized BP neural network training initial weights and thresholds, to avoid the problem of BP neural network fall into local minima. The results of analysis by an example, the method can efficiently diagnose network fault location, and improve fault-tolerance and grid fault diagnosis effect.
Idris, N. H.; Salim, N. A.; Othman, M. M.; Yasin, Z. M.
2018-03-01
This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).
Image recovery from defocused 2D fluorescent images in multimodal digital holographic microscopy.
Quan, Xiangyu; Matoba, Osamu; Awatsuji, Yasuhiro
2017-05-01
A technique of three-dimensional (3D) intensity retrieval from defocused, two-dimensional (2D) fluorescent images in the multimodal digital holographic microscopy (DHM) is proposed. In the multimodal DHM, 3D phase and 2D fluorescence distributions are obtained simultaneously by an integrated system of an off-axis DHM and a conventional epifluorescence microscopy, respectively. This gives us more information of the target; however, defocused fluorescent images are observed due to the short depth of field. In this Letter, we propose a method to recover the defocused images based on the phase compensation and backpropagation from the defocused plane to the focused plane using the distance information that is obtained from a 3D phase distribution. By applying Zernike polynomial phase correction, we brought back the fluorescence intensity to the focused imaging planes. The experimental demonstration using fluorescent beads is presented, and the expected applications are suggested.
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.
Energy Technology Data Exchange (ETDEWEB)
Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br
2009-07-01
This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)
Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network
Directory of Open Access Journals (Sweden)
Haiyan Mo
2013-01-01
Full Text Available In view of the applications of artificial neural networks in economic and financial forecasting, a stochastic time strength function is introduced in the backpropagation neural network model to predict the fluctuations of stock price changes. In this model, stochastic time strength function gives a weight for each historical datum and makes the model have the effect of random movement, and then we investigate and forecast the behavior of volatility degrees of returns for the Chinese stock market indexes and some global market indexes. The empirical research is performed in testing the prediction effect of SSE, SZSE, HSI, DJIA, IXIC, and S&P 500 with different selected volatility degrees in the established model.
Artificial neural networks for plasma spectroscopy analysis
International Nuclear Information System (INIS)
Morgan, W.L.; Larsen, J.T.; Goldstein, W.H.
1992-01-01
Artificial neural networks have been applied to a variety of signal processing and image recognition problems. Of the several common neural models the feed-forward, back-propagation network is well suited for the analysis of scientific laboratory data, which can be viewed as a pattern recognition problem. The authors present a discussion of the basic neural network concepts and illustrate its potential for analysis of experiments by applying it to the spectra of laser produced plasmas in order to obtain estimates of electron temperatures and densities. Although these are high temperature and density plasmas, the neural network technique may be of interest in the analysis of the low temperature and density plasmas characteristic of experiments and devices in gaseous electronics
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
Early detection of incipient faults in power plants using accelerated neural network learning
International Nuclear Information System (INIS)
Parlos, A.G.; Jayakumar, M.; Atiya, A.
1992-01-01
An important aspect of power plant automation is the development of computer systems able to detect and isolate incipient (slowly developing) faults at the earliest possible stages of their occurrence. In this paper, the development and testing of such a fault detection scheme is presented based on recognition of sensor signatures during various failure modes. An accelerated learning algorithm, namely adaptive backpropagation (ABP), has been developed that allows the training of a multilayer perceptron (MLP) network to a high degree of accuracy, with an order of magnitude improvement in convergence speed. An artificial neural network (ANN) has been successfully trained using the ABP algorithm, and it has been extensively tested with simulated data to detect and classify incipient faults of various types and severity and in the presence of varying sensor noise levels
DEFF Research Database (Denmark)
Nilsson, Torben; Bassani, Maria R.; Larsen, Thomas Ostenfeld
1996-01-01
different agar media, replicates of the same species grouped together. Likewise, a satisfactory classification was achieved by multivariate analysis of the data for various isolates of the cheese-associated fungi Aspergillus versicolor, P. discolor, P. roqueforti, P. solitum, P. verrucosum, P. commune and P......Curie point pyrolysis/mass spectrometry of Penicillium species was performed with 530 degrees C Curie point foils. The mass spectra were submitted to principal component analysis, canonical variates analysis and hierarchical cluster analysis, producing a final dendrogram by the use of average....... palitans. However, some difficulties appeared in distinguishing the closely related species P. commune and P. palitans. Such difficulties became greater on including more isolates and limiting the analysis to five of the species. The use of back-propagation artificial neural networks, in contrast, resulted...
Directory of Open Access Journals (Sweden)
Eduardo Gabriel Côrtes
2016-07-01
Full Text Available Este trabalho propõe desenvolver uma aplicação para auxiliar no entendimento do significado de palavras ambíguas da língua portuguesa, que dependem necessariamente do contexto para serem identificadas. A aplicação foi implementada na linguagem de programação JAVA, utilizando a IDE NetBeans, juntamente com a ferramenta ADReNA, que permitiu modelar e treinar a rede neural artificial backpropagation. Foram desenvolvidas duas versões da aplicação, que se diferenciam pela forma que representam a camada de entrada da rede neural artificial. Testes realizados mostraram que a forma que a rede neural é representada gera influência em seus resultados. Após a realização dos testes, a aplicação se mostrou promissora no reconhecimento de significados de palavras homônimas.
The Spiral Discovery Network as an Automated General-Purpose Optimization Tool
Directory of Open Access Journals (Sweden)
Adam B. Csapo
2018-01-01
Full Text Available The Spiral Discovery Method (SDM was originally proposed as a cognitive artifact for dealing with black-box models that are dependent on multiple inputs with nonlinear and/or multiplicative interaction effects. Besides directly helping to identify functional patterns in such systems, SDM also simplifies their control through its characteristic spiral structure. In this paper, a neural network-based formulation of SDM is proposed together with a set of automatic update rules that makes it suitable for both semiautomated and automated forms of optimization. The behavior of the generalized SDM model, referred to as the Spiral Discovery Network (SDN, and its applicability to nondifferentiable nonconvex optimization problems are elucidated through simulation. Based on the simulation, the case is made that its applicability would be worth investigating in all areas where the default approach of gradient-based backpropagation is used today.
Optimal fuel loading pattern design using artificial intelligence techniques
International Nuclear Information System (INIS)
Kim, Han Gon; Chang, Soon Heung; Lee, Byung Ho
1993-01-01
The Optimal Fuel Shuffling System (OFSS) is developed for optimal design of PWR fuel loading pattern. OFSS is a hybrid system that a rule based system, a fuzzy logic, and an artificial neural network are connected each other. The rule based system classifies loading patterns into two classes using several heuristic rules and a fuzzy rule. A fuzzy rule is introduced to achieve more effective and fast searching. Its membership function is automatically updated in accordance with the prediction results. The artificial neural network predicts core parameters for the patterns generated from the rule based system. The back-propagation network is used for fast prediction of core parameters. The artificial neural network and the fuzzy logic can be used as the tool for improvement of existing algorithm's capabilities. OFSS was demonstrated and validated for cycle 1 of Kori unit 1 PWR. (Author)
Predicting breast screening attendance using machine learning techniques.
Baskaran, Vikraman; Guergachi, Aziz; Bali, Rajeev K; Naguib, Raouf N G
2011-03-01
Machine learning-based prediction has been effectively applied for many healthcare applications. Predicting breast screening attendance using machine learning (prior to the actual mammogram) is a new field. This paper presents new predictor attributes for such an algorithm. It describes a new hybrid algorithm that relies on back-propagation and radial basis function-based neural networks for prediction. The algorithm has been developed in an open source-based environment. The algorithm was tested on a 13-year dataset (1995-2008). This paper compares the algorithm and validates its accuracy and efficiency with different platforms. Nearly 80% accuracy and 88% positive predictive value and sensitivity were recorded for the algorithm. The results were encouraging; 40-50% of negative predictive value and specificity warrant further work. Preliminary results were promising and provided ample amount of reasons for testing the algorithm on a larger scale.
Receiving efficiency of monostatic pulsed coherent lidars. I - Theory. II - Applications
Zhao, Yanzeng; Post, Madison J.; Hardesty, Michael
1990-01-01
Pulsed coherent radars' receiving efficiency, eta, is presently investigated as a function of range z on the basis of a theory which relates eta(z) to both the transmitted laser intensity and the point-source receiving efficiency; this efficiency is calculated by a backward method employing the back-propagated local oscillator (BPLO) approach. The theory is applied to the ideal case, in order to study system optimization when both the transmitted and the BPLO fields at the antenna are Gaussian. In the second part of this work, eta(z) is calculated for various conditions of the NOAA/ERL Wave Propagation Laboratory CO2 Doppler lidar; the sensitivity of eta(z) to transmitted laser beam quality, telescope focal setting, telescope power, scanner astigmatism, and system misalignment.
The Sustainable Development Assessment of Reservoir Resettlement Based on a BP Neural Network
Directory of Open Access Journals (Sweden)
Li Huang
2018-01-01
Full Text Available 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.
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)
Artificial neural networks in the nuclear engineering (Part 2)
International Nuclear Information System (INIS)
Baptista Filho, Benedito Dias
2002-01-01
The field of Artificial Neural Networks (ANN), one of the branches of Artificial Intelligence has been waking up a lot of interest in the Nuclear Engineering (NE). ANN can be used to solve problems of difficult modeling, when the data are fail or incomplete and in high complexity problems of control. The first part of this work began a discussion with feed-forward neural networks in back-propagation. In this part of the work, the Multi-synaptic neural networks is applied to control problems. Also, the self-organized maps is presented in a typical pattern classification problem: transients classification. The main purpose of the work is to show that ANN can be successfully used in NE if a carefully choice of its type is done: the application sets this choice. (author)
Bacterial population solitary waves can defeat rings of funnels
International Nuclear Information System (INIS)
Morris, Ryan J; Phan, Trung V; Austin, Robert H; Black, Matthew; Bos, Julia A; Lin, Ke-Chih; Kevrekidis, Ioannis G
2017-01-01
We have constructed a microfabricated circular corral for bacteria made of rings of concentric funnels which channel motile bacteria outwards via non-hydrodynamic interactions with the funnel walls. Initially bacteria do move rapidly outwards to the periphery of the corral. At the edge, nano-slits allow for the transport of nutrients into the device while keeping the bacteria from escaping. After a period of time in which the bacteria increase their cell density in this perimeter region, they are then able to defeat the physical constrains of the funnels by launching back-propagating collective waves. We present the basic data and some nonlinear modeling which can explain how bacterial population waves propagate through a physical funnel, and discuss possible biological implications. (paper)
Gradient Optimization for Analytic conTrols - GOAT
Assémat, Elie; Machnes, Shai; Tannor, David; Wilhelm-Mauch, Frank
Quantum optimal control becomes a necessary step in a number of studies in the quantum realm. Recent experimental advances showed that superconducting qubits can be controlled with an impressive accuracy. However, most of the standard optimal control algorithms are not designed to manage such high accuracy. To tackle this issue, a novel quantum optimal control algorithm have been introduced: the Gradient Optimization for Analytic conTrols (GOAT). It avoids the piecewise constant approximation of the control pulse used by standard algorithms. This allows an efficient implementation of very high accuracy optimization. It also includes a novel method to compute the gradient that provides many advantages, e.g. the absence of backpropagation or the natural route to optimize the robustness of the control pulses. This talk will present the GOAT algorithm and a few applications to transmons systems.
Artificial neural networks in prediction of mechanical behavior of concrete at high temperature
International Nuclear Information System (INIS)
Mukherjee, A.; Nag Biswas, S.
1997-01-01
The behavior of concrete structures that are exposed to extreme thermo-mechanical loading is an issue of great importance in nuclear engineering. The mechanical behavior of concrete at high temperature is non-linear. The properties that regulate its response are highly temperature dependent and extremely complex. In addition, the constituent materials, e.g. aggregates, influence the response significantly. Attempts have been made to trace the stress-strain curve through mathematical models and rheological models. However, it has been difficult to include all the contributing factors in the mathematical model. This paper examines a new programming paradigm, artificial neural networks, for the problem. Implementing a feedforward network and backpropagation algorithm the stress-strain relationship of the material is captured. The neural networks for the prediction of uniaxial behavior of concrete at high temperature has been presented here. The results of the present investigation are very encouraging. (orig.)
Identification of nonlinear dynamics in power plant components using neural networks
International Nuclear Information System (INIS)
Parlos, A.G.; Fernandez, B.; Tsai, W.K.
1990-01-01
Advances in digital computer technology have enabled widespread implementation of closed-loop digital control systems in a variety of industries. In some instances, however, the complexity of the plant and the uncertainty associated with the parameters involved in the mathematical modeling narrow the range of applicability of most systematic control system design methodologies. A multiyear project has been initiated to assess the feasibility of the artificial neural networks (ANNs) technology for computerized enhanced diagnostics and control of nuclear power plant components. At this stage of the project, a new methodology, based on backpropagation learning, has been developed for identifying the nonlinear dynamic systems from a set of input-output data known as the training set
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)
Intelligent navigation and accurate positioning of an assist robot in indoor environments
Hua, Bin; Rama, Endri; Capi, Genci; Jindai, Mitsuru; Tsuri, Yosuke
2017-12-01
Intact robot's navigation and accurate positioning in indoor environments are still challenging tasks. Especially in robot applications, assisting disabled and/or elderly people in museums/art gallery environments. In this paper, we present a human-like navigation method, where the neural networks control the wheelchair robot to reach the goal location safely, by imitating the supervisor's motions, and positioning in the intended location. In a museum similar environment, the mobile robot starts navigation from various positions, and uses a low-cost camera to track the target picture, and a laser range finder to make a safe navigation. Results show that the neural controller with the Conjugate Gradient Backpropagation training algorithm gives a robust response to guide the mobile robot accurately to the goal position.
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.
Effect of a Background Noise on the Acoustic Leak Detection Methodology for a SFR Steam Generator
International Nuclear Information System (INIS)
Kim, Tae-Joon; Jeong, Ji-Young; Kim, Jong-Man
2007-01-01
The protection of a water/steam leak into a sodium in the SFR SG at an early phase of a leak origin depends on a fast response and sensitivity of a leak detection system not to a response against the several kinds of noises. The subject in this study is to introduce a detection performance by using our developed acoustic leak detection methodology discriminated by a backpropagation neural network according to a preprocessing of the 1/6 Octave band analysis or 1/12 Octave band analysis and the x n method defined by us. It was used for the acoustic signals generated from the simulation works which are the noises of an artificial background such as a scratching, a hammering on a steel structure and so on. In a previous study, we showed that the performance of a LabVIEW tool embedded with the developed acoustic leak detection methodology detected the SWR leak signals
An evaluation of neural networks for identification of system parameters in reactor noise signals
International Nuclear Information System (INIS)
Miller, L.F.
1991-01-01
Several backpropagation neural networks for identifying fundamental mode eigenvalues were evaluated. The networks were trained and tested on analytical data and on results from other numerical methods. They were then used to predict first mode break frequencies for noise data from several sources. These predictions were, in turn, compared with analytical values and with results from alternative methods. Comparisons of results for some data sets suggest that the accuracy of predictions from neural networks are essentially equivalent to results from conventional methods while other evaluations indicate that either method may be superior. Experience gained from these numerical experiments provide insight for improving the performance of neural networks relative to other methods for identifying parameters associated with experimental data. Neural networks may also be used in support of conventional algorithms by providing starting points for nonlinear minimization algorithms
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
Phase and amplitude inversion of crosswell radar data
Ellefsen, Karl J.; Mazzella, Aldo T.; Horton, Robert J.; McKenna, Jason R.
2011-01-01
Phase and amplitude inversion of crosswell radar data estimates the logarithm of complex slowness for a 2.5D heterogeneous model. The inversion is formulated in the frequency domain using the vector Helmholtz equation. The objective function is minimized using a back-propagation method that is suitable for a 2.5D model and that accounts for the near-, intermediate-, and far-field regions of the antennas. The inversion is tested with crosswell radar data collected in a laboratory tank. The model anomalies are consistent with the known heterogeneity in the tank; the model’s relative dielectric permittivity, which is calculated from the real part of the estimated complex slowness, is consistent with independent laboratory measurements. The methodologies developed for this inversion can be adapted readily to inversions of seismic data (e.g., crosswell seismic and vertical seismic profiling data).
Effect of the size of an artificial neural network used as pattern identifier
International Nuclear Information System (INIS)
Reynoso V, M.R.; Vega C, J.J.
2003-01-01
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)
Inverse electronic scattering by Green's functions and singular values decomposition
International Nuclear Information System (INIS)
Mayer, A.; Vigneron, J.-P.
2000-01-01
An inverse scattering technique is developed to enable a sample reconstruction from the diffraction figures obtained by electronic projection microscopy. In its Green's functions formulation, this technique takes account of all orders of diffraction by performing an iterative reconstruction of the wave function on the observation screen. This scattered wave function is then backpropagated to the sample to determine the potential-energy distribution, which is assumed real valued. The method relies on the use of singular values decomposition techniques, thus providing the best least-squares solutions and enabling a reduction of noise. The technique is applied to the analysis of a two-dimensional nanometric sample that is observed in Fresnel conditions with an electronic energy of 25 eV. The algorithm turns out to provide results with a mean relative error of the order of 5% and to be very stable against random noise
Modeling of wear behavior of Al/B_4C composites produced by powder metallurgy
International Nuclear Information System (INIS)
Sahin, Ismail; Bektas, Asli; Guel, Ferhat; Cinci, Hanifi
2017-01-01
Wear characteristics of composites, Al matrix reinforced with B_4C particles percentages of 5, 10,15 and 20 produced by the powder metallurgy method were studied in this study. For this purpose, a mixture of Al and B_4C powders were pressed under 650 MPa pressure and then sintered at 635 C. The analysis of hardness, density and microstructure was performed. The produced samples were worn using a pin-on-disk abrasion device under 10, 20 and 30 N load through 500, 800 and 1200 mesh SiC abrasive papers. The obtained wear values were implemented in an artificial neural network (ANN) model having three inputs and one output using feed forward backpropagation Levenberg-Marquardt algorithm. Thus, the optimum wear conditions and hardness values were determined.
Modeling of wear behavior of Al/B{sub 4}C composites produced by powder metallurgy
Energy Technology Data Exchange (ETDEWEB)
Sahin, Ismail; Bektas, Asli [Gazi Univ., Ankara (Turkey). Dept. of Industrial Design Engineering; Guel, Ferhat; Cinci, Hanifi [Gazi Univ., Ankara (Turkey). Dept. of Materials and Metallurgy Engineering
2017-06-01
Wear characteristics of composites, Al matrix reinforced with B{sub 4}C particles percentages of 5, 10,15 and 20 produced by the powder metallurgy method were studied in this study. For this purpose, a mixture of Al and B{sub 4}C powders were pressed under 650 MPa pressure and then sintered at 635 C. The analysis of hardness, density and microstructure was performed. The produced samples were worn using a pin-on-disk abrasion device under 10, 20 and 30 N load through 500, 800 and 1200 mesh SiC abrasive papers. The obtained wear values were implemented in an artificial neural network (ANN) model having three inputs and one output using feed forward backpropagation Levenberg-Marquardt algorithm. Thus, the optimum wear conditions and hardness values were determined.
Channel Equalization Using Multilayer Perceptron Networks
Directory of Open Access Journals (Sweden)
Saba Baloch
2012-07-01
Full Text Available In most digital communication systems, bandwidth limited channel along with multipath propagation causes ISI (Inter Symbol Interference to occur. This phenomenon causes distortion of the given transmitted symbol due to other transmitted symbols. With the help of equalization ISI can be reduced. This paper presents a solution to the ISI problem by performing blind equalization using ANN (Artificial Neural Networks. The simulated network is a multilayer feedforward Perceptron ANN, which has been trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network. This paper presents a very effective method for blind channel equalization, being more efficient than the pre-existing algorithms. The obtained results show a visible reduction in the noise content.
DEFF Research Database (Denmark)
Porto da Silva, Edson
Digital signal processing (DSP) has become one of the main enabling technologies for the physical layer of coherent optical communication networks. The DSP subsystems are used to implement several functionalities in the digital domain, from synchronization to channel equalization. Flexibility...... nonlinearity compensation, (II) spectral shaping, and (III) adaptive equalization. For (I), original contributions are presented to the study of the nonlinearity compensation (NLC) with digital backpropagation (DBP). Numerical and experimental performance investigations are shown for different application...... scenarios. Concerning (II), it is demonstrated how optical and electrical (digital) pulse shaping can be allied to improve the spectral confinement of a particular class of optical time-division multiplexing (OTDM) signals that can be used as a building block for fast signaling single-carrier transceivers...
Directory of Open Access Journals (Sweden)
J. Prakash Maran
2013-09-01
Full Text Available In this study, a comparative approach was made between artificial neural network (ANN and response surface methodology (RSM to predict the mass transfer parameters of osmotic dehydration of papaya. The effects of process variables such as temperature, osmotic solution concentration and agitation speed on water loss, weight reduction, and solid gain during osmotic dehydration were investigated using a three-level three-factor Box-Behnken experimental design. Same design was utilized to train a feed-forward multilayered perceptron (MLP ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of root mean square error (RMSE, mean absolute error (MAE, standard error of prediction (SEP, model predictive error (MPE, chi square statistic (χ2, and coefficient of determination (R2 based on the validation data set. The results showed that properly trained ANN model is found to be more accurate in prediction as compared to RSM model.
Parallel Multi-Focusing Using Plane Wave Decomposition
DEFF Research Database (Denmark)
Misaridis, Thanassis; Munk, Peter; Jensen, Jørgen Arendt
2003-01-01
of desired 2-D sensitivity functions is specified, for multi-focusing in a number of directions. The field along these directions is decomposed to a sufficiently large (for accurate specification) number of plane waves, which are then back-propagated to all transducer elements. The contributions of all plane...... waves result in one time function per element. The numerical solution is presented and discussed. It contains pulses with a variation in central frequency and time-varying apodization across the aperture (dynamic apodization). The RMS difference between the transmitted field using the calculated pulse...... of the transmitted pulses is based on the directivity spectrum method, a generalization of the angular spectrum method, a generalization of the angular spectrum method, containing no evanescent waves. The underlying theory is based on the Fourier slice theorem, and field reconstruction from projections. First a set...
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)
Deep-Learning-Based Approach for Prediction of Algal Blooms
Directory of Open Access Journals (Sweden)
Feng Zhang
2016-10-01
Full Text Available Algal blooms have recently become a critical global environmental concern which might put economic development and sustainability at risk. However, the accurate prediction of algal blooms remains a challenging scientific problem. In this study, a novel prediction approach for algal blooms based on deep learning is presented—a powerful tool to represent and predict highly dynamic and complex phenomena. The proposed approach constructs a five-layered model to extract detailed relationships between the density of phytoplankton cells and various environmental parameters. The algal blooms can be predicted by the phytoplankton density obtained from the output layer. A case study is conducted in coastal waters of East China using both our model and a traditional back-propagation neural network for comparison. The results show that the deep-learning-based model yields better generalization and greater accuracy in predicting algal blooms than a traditional shallow neural network does.
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
Fault Diagnosis of Rolling Bearings Based on EWT and KDEC
Directory of Open Access Journals (Sweden)
Mingtao Ge
2017-12-01
Full Text Available This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT and kernel density estimation classifier (KDEC, which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnosis method, the vibration signal of rolling element bearing was firstly decomposed into a series of F modes by EWT, and the root mean square, kurtosis, and skewness of the F modes were computed and combined into the feature vector. According to the characteristics of kernel density estimation, a classifier based on kernel density estimation and mutual information was proposed. Then, the feature vectors were input into the KDEC for training and testing. The experimental results indicated that the proposed method can effectively identify three different operative conditions of rolling element bearings, and the accuracy rates was higher than support vector machine (SVM classifier and back-propagation (BP neural network classifier.
HIV lipodystrophy case definition using artificial neural network modelling
DEFF Research Database (Denmark)
Ioannidis, John P A; Trikalinos, Thomas A; Law, Matthew
2003-01-01
OBJECTIVE: A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy. METHODS......: The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers...... were trained and validated. Results were compared against logistic regression models using the same information. RESULTS: Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under...
Directory of Open Access Journals (Sweden)
Davide Marocco
2010-05-01
Full Text Available This paper presents a cognitive robotics model for the study of the embodied representation of action words. The present research will present how a iCub humanoid robot can learn the meaning of action words (i.e. words that represent dynamical events that happen in time by physically acting on the environment and linking the effects of its own actions with the behaviour observed on the objects before and after the action. The control system of the robot is an artificial neural network trained to manipulate an object through a Back-Propagation Through Time algorithm. We will show that in the presented model the grounding of action words relies directly to the way in which an agent interacts with the environment and manipulates it.
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.)
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
Goodacre, R; Hiom, S J; Cheeseman, S L; Murdoch, D; Weightman, A J; Wade, W G
1996-02-01
Curie-point pyrolysis mass spectra were obtained from 29 oral asaccharolytic Eubacterium strains and 6 abscess isolates previously identified as Peptostreptococcus heliotrinreducens. Pyrolysis mass spectrometry (PyMS) with cluster analysis was able to clarify the taxonomic position of this group of organisms. Artificial neural networks (ANNS) were then trained by supervised learning (with the back-propagation algorithm) to recognize the strains from their pyrolysis mass spectra; all Eubacterium strains were correctly identified, and the abscess isolates were identified as un-named Eubacterium taxon C2 and were distinct from the type strain of P. heliotrinreducens. These results demonstrate that the combination of PyMS and ANNs provides a rapid and accurate identification technique.
Molecular identity of dendritic voltage-gated sodium channels.
Lorincz, Andrea; Nusser, Zoltan
2010-05-14
Active invasion of the dendritic tree by action potentials (APs) generated in the axon is essential for associative synaptic plasticity and neuronal ensemble formation. In cortical pyramidal cells (PCs), this AP back-propagation is supported by dendritic voltage-gated Na+ (Nav) channels, whose molecular identity is unknown. Using a highly sensitive electron microscopic immunogold technique, we revealed the presence of the Nav1.6 subunit in hippocampal CA1 PC proximal and distal dendrites. Here, the subunit density is lower by a factor of 35 to 80 than that found in axon initial segments. A gradual decrease in Nav1.6 density along the proximodistal axis of the dendritic tree was also detected without any labeling in dendritic spines. Our results reveal the characteristic subcellular distribution of the Nav1.6 subunit, identifying this molecule as a key substrate enabling dendritic excitability.
Energy Technology Data Exchange (ETDEWEB)
Yamakawa, S.; Yamaguchi, A. (Toyama National College of Maritime Technology, Toyama (Japan))
1992-12-20
The present paper proposes a new taste recognition system using optical response patterns from multi-channel optical fiber sensors having potential sensitive dye coatings. It was found that the sensors give large changes in optical absorption spectra of the dyes when they are immersed in various taste solutions. Consequently, it was shown that the sensors can be used as a taste sensor. Six dyes, which give large changes in dye absorption, were selected from twenty dyes and used for six-channel optical fiber taste sensors array. The absorption spectra change data were processed by multiple discriminant analysis and neural networks using back-propagation algorithm. From the analytical results, it was demonstrated that salty (NaCl), bitter (quinidine), sweet (sucrose), sour (HCl), and umami (sodium glutamate) substances can be recognized from each other by using the optical taste sensor system. 11 refs., 8 figs., 2 tabs.
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.
Energy Technology Data Exchange (ETDEWEB)
Kunihiko, Nabeshima; Katsuo, Suzuki [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan); Nose, Shoichi; Kudo, Kazuhiko [Kyushu Univ., Fukuoka (Japan). Faculty of Engineering
1997-12-31
The purpose of this paper is to demonstrate a nuclear power plant monitoring system using artificial neural network (ANN). The major advantages of the monitoring system are that a multi-output process system can be modelled using measurement information without establishing any mathematical expressions. The dynamics model of reactor plant was constructed by using three layered auto-associative neural network with backpropagation learning algorithm. The basic idea of anomaly detection method is to monitor the deviation between process signals measured from actual plant and corresponding output signals from the ANN plant model. The simulator used is a self contained system designed for training. Four kinds of simulated malfunction caused by equipment failure during steady state operation were used to evaluate the capability of the neural network monitoring system. The results showed that this monitoring system detected the symptom of small anomaly earlier than the prevailing alarm system. (author). 7 refs, 7 figs, 2 tabs.
An Artificial Neural Network Modeling for Force Control System of a Robotic Pruning Machine
Directory of Open Access Journals (Sweden)
Ali Hashemi
2014-06-01
Full Text Available Nowadays, there has been an increasing application of pruning robots for planted forests due to the growing concern on the efficiency and safety issues. Power consumption and working time of agricultural machines have become important issues due to the high value of energy in modern world. In this study, different multi-layer back-propagation networks were utilized for mapping the complex and highly interactive of pruning process parameters and to predict power consumption and cutting time of a force control equipped robotic pruning machine by knowing input parameters such as: rotation speed, stalk diameter, and sensitivity coefficient. Results showed significant effects of all input parameters on output parameters except rotational speed on cutting time. Therefore, for reducing the wear of cutting system, a less rotational speed in every sensitivity coefficient should be selected.
Multiple modes of action potential initiation and propagation in mitral cell primary dendrite
DEFF Research Database (Denmark)
Chen, Wei R; Shen, Gongyu Y; Shepherd, Gordon M
2002-01-01
recordings with computational modeling to analyze action-potential initiation and propagation in the primary dendrite. In response to depolarizing current injection or distal olfactory nerve input, fast Na(+) action potentials were recorded along the entire length of the primary dendritic trunk. With weak......-to-moderate olfactory nerve input, an action potential was initiated near the soma and then back-propagated into the primary dendrite. As olfactory nerve input increased, the initiation site suddenly shifted to the distal primary dendrite. Multi-compartmental modeling indicated that this abrupt shift of the spike......-initiation site reflected an independent thresholding mechanism in the distal dendrite. When strong olfactory nerve excitation was paired with strong inhibition to the mitral cell basal secondary dendrites, a small fast prepotential was recorded at the soma, which indicated that an action potential was initiated...
Learning in the machine: The symmetries of the deep learning channel.
Baldi, Pierre; Sadowski, Peter; Lu, Zhiqin
2017-11-01
In a physical neural system, learning rules must be local both in space and time. In order for learning to occur, non-local information must be communicated to the deep synapses through a communication channel, the deep learning channel. We identify several possible architectures for this learning channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges: (1) symmetry of architectures; (2) symmetry of weights; (3) symmetry of neurons; (4) symmetry of derivatives; (5) symmetry of processing; and (6) symmetry of learning rules. Random backpropagation (RBP) addresses the second and third symmetry, and some of its variations, such as skipped RBP (SRBP) address the first and the fourth symmetry. Here we address the last two desirable symmetries showing through simulations that they can be achieved and that the learning channel is particularly robust to symmetry variations. Specifically, random backpropagation and its variations can be performed with the same non-linear neurons used in the main input-output forward channel, and the connections in the learning channel can be adapted using the same algorithm used in the forward channel, removing the need for any specialized hardware in the learning channel. Finally, we provide mathematical results in simple cases showing that the learning equations in the forward and backward channels converge to fixed points, for almost any initial conditions. In symmetric architectures, if the weights in both channels are small at initialization, adaptation in both channels leads to weights that are essentially symmetric during and after learning. Biological connections are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.
Benaouda, D.; Wadge, G.; Whitmarsh, R. B.; Rothwell, R. G.; MacLeod, C.
1999-02-01
In boreholes with partial or no core recovery, interpretations of lithology in the remainder of the hole are routinely attempted using data from downhole geophysical sensors. We present a practical neural net-based technique that greatly enhances lithological interpretation in holes with partial core recovery by using downhole data to train classifiers to give a global classification scheme for those parts of the borehole for which no core was retrieved. We describe the system and its underlying methods of data exploration, selection and classification, and present a typical example of the system in use. Although the technique is equally applicable to oil industry boreholes, we apply it here to an Ocean Drilling Program (ODP) borehole (Hole 792E, Izu-Bonin forearc, a mixture of volcaniclastic sandstones, conglomerates and claystones). The quantitative benefits of quality-control measures and different subsampling strategies are shown. Direct comparisons between a number of discriminant analysis methods and the use of neural networks with back-propagation of error are presented. The neural networks perform better than the discriminant analysis techniques both in terms of performance rates with test data sets (2-3 per cent better) and in qualitative correlation with non-depth-matched core. We illustrate with the Hole 792E data how vital it is to have a system that permits the number and membership of training classes to be changed as analysis proceeds. The initial classification for Hole 792E evolved from a five-class to a three-class and then to a four-class scheme with resultant classification performance rates for the back-propagation neural network method of 83, 84 and 93 per cent respectively.
Optimization of neural network architecture for classification of radar jamming FM signals
Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.
2017-05-01
The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.
Directory of Open Access Journals (Sweden)
Irina Popova
2013-08-01
Full Text Available Very-low-frequency/ low-frequency (VLF/LF sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself. To train a neural network, we first create a so-called ‘training set’. The ‘teacher’ specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007, and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed.
Directory of Open Access Journals (Sweden)
Manoj Bhasin
Full Text Available Vein graft failure occurs between 1 and 6 months after implantation due to obstructive intimal hyperplasia, related in part to implantation injury. The cell-specific and temporal response of the transcriptome to vein graft implantation injury was determined by transcriptional profiling of laser capture microdissected endothelial cells (EC and medial smooth muscle cells (SMC from canine vein grafts, 2 hours (H to 30 days (D following surgery. Our results demonstrate a robust genomic response beginning at 2 H, peaking at 12-24 H, declining by 7 D, and resolving by 30 D. Gene ontology and pathway analyses of differentially expressed genes indicated that implantation injury affects inflammatory and immune responses, apoptosis, mitosis, and extracellular matrix reorganization in both cell types. Through backpropagation an integrated network was built, starting with genes differentially expressed at 30 D, followed by adding upstream interactive genes from each prior time-point. This identified significant enrichment of IL-6, IL-8, NF-κB, dendritic cell maturation, glucocorticoid receptor, and Triggering Receptor Expressed on Myeloid Cells (TREM-1 signaling, as well as PPARα activation pathways in graft EC and SMC. Interactive network-based analyses identified IL-6, IL-8, IL-1α, and Insulin Receptor (INSR as focus hub genes within these pathways. Real-time PCR was used for the validation of two of these genes: IL-6 and IL-8, in addition to Collagen 11A1 (COL11A1, a cornerstone of the backpropagation. In conclusion, these results establish causality relationships clarifying the pathogenesis of vein graft implantation injury, and identifying novel targets for its prevention.
Directory of Open Access Journals (Sweden)
Lucimar M.F. de Carvalho
2008-06-01
Full Text Available OBJECTIVE: To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. METHOD: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication architecture and an artificial neural network with backpropagation learning algorithm (ANNB. RESULTS: The study was composed by 244 patients with a bigger frequency of the feminine sex. The number of right decisions at the test phase, obtained by the NEFCLASS and ANNB was 83.60% and 90.16%, respectively. The best sensibility result was attained by NEFCLASS (84.90%; the best specificity result were attained by ANNB with 95.65%. CONCLUSION: The proposed neuro-fuzzy system combined the artificial neural network capabilities in the pattern classifications together with the fuzzy logic qualitative approach, leading to a bigger rate of system success.OBJETIVO: Investigar diferentes operações aritméticas difusas para auxíliar no diagnóstico de eventos epilépticos e eventos não-epilépticos. MÉTODO: Um sistema neuro-difuso foi desenvolvido utilizando a arquitetura NEFCLASS (NEuro Fuzzy CLASSIfication e uma rede neural artificial com o algoritmo de aprendizagem backpropagation (RNAB. RESULTADOS: A amostra estudada foi de 244 pacientes com maior freqüência no sexo feminino. O número de decisões corretas na fase de teste, obtidas através do NEFCLASS e RNAB foi de 83,60% e 90,16%, respectivamente. O melhor resultado de sensibilidade foi obtido com o NEFCLASS (84,90%; o melhor resultado de especificidade foi obtido com a RNAB (95,65%. CONCLUSÃO: O sistema neuro-difuso proposto combinou a capacidade das redes neurais artificiais na classificação de padrões juntamente com a abordagem qualitativa da logica difusa, levando a maior taxa de acertos do sistema.
GPU-accelerated adjoint algorithmic differentiation
Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe
2016-03-01
Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the ;tape;. Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.
Wang, Kai; Riera, Jorge; Enjieu-Kadji, Herve; Kawashima, Ryuta
2013-07-01
With the rapid increase in the number of technologies aimed at observing electric activity inside the brain, scientists have felt the urge to create proper links between intracellular- and extracellular-based experimental approaches. Biophysical models at both physical scales have been formalized under assumptions that impede the creation of such links. In this work, we address this issue by proposing a multicompartment model that allows the introduction of complex extracellular and intracellular resistivity profiles. This model accounts for the geometrical and electrotonic properties of any type of neuron through the combination of four devices: the integrator, the propagator, the 3D connector, and the collector. In particular, we applied this framework to model the tufted pyramidal cells of layer 5 (PCL5) in the neocortex. Our model was able to reproduce the decay and delay curves of backpropagating action potentials (APs) in this type of cell with better agreement with experimental data. We used the voltage drops of the extracellular resistances at each compartment to approximate the local field potentials generated by a PCL5 located in close proximity to linear microelectrode arrays. Based on the voltage drops produced by backpropagating APs, we were able to estimate the current multipolar moments generated by a PCL5. By adding external current sources in parallel to the extracellular resistances, we were able to create a sensitivity profile of PCL5 to electric current injections from nearby microelectrodes. In our model for PCL5, the kinetics and spatial profile of each ionic current were determined based on a literature survey, and the geometrical properties of these cells were evaluated experimentally. We concluded that the inclusion of the extracellular space in the compartmental models of neurons as an extra electrotonic medium is crucial for the accurate simulation of both the propagation of the electric potentials along the neuronal dendrites and the
Three-Dimensional Passive-Source Reverse-Time Migration of Converted Waves: The Method
Li, Jiahang; Shen, Yang; Zhang, Wei
2018-02-01
At seismic discontinuities in the crust and mantle, part of the compressional wave energy converts to shear wave, and vice versa. These converted waves have been widely used in receiver function (RF) studies to image discontinuity structures in the Earth. While generally successful, the conventional RF method has its limitations and is suited mostly to flat or gently dipping structures. Among the efforts to overcome the limitations of the conventional RF method is the development of the wave-theory-based, passive-source reverse-time migration (PS-RTM) for imaging complex seismic discontinuities and scatters. To date, PS-RTM has been implemented only in 2D in the Cartesian coordinate for local problems and thus has limited applicability. In this paper, we introduce a 3D PS-RTM approach in the spherical coordinate, which is better suited for regional and global problems. New computational procedures are developed to reduce artifacts and enhance migrated images, including back-propagating the main arrival and the coda containing the converted waves separately, using a modified Helmholtz decomposition operator to separate the P and S modes in the back-propagated wavefields, and applying an imaging condition that maintains a consistent polarity for a given velocity contrast. Our new approach allows us to use migration velocity models with realistic velocity discontinuities, improving accuracy of the migrated images. We present several synthetic experiments to demonstrate the method, using regional and teleseismic sources. The results show that both regional and teleseismic sources can illuminate complex structures and this method is well suited for imaging dipping interfaces and sharp lateral changes in discontinuity structures.
Identification of lithofacies using Kohonen self-organizing maps
Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.
2002-01-01
Lithofacies identification is a primary task in reservoir characterization. Traditional techniques of lithofacies identification from core data are costly, and it is difficult to extrapolate to non-cored wells. We present a low-cost automated technique using Kohonen self-organizing maps (SOMs) to identify systematically and objectively lithofacies from well log data. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. A case study used five wells located in Appleton Field, Escambia County, Alabama (Smackover Formation, limestone and dolomite, Oxfordian, Jurassic). A five-input, one-dimensional output approach is employed, assuming the lithofacies are in ascending/descending order with respect to paleoenvironmental energy levels. To consider the possible appearance of new logfacies not seen in training mode, which may potentially appear in test wells, the maximum number of outputs is set to 20 instead of four, the designated number of lithosfacies in the study area. This study found eleven major clusters. The clusters were compared to depositional lithofacies identified by manual core examination. The clusters were ordered by the SOM in a pattern consistent with environmental gradients inferred from core examination: bind/boundstone, grainstone, packstone, and wackestone. This new approach predicted lithofacies identity from well log data with 78.8% accuracy which is more accurate than using a backpropagation neural network (57.3%). The clusters produced by the SOM are ordered with respect to paleoenvironmental energy levels. This energy-related clustering provides geologists and petroleum engineers with valuable geologic information about the logfacies and their interrelationships. This advantage is not obtained in backpropagation neural networks and adaptive resonance theory neural networks. ?? 2002 Elsevier Science Ltd. All rights reserved.
Algebraic and adaptive learning in neural control systems
Ferrari, Silvia
A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.
Directory of Open Access Journals (Sweden)
Erlandsson Anthony de Sousa
2003-12-01
Full Text Available A carne mecanicamente separada (CMS de frango é uma matéria-prima cárnea produzida através de equipamentos próprios do tipo desossadores mecânicos, utilizando partes de frango de baixo valor comercial como o dorso e o pescoço. Para determinação do teor de CMS utilizada na composição de produtos cárneos comerciais construímos uma rede neural artificial do tipo Backpropagation (BP. O objetivo deste trabalho foi treinar, testar e aplicar uma rede do tipo BP, com três camadas de neurônios, para previsão do teor de CMS a partir do teor de minerais de salsichas formuladas com diferentes teores de carne de frango mecanicamente separada. Utilizamos a composição mineral de 29 amostras de salsicha contendo diferentes teores de CMS e 22 amostras de produtos cárneos comerciais. A topologia da rede foi 5-5-1. O erro quadrático médio no conjunto de treinamento foi de 2,4% e na fase de teste foi de apenas 3,8%. No entanto, a aplicação da rede às amostras comerciais foi inadequada devido à diferença de ingredientes das salsichas usadas no treinamento e os ingredientes das amostras comerciais. A rede neural construída para determinação do teor de carne mecanicamente separada mostrou-se eficiente durante a fase de treinamento e teste da rede.Mechanically Deboned Poultry Meat (MDPM is constituted of the neck and back from chicken carcasses that are extracted in machine. An artificial neural network of the Back-Propagation type was built to determine the amount of MDPM in the composition of commercial foods. The objective of this work was to train, evaluate and apply a network of the Back-Propagation type, with three layers of neurons, in predicting the amount of MDPM in relation to the amount of minerals in the sausage. We used the mineral composition of 29 product samples that contained different amounts of MDPM and 23 commercial samples. The topology of the network was a 5-5-1. The average quadratic error in the training group was of
International Nuclear Information System (INIS)
Kim, Wan Joo
1993-02-01
In order to assist the operators at the transient states of a nuclear power plant, the automation of signal processing is needed. This study has the objective to process the signals from a nuclear power plant for the purpose of advising the operator. To meet this objective, in this study, two kinds of on-line signal processing system based on AI techniques are developed for the nuclear power plant application with on-line signals. First, an artificial neural network for signal prediction is developed for the adequate countermoves at transient states. The steam generator water level is adopted as the example and the outputs of a simulation program for the dynamics of steam generator combined with noises are used as the training patterns. For the training of the artificial neural network, the modified backpropagation algorithm is proposed for escaping quickly from local minima. The modified algorithm is different from the ordinary backpropagation algorithm in the aspect that the training rate coefficient is repeatedly adjusted randomly and taken when the training is improved. This trial has an effect to search for an adequate magnitude of a training rate coefficient. The comparison result shows that the modified algorithm enables the neural network to be trained more quickly. The simulation result shows that the outputs of the artificial neural network are not sensitive to noises. Using the artificial neural networks proposed in this thesis, the operators can predict the next status of a plant and can take actions to maintain the stability of plant. Second, the multi sensor integration system has been developed for the identification of transient states. The developed system is divided into two parts; pre-processors and a fusion part. An artificial neural network is adopted in the fusion part to include the knowledge about the identification and to make a decision of the transient state. The developed pre-processors play a role of classifying the trend types of
An ensemble classification approach for improved Land use/cover change detection
Chellasamy, M.; Ferré, T. P. A.; Humlekrog Greve, M.; Larsen, R.; Chinnasamy, U.
2014-11-01
Change Detection (CD) methods based on post-classification comparison approaches are claimed to provide potentially reliable results. They are considered to be most obvious quantitative method in the analysis of Land Use Land Cover (LULC) changes which provides from - to change information. But, the performance of post-classification comparison approaches highly depends on the accuracy of classification of individual images used for comparison. Hence, we present a classification approach that produce accurate classified results which aids to obtain improved change detection results. Machine learning is a part of broader framework in change detection, where neural networks have drawn much attention. Neural network algorithms adaptively estimate continuous functions from input data without mathematical representation of output dependence on input. A common practice for classification is to use Multi-Layer-Perceptron (MLP) neural network with backpropogation learning algorithm for prediction. To increase the ability of learning and prediction, multiple inputs (spectral, texture, topography, and multi-temporal information) are generally stacked to incorporate diversity of information. On the other hand literatures claims backpropagation algorithm to exhibit weak and unstable learning in use of multiple inputs, while dealing with complex datasets characterized by mixed uncertainty levels. To address the problem of learning complex information, we propose an ensemble classification technique that incorporates multiple inputs for classification unlike traditional stacking of multiple input data. In this paper, we present an Endorsement Theory based ensemble classification that integrates multiple information, in terms of prediction probabilities, to produce final classification results. Three different input datasets are used in this study: spectral, texture and indices, from SPOT-4 multispectral imagery captured on 1998 and 2003. Each SPOT image is classified
Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok
2013-02-01
The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic
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
Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain
Dai, Yonghui; Han, Dongmei; Dai, Weihui
2014-01-01
The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. PMID:24782659
Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment
Directory of Open Access Journals (Sweden)
Diego José Luis Botia Valderrama
2016-01-01
Full Text Available The measurement and evaluation of the QoE (Quality of Experience have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks and the RNNs (Random Neural Networks is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score and the PSNR (Peak Signal Noise Ratio, SSIM (Structural Similarity Index Metric, VQM (Video Quality Metric, and QIBF (Quality Index Based Frame. The proposed model allows establishing the QoS (Quality of Service based in the strategy Diffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error, and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.
A New Delay Connection for Long Short-Term Memory Networks.
Wang, Jianyong; Zhang, Lei; Chen, Yuanyuan; Yi, Zhang
2017-12-17
Connections play a crucial role in neural network (NN) learning because they determine how information flows in NNs. Suitable connection mechanisms may extensively enlarge the learning capability and reduce the negative effect of gradient problems. In this paper, a new delay connection is proposed for Long Short-Term Memory (LSTM) unit to develop a more sophisticated recurrent unit, called Delay Connected LSTM (DCLSTM). The proposed delay connection brings two main merits to DCLSTM with introducing no extra parameters. First, it allows the output of the DCLSTM unit to maintain LSTM, which is absent in the LSTM unit. Second, the proposed delay connection helps to bridge the error signals to previous time steps and allows it to be back-propagated across several layers without vanishing too quickly. To evaluate the performance of the proposed delay connections, the DCLSTM model with and without peephole connections was compared with four state-of-the-art recurrent model on two sequence classification tasks. DCLSTM model outperformed the other models with higher accuracy and F1[Formula: see text]score. Furthermore, the networks with multiple stacked DCLSTM layers and the standard LSTM layer were evaluated on Penn Treebank (PTB) language modeling. The DCLSTM model achieved lower perplexity (PPL)/bit-per-character (BPC) than the standard LSTM model. The experiments demonstrate that the learning of the DCLSTM models is more stable and efficient.
International Nuclear Information System (INIS)
Moreira, M.C.F.; Conti, C.C.; Schirru, R.
2010-01-01
An NaI(Tl) multidetector layout combined with the use of Monte Carlo (MC) calculations and artificial neural networks(ANN) is proposed to assess the radioactive contamination of urban and semi-urban environment surfaces. A very simple urban environment like a model street composed of a wall on either side and the ground surface was the study case. A layout of four NaI(Tl) detectors was used, and the data corresponding to the response of the detectors were obtained by the Monte Carlo method. Two additional data sets with random values for the contamination and for detectors' response were also produced to test the ANNs. For this work, 18 feedforward topologies with backpropagation learning algorithm ANNs were chosen and trained. The results showed that some trained ANNs were able to accurately predict the contamination on the three urban surfaces when submitted to values within the training range. Other results showed that generalization outside the training range of values could not be achieved. The use of Monte Carlo calculations in combination with ANNs has been proven to be a powerful tool to perform detection calibration for highly complicated detection geometries.
Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir
2017-04-19
As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems.
PREDIKSI PENGGUNA BUS TRANS SARBAGITA DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM
Directory of Open Access Journals (Sweden)
SLAMET SAMSUL HIDAYAT
2013-11-01
Full Text Available Trans Sarbagita is a public transportation services people at Denpasar, Badung, Gianyar and Tabanan. Trans Sarbagita is aimed to resolve a problems caused by accretion volume of vehicles in Bali. This study conducted to forecast the number of Trans Sarbagita passengers in 2013 using ANFIS. The ANFIS system composed by five layers where each layers has a different function and its divide in two phases, i.e. forward and backward phases. The ANFIS uses a hybrid learning algorithm which is a combination of Least Squares Estimator (LSE on forwards phases and Error Backpropagation (EBP on the backward phases. The results show, ANFIS with six inputs with M.F of Pi produces smallest error, compared to seven and eight input and M.F gauss and generalizedbell. Forecast of Trans Sarbagita passenger numbers in 2013 have to fluctuated every day and the average of passenger’s Trans Sarbagita for a day is 1627 passengers with MSE equal to 10210 and MAPE is 4.01%.
Multi-source waveform inversion of marine streamer data using the normalized wavefield
Choi, Yun Seok
2012-01-01
Even though the encoded multi-source approach dramatically reduces the computational cost of waveform inversion, it is generally not applicable to marine streamer data. This is because the simultaneous-sources modeled data cannot be muted to comply with the configuration of the marine streamer data, which causes differences in the number of stacked-traces, or energy levels, between the modeled and observed data. Since the conventional L2 norm does not account for the difference in energy levels, multi-source inversion based on the conventional L2 norm does not work for marine streamer data. In this study, we propose the L2, approximated L2, and L1 norm using the normalized wavefields for the multi-source waveform inversion of marine streamer data. Since the normalized wavefields mitigate the different energy levels between the observed and modeled wavefields, the multi-source waveform inversion using the normalized wavefields can be applied to marine streamer data. We obtain the gradient of the objective functions using the back-propagation algorithm. To conclude, the gradient of the L2 norm using the normalized wavefields is exactly the same as that of the global correlation norm. In the numerical examples, the new objective functions using the normalized wavefields generate successful results whereas conventional L2 norm does not.
Source-independent elastic waveform inversion using a logarithmic wavefield
Choi, Yun Seok
2012-01-01
The logarithmic waveform inversion has been widely developed and applied to some synthetic and real data. In most logarithmic waveform inversion algorithms, the subsurface velocities are updated along with the source estimation. To avoid estimating the source wavelet in the logarithmic waveform inversion, we developed a source-independent logarithmic waveform inversion algorithm. In this inversion algorithm, we first normalize the wavefields with the reference wavefield to remove the source wavelet, and then take the logarithm of the normalized wavefields. Based on the properties of the logarithm, we define three types of misfit functions using the following methods: combination of amplitude and phase, amplitude-only, and phase-only. In the inversion, the gradient is computed using the back-propagation formula without directly calculating the Jacobian matrix. We apply our algorithm to noise-free and noise-added synthetic data generated for the modified version of elastic Marmousi2 model, and compare the results with those of the source-estimation logarithmic waveform inversion. For the noise-free data, the source-independent algorithms yield velocity models close to true velocity models. For random-noise data, the source-estimation logarithmic waveform inversion yields better results than the source-independent method, whereas for coherent-noise data, the results are reversed. Numerical results show that the source-independent and source-estimation logarithmic waveform inversion methods have their own merits for random- and coherent-noise data. © 2011.