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检索条件"主题词=Backpropagation Algorithms"

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Memory based processor array for artificial neural networks

Memory based processor array for artificial neural networks

引用

International Conference on Neural Networks

作者： Youngsik Kim Mi-Jung Noh Tack-Don Han Shin-Dug Kim Sung-Bong Yang Parallel Processing System Laboratory Department of Computer Science Yonsei University Seoul South Korea

ISBN: (纸本)0780341228

In this paper an effective memory-processor integrated architecture, called memory based processor array for artificial neural networks (MPAA), is proposed. The MPAA can be easily integrated into any host system via memory interface. Specifically, the MPAA system provides an efficient mechanism for its local memory accesses allowed by the row basis and the column basis using the hybrid row and column decoding, which is suitable for the computation model of ANNs such as the accessing and alignment patterns given for matrix-by-vector operations. Mapping algorithms to implement the multilayer perceptron with backpropagation learning on the MPAA system are also provided. The proposed algorithms support both neuron and layer level parallelisms which allow the MPAA system to operate the learning phase as well as the recall phase in the pipelined fashion. Performance evaluation is provided by detailed comparison in terms of two metrics such as the cost and the number of computation steps.

关键词： Artificial neural networks Computer architecture Parallel processing backpropagation algorithms Neurons Decoding Computational modeling Memory architecture Bandwidth Read-write memory

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Removal of Muscular and Artefacts Noise from the ECG by a Neural Network

Removal of Muscular and Artefacts Noise from the ECG by a Ne...

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Jorge Mateo Sotos Jose Manuel Blas Arnau Ana Maria Torres Aranda Cesar Sanchez Melendez Castilla la Mancha University Spain Innovation in Bioengineering Group University of Castilla La Mancha (UCLM) Spain

The following work presents a system of cancellation muscular and artefacts noise in biomedical signals with a multilayer structure of neural networks. In this study in particular the signal has been analyzed from electrocardiogram (ECG). This system consists in a simple structure similar to the neuronal network MADALINE (Multiple ADAptive LINear Element), which is used like a structure. The proposed system is a growed artificial neuronal network which allows to optimize, the number of nodes of the hidden layer and the matrixes of coefficients. The coefficients matrix are optimized using the algorithm of simultaneous perturbation which requires a smaller computer complexity than the required one by the backpropagation algorithm. The comparison between the different typical methods (filter FIR, biorthogonal wavelet 6,8, filtered adaptive LMS) and the system based on neural multilayer networks proposed, is obtained calculating the cross correlation between the input signal to the system and the exit, in addition for the calculation of the SIR (relation parameter signal interference). The comparison shows that the neural networks method is able to better preserve the signal waveform at system output with an improved noise reduction in comparison with traditional techniques. With this method it is possible to eliminate white, artefacts and muscular noise. The rest of filter systems give worse results.

关键词： Electrocardiography Neural networks Finite impulse response filter Multi-layer neural network Noise cancellation Biological neural networks backpropagation algorithms Adaptive filters Artificial neural networks Signal analysis

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Utilization of hierarchical structure stochastic automata for neural network learning

Utilization of hierarchical structure stochastic automata fo...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： N. Baba Y. Mogami Information Science Osaka Kyoiku University Kashihara Osaka Japan Faculty of Engineering University of Tokushima Tokushima Japan

The backpropagation method was proposed by Rumelhart and McClelland (1986). It has contributed a lot to the literature of neural network computing. However, it has become clear that the BP method has several limitations. One of the most important limitations is that it often fails to find a global minimum of total error function of neural networks. In order to overcome this limitation, we have proposed a hybrid algorithm which combines the random optimization method with the modified backpropagation method. This hybrid algorithm has been successfully applied to several actual problems such as forecast of stock price, construction of an intelligent computer gaming system, etc. However, even this hybrid algorithm sometimes needs a large number of calculation to escape from local minima of the total error function of neural networks. In this paper, learning performance of hierarchical structure stochastic automata, is utilized to accelerate convergence of this hybrid algorithm. Several computer simulation results confirm our ideas.< >

关键词： Stochastic processes Neural networks backpropagation algorithms Computer errors Computer networks Optimization methods Hybrid intelligent systems Learning automata Acceleration Convergence

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Robust Supplementary Controllers for AVR and PSS

Robust Supplementary Controllers for AVR and PSS

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IEEE India Conference (INDICON)

作者： M. Uma Vani G. S. Raju K. R. L. Prasad EEE Department LBRCE Mylavaram India EEE Department VNR VJIT Hyderabad India

ISBN: (纸本)9781424448586;9781424448593

The paper presents a step-by-step design methodology of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and H ¿ optimization methods based Automatic Voltage Regulator (AVR) and Power System Stabilizer (PSS).This paper demonstrates their performance in a single-machine-infinite-bus power system through digital simulation. The ANFIS design employs a zero and a first order Sugeno fuzzy model, whose parameters are tuned off-line through hybrid learning algorithm. This algorithm is a combination of Least Square Estimator and Error backpropagation method. The performance of the ANFIS and H ¿ optimization methods based AVR and PSS in damping oscillation is then compared with conventional AVR and PSS performance. It is found that the damping characteristics of both ANFIS and optimization based H ¿ AVR and PSS are better than the conventional AVR and PSS. The effectiveness of the proposed ANFIS-based AVR and PSS in small-signal stability is thus established. Index Temts- AVR PSS, Fuzzy Logic, Adaptive NeuroFuzzy Inference System, H ¿ optimization, SugenoFuzzy Model, Hybrid Learning Algorithm.

关键词： Robust control Hybrid power systems Power system modeling Adaptive systems Optimization methods Power system simulation backpropagation algorithms Damping Automatic control Design methodology

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Functional site prediction on the DNA sequence by artificial neural networks

Functional site prediction on the DNA sequence by artificial...

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IEEE International Joint Symposia on Intelligence and Systems

作者： A. Hatzigeorgiou N. Mache M. Reczko Biol. Structure & BioComputer Progr European Molecular Biology Laboratory Heidelberg Germany IPVS Universität Stuttgart Stuttgart Germany Synaptic Limited Greece

A modular system of neural networks is used to identify genes in DNA sequences of eukaryotic organisms. The identification task is decomposed into the detection of distinct signals using separate neural network modules. Such signals are coding regions, splice sites and transcription start regions (cap-site). A focus of the work is the use of back-percolation, cascade correlation, and time-delay neural networks. These give, in this particular application, better generalization than the well known backpropagation algorithm. This system achieves a prediction accuracy comparable to the traditionally designed gene identification packages and is able to produce more accurate protein sequences from the constructed gene structures.

关键词： DNA Sequences Artificial neural networks Organisms Signal processing Signal detection Neural networks backpropagation algorithms Accuracy Packaging

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Modular configuration Rprop/SF-ANN for microwave waveguide on EBG structures modeling

Modular configuration Rprop/SF-ANN for microwave waveguide o...

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Microwave, MTT-S International Symposium

作者： E.N.R. Queiroz Fernandes P.H. da Fonseca Silva M.A. Barbosa de Melo A.G. d'Assuncao UERN Mossoro Rio Grande do Norte Brazil CEFET-PB Joao Pessoa Paraiba Brazil UFCG Campina Grande Paraiba Brazil UFRN-CT-DEE Natal Rio Grande do Norte Brazil

This work describes a new class of modular network that has been used to analyze microwave waveguides on electromagnetic bandgap (EBG) structures. The proposed modular configuration employs three or more SF-ANN (Sample Function Artificial Neural Network) trained by the Resilient backpropagation (RProp) algorithm. Two types of waveguides are modeled by this new modular neurotechnique that are the rectangular waveguide and the parallel plate waveguide with EBG structures. The rectangular waveguide uses the UC-EBG structure, which consists of a metal pad and four connecting branches. This device is employed in the development of high-performance and compact circuit components for microwave and millimeter-wave frequencies. The parallel plate waveguide is formed by the microstrip ground plane and a metal plate with EBG elements on top that works like one metal shield. This structure has the capability of suppressing the propagation of unwanted parallel plate modes. Simulations through the proposed neural network models for microwave waveguides on EBG structures gave results in excellent agreement with measured results available in the literature. Furthermore, this technique is able to generalize for new values of input parameters and frequency ranges, validating it as a good estimator into the mapping regions where no previous numerical or experimental information is available.

关键词： Periodic structures Metamaterials Electromagnetic waveguides Rectangular waveguides Frequency estimation Electromagnetic analysis Artificial neural networks backpropagation algorithms Joining processes Microwave circuits

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Estimation of mixing ratios of two mixture solutions using shear horizontal surface acoustic wave (SH-SAW) sensors

Estimation of mixing ratios of two mixture solutions using s...

引用

National Conference on Radio Science (NSRC)

作者： Taha El-Sayed Taha Mohmed A.A. Eldosoky Ahmed Mostafa El-Sayed Communication Department Faculty of Electronics Engineering Menoufia University Egypt Department of Biomedical Eng Faculty of Engineering Helwan university Egypt

Recently, surface acoustic waves sensors are developed and used in many applications. Estimation of the mixed electrolytes is one of these applications. This is done by using a flow of this solution passed between two interdigital transducers of SAW sensor. A certain kind of SAW will be generated and called shear horizontal (SH-SAW). This wave is very sensitive to these solutions, where clear perturbations in the attenuation and the frequency shifts of the received signal are observed. Experimental results of the flow liquid system with SH-SAW sensor are taken. From this data, an algorithm is proposed with the aid of backpropagation neural network that will be used directly to identify the two components of ions and determine the mixing ratio between the electrolytes. The durations of the time responses, fast Fourier transform (FFT), discrete cosine transform (DCT) are used as features in this algorithm.

关键词： Surface acoustic waves Acoustic waves Acoustic sensors backpropagation algorithms Discrete cosine transforms Acoustic transducers Attenuation Frequency Fluid flow Sensor systems

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On the PDF Estimation for Information Theoretic Learning for Neural Networks

On the PDF Estimation for Information Theoretic Learning for...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Tokunbo Ogunfunmi Manas Deb Department of Electrical Engineering Santa Clara University Santa Clara CA USA

ISBN: (纸本)9781728102436;9789881476852

Information-Theoretic Learning (ITL) is one of the new methods gaining popularity used for adaptive signal processing learning algorithms and has many advantages compared to traditional method which minimizes the mean square error (MSE). Previously [12], we described a method based on the backpropagation algorithm to train a type of neural network called the multi-layer perceptron (MLP) using Information Theoretic Learning (ITL) techniques. Our method was developed to train MLPs by utilizing the minimum error entropy (MEE) of the error samples. The MSE is a second order statistic whereas the MEE uses the probability density function of the error samples. Therefore, the MEE technique uses higher order statistical information from the error samples to adapt the weights of the neural network. When the error distribution is non-gaussian, higher order statistical information can lead to faster training and smaller residual training error. The Probability Density Function (PDF) estimation using the Parzen window could affect the accuracy of the Back-Propagation training. In this paper, we investigate the effects of the Parzen Window estimator on the efficacy of the ITL training using Renyi's Entropy and Shannon's Entropy. Using different estimators and simulations, we compare MLP using the typical backpropagation algorithm (using MSE and cross-entropy) and also one using ITL methods in terms of convergence speed of the weights, PDF estimator and the residual error. We use standard data sets (like the MNIST handwriting data set available on the Internet) to train and test the MLP using all these methods. Simulation results compare the prediction accuracy of the three different types of backpropagation algorithms (MSE, Shannon's cross-entropy, Renyi's quadratic entropy) in the paper.

关键词： Entropy Probability density function Neurons Training Microsoft Windows Signal processing algorithms backpropagation algorithms

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The dependence identification neural network construction algorithm

The dependence identification neural network construction al...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： J.O. Moody P.J. Antsaklis Department of Electrical Engineering University of Notre Dame Notre Dame IN USA

An algorithm for constructing and training multilayer neural networks, dependence identification, is presented in this paper. Its distinctive features are that: 1) it transforms the training problem into a set of quadratic optimization problems that are solved by a number of linear equations; and 2) it constructs an appropriate network to meet the training specifications. The architecture and network weights produced by the algorithm can also be used as initial conditions for further online training by backpropagation or a similar iterative gradient descent training algorithm if necessary. In addition to constructing an appropriate network based on training data, the dependence identification algorithm significantly speeds up learning in feedforward multilayer neural networks compared to standard backpropagation.< >

关键词： Neural networks Multi-layer neural network backpropagation algorithms Neurons Iterative algorithms Pattern matching Transforms Equations Feedforward neural networks Intelligent networks

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Flexible data parallel training of neural networks using MIMD-Computers

Flexible data parallel training of neural networks using MIM...

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Euromicro Workshop on Parallel and Distributed Processing

作者： M. Besch H.W. Pohl German National Research Center for Computer Science GMD FIRST Berlin Germany German Nat. Res. Center for Comput. Sci. Berlin Germany

An approach to flexible and efficient data parallel simulation of neural networks on large scale MIMD machines is presented. We regard the exploitation of the inherent parallelism of neural network models as necessary if larger networks and training data sets respectively are to be considered. Nevertheless it is essential to provide the flexibility for investigating various training algorithms or creating new ones without intimate knowledge of the underlaying hardware architecture and communication subsystem. We therefore encapsulated functional units being substantial with respect to the parallel execution. Based on these components even complex training algorithms can be formulated as a sequential program while the details of the parallelization are transparent. Communication tasks are performed very efficiently by using a distributed logarithmic tree. This logical structure additionally allows a direct mapping of the algorithm on various important parallel architectures. Finally a theoretical time complexity model is given and the correspondence to empirical data is shown.< >

关键词： Neural networks Parallel processing Concurrent computing backpropagation algorithms Object oriented modeling Computational modeling Computer simulation Large-scale systems Parallel algorithms Distributed computing

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