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International Symposium on VLSI Design, Automation and Test

作者： Hsu, Terng-Ren Chen, Chi-Shi Hsu, Terng-Yin Lee, Chen-Yi Natl Chiao Tung Univ Dept Elect Engn 1001 Univ Rd Hsinchu 300 Taiwan Chung Hua Univ Dept Microelect Engn Hsinchu 300 Taiwan Natl Chip Implementat Ctr Natl Appl Res Lab Hsinchu 300 Taiwan Natl Chiao Tung Univ Dept Comp Sci & Informat Engn Hsinchu 300 Taiwan

ISBN: (纸本)9781424405824

in this work, we base on generalized multi-layered perceptron neural networks with backpropagation algorithm (Generalized MLP/BP) to construct multi-input multi-output (MIMO) decision feedback equalizers (DFEs). The proposal is used to recover distorted nonretum-to-zero (NRZ) data in wireline parallel bandlimited channels. From the simulations, we note that the proposed design can recover severe distorted NRZ data as well as suppress intersymbol interference (ISI), adjacent channel interference (ACI) and background noise. The better BER performance as compared to a set of LMS DFEs and an MLPIBP-based MIMO DFE is achieved in the wireline parallel band-limited channels where the data rate is ten times as much as the channel bandwidth.

关键词： Automatic car identification channel bandwidth backpropagation algorithms Data transfer rates bit error rate performance BACKGROUND NOISE

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Self-organizing neural networks by dynamic and spatial changing weights 4

Self-organizing neural networks by dynamic and spatial chang...

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4th International Symposium on Uncertainty Modeling and Analysis

作者： Homma, N Gupta, MM Yoshizawa, M Abe, K Tohoku Univ Coll Med Sci Aoba Ku Sendai Miyagi 9808575 Japan

ISBN: (纸本)0769519970

We propose a self-organizing neural structure with dynamic and spatial changing weights for forming a feature space representation of concepts. An essential core of this self-organization is an appropriate combination of an unsupervised learning with incomplete information for the dynamic changing and an extended Hebbian rule for a signal-driven spatial changing. A concept formation problem requires the neural network to acquire the complete feature space structure of concept information using an incomplete observation of the concept. The informational structure can be stored as the connection structure of self-organizing network by using the two rules: The Hebbian rule can create a necessary connection, while unsupervised learning can delete unnecessary connections. Finally concept formation ability of the proposed neural network is proven under some conditions.

关键词： backpropagation algorithms Biological neural networks Biomedical engineering Cognition Educational institutions Hebbian theory Learning systems Neural networks Self-organizing networks Unsupervised learning

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A neural network-based direct inverse control for active control of vibrations of mechanical systems 6

A neural network-based direct inverse control for active con...

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6th Brazilian Symposium on Neural Networks

作者： de Abreu, GLCM Teixeira, RL Ribeiro, JF Univ Fed Uberlandia Dep Engn Mecan Lab Dinam Sistemas Mecanicos BR-38400902 Uberlandia MG Brazil

ISBN: (纸本)0769508561

This paper describes the use of Artificial Neural Networks for the control of vibrations of a mechanical system using its experimental direct inverse model. The neural controller is trained to model the experimental inverse model of the plans using the back-propagation algorithm with si, annealing. The inverse nlodel of the plant is obtained by the training mechanism that rises experimental input and output data. After the training the neural network is used as a forward controller. The efficiency and the robustness of the controller are shown through experimental rests. The neural control algorithm is implemented in a computer and the performance of controller is evaluated under a set of experimental tests, made to the active control of vibrations of a mechanical system of orle degree of freedom actuated by magnetic actuators.

关键词： Artificial neural networks backpropagation algorithms Control systems Inverse problems Mechanical systems Neural networks Robust control Simulated annealing Testing Vibration control

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Standardization of Neural Network Terminology

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IEEE Transactions on Neural Networks 1990年第2期1卷 244-245页

作者： Eberhart, Russell C. Applied Physics Laboratory Johns Hopkins University Laurel MD 20707 United States

It is desirable to move toward commonly accepted terminology in the neural network field. This letter outlines the initial activities of an Ad Hoc Standards Committee established by the IEEE Neural Networks Council to... 详细信息

关键词： Standardization Neural networks Terminology backpropagation algorithms Control systems Intelligent robots Robot control Knowledge based systems Adaptive control Nonlinear systems

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Supervised backpropagation neural networks for the classification of ultrasonic signals from fiber microcracking in metal matrix composites

Supervised backpropagation neural networks for the classific...

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IEEE Symposium (IUS) Ultrasonics

作者： L.L. Mann T.E. Matikas P. Karpur S. Krishnamurthy NDE Branch Materials Directorate OH USA Research Institute University of Dayton Dayton OH USA Universal Energy Systems Inc. Dayton OH USA

The results of the application of supervised backpropagation neural networks to the classification of ultrasonic signals obtained from a model metal matrix composite are presented. This composite is made of a single fiber embedded in a Ti-24Al-llNb matrix and is used for the characterization of the fiber-matrix interface. The neural network is implemented in the frequency domain with two hidden layers and shows excellent discrimination capability when the network is trained with a judicious choice for the training set of ultrasonic signals. The sensitivity of the performance of the network to the number of examples used for training and the robustness of the algorithm to the change in the training set are discussed.< >

关键词： Neural networks Optical fiber testing backpropagation algorithms Composite materials Inorganic materials Titanium alloys Signal processing Image processing Aerospace materials System testing

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Performance analysis of backpropagation Algorithm of Artificial Neural Networks in Verilog

Performance analysis of Backpropagation Algorithm of Artific...

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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： J Amrutha A S Remya Ajai Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

Artificial neural networks learn or get trained to execute definite tasks, instead of programmed computational systems through training algorithms such as backpropagation algorithm. It is the basic tool for pattern classification application of ANN in field of medical diagnosis and remote sensing. This method involves changing the weights in the network using a training set of input output examples. Digital implementation of these neural networks for classification is suitable as it preserves the parallel architecture of the neurons and can be reconfigured by the user with FPGA. This parallelism in neural networks make it potentially fast for computation of tasks. This work implements backpropagation training algorithm in verilog with Modelsim-Altera 6.5b for a feedforward neural network. Since multiplier algorithms determine the operational speed and power consumption; a performance analysis is made based on different multipliers. The work can be further extended to the implementation of artificial neural networks on FPGA and to implement a classification application in the trained network.

关键词： Artificial neural networks Neurons Training Biological neural networks Field programmable gate arrays backpropagation algorithms backpropagation

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Lazy training: improving backpropagation learning through network interaction

Lazy training: improving backpropagation learning through ne...

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International Joint Conference on Neural Networks (IJCNN)

作者： M.E. Rimer T.L. Andersen T.R. Martinez Computer Science Department Brigham Young University Provo UT USA

ISBN: (纸本)0780370449

backpropagation, similar to most high-order learning algorithms, is prone to overfitting. We address this issue by introducing interactive training (IT), a logical extension to backpropagation training that employs interaction among multiple networks. This method is based on the theory that centralized control is more effective for learning in deep problem spaces in a multi-agent paradigm. IT methods allow networks to work together to form more complex systems while not restraining their individual ability to specialize. Lazy training, an implementation of IT that minimizes misclassification error, is presented. Lazy training discourages overfitting and is conducive to higher accuracy in multiclass problems than standard backpropagation. Experiments on a large, real world OCR data set have shown interactive training to significantly increase generalization accuracy, from 97.86% to 99.11%. These results are supported by theoretical and conceptual extensions from algorithmic to interactive training models.

关键词： backpropagation algorithms Optical character recognition software Training data Network topology Computer science Centralized control Artificial neural networks Robustness Databases Gaussian noise

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The effects of quantization on the backpropagation learning

The effects of quantization on the backpropagation learning

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International Conference on Neural Networks

作者： K. Ikeda A. Suzuki K. Nakayama Department of Elec. Computer Engineering Kanazawa University Kanazawa Japan

The effects of the quantization of the parameters of a learning machine are discussed. The learning coefficient should be as small as possible for a better estimate of parameters. On the other hand, when the parameters are quantized, it should be relatively larger in order to avoid the paralysis of learning originated from the quantization. How to choose the learning coefficient is given in this paper from the statistical point of view.

关键词： Quantization Machine learning Multilayer perceptrons Computer errors Neurons Parameter estimation Circuits Stochastic processes backpropagation algorithms Equations

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A suggestion for solving negative transfer problem in VLSI backpropagation neural networks

A suggestion for solving negative transfer problem in VLSI b...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jien-Chung Lo S. Kolluri Department of Electrical Engineering University of Rhode Island Kingston RI USA

To overcome negative transfer problem in backpropagation (BP) neural networks, a modified BP algorithm has been proposed. However, for VLSI BP neural networks, this means an extensive design efforts. We analyze in this paper the nature of negative transfer problem and identify a simple technique to solve the negative transfer problem in VLSI BP neural networks.< >

关键词： Intelligent networks Very large scale integration Neural networks backpropagation algorithms Neurons Histograms Gaussian distribution Performance analysis Algorithm design and analysis Network-on-a-chip

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Design of the adaptive noise canceler using neural network with backpropagation algorithm

Design of the adaptive noise canceler using neural network w...

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International Forum on Strategic Technology, IFOST

作者： Hyung Suk Chu Chong Koo An DSP Laboratory Department of Electronics Eng University of Ulsan Ulsan South Korea

In this paper, an adaptive noise canceller using a neural network with backpropagation is designed. The adaptive noise canceller using the least mean square algorithm has the large correlativity of the reference signal and shows the limitation of the big signal to noise ratio. The system proposed in this paper plays an important role in denoising these signals. In addition, the experiments are carried out to analyze the effects of the number of hidden layers and nodes about the system. The performance of the proposed adaptive noise canceller is compared with that of the system which is used the least mean square algorithm.

关键词： Algorithm design and analysis Noise cancellation Neural networks Signal to noise ratio Signal processing Least mean square algorithms Adaptive filters Signal processing algorithms Adaptive signal processing backpropagation algorithms

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