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检索条件"主题词=Backpropagation Algorithms"

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Fuzzy neural modeling using stable learning algorithm

Fuzzy neural modeling using stable learning algorithm

引用

American Control Conference (ACC)

作者： Wen Yu Xiaoou Li Departamento de Control Automatico CINVESTAV-IPN México D.F. México Sección de Computación CINVESTAV-IPN México D.F México

ISBN: (纸本)0780378962

In general, fuzzy neural networks cannot match nonlinear systems exactly. Unmodeled dynamic can lead parameters drive and even instability problem. Some robust modifications must be contained, in order to guarantee Lyapunov stability. In this paper input-to-state stability is applied to access robust training algorithm of the fuzzy neural networks. We state that the normal gradient descent law with a time-varying learning rate is stable in the sense of L/sub /spl infin//. The fuzzy neural networks approximation, which is suggested in this paper, needs no robust modification and is robust to any bounded uncertainty.

关键词： Fuzzy neural networks Robust stability Uncertainty Neural networks Fuzzy systems Fuzzy control Fuzzy logic backpropagation algorithms Noise robustness Robust control

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Application of neural fuzzy network to pulse compression with binary phase code

Application of neural fuzzy network to pulse compression wit...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Fun-Bin Duh Chia-Feng Juang Chin-Teng Lin Department of Electrical and Control Engineering of Electrical Engineering National Chiao Tung University Hsinchu Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

ISBN: (纸本)0780378105

To solve the existing dilemma between making good range resolution and maintaining the low average transmitted power, it is necessary for the pulse compression processing to give low range sidelobes in the modern high-resolution radar systems. The traditional pulse compression algorithms based on 13-element Barker code such as direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter have been developed, and the neural network algorithms were issued recently. However, the traditional algorithms cannot achieve the requirement of high signal-to-sidelobe ratio, and the normal neural network such as backpropagation (BP) network usually produces the extra problems of low convergence speed and sensitive to the Doppler frequency shift. To overcome these defects, a new approach using a neural fuzzy network with binary phase code to deal with pulse compression in a radar system is presented in this paper. The 13-element Barker code used as the binary phase signal code is carried out by six-layer self-constructing neural fuzzy network (SONFIN) with supervised learning algorithm. Simulation results show that this neural fuzzy network pulse compression (NFNPC) algorithm has the significant advantages in noise rejection performance, range resolution ability and Doppler tolerance, which are superior to the traditional and BP algorithms, and has faster convergence speed than BP algorithm.

关键词： Fuzzy neural networks Pulse compression methods Nonlinear filters Neural networks backpropagation algorithms Convergence Radar Autocorrelation Least squares methods Linear programming

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An adaptive RBFN-based filter for adaptive noise cancellation

An adaptive RBFN-based filter for adaptive noise cancellatio...

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IEEE Conference on Decision and Control

作者： Zhengrong Li Meng Joo Er Yang Gao School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)0780379241

In this paper, a new adaptive radial-basis-function-networks- (RBFN-) based filter for the adaptive noise cancellation (AXC) problem is proposed. The algorithm of structure identification and parameters adjustment is developed. The proposed RBFN-based filtering approach implements Takagi-Sugeno-Kang (TSK) fuzzy systems functionally. The RBFN-based filter has three major features: (1) no space pre-partitioning is needed; (2) no predetermination, such as the number of RBF neurons (fuzzy rules), must be given; (3) fast learning speed is achieved. Simulation results demonstrate that the proposed adaptive RBFN-based filter can cancel the noise successfully and efficiently with a parsimonious structure.

关键词： Adaptive filters Noise cancellation Neural networks Nonlinear filters Neurons Signal processing algorithms Fuzzy neural networks backpropagation algorithms Filtering algorithms Clustering algorithms

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A pruning algorithm for training neural network ensembles

A pruning algorithm for training neural network ensembles

引用

SICE Annual Conference

作者： Md. Shahjahan M.A.H. Akhand K. Murase Fukui University Fukui-shi Japan Bangladesh Institute of Technology Khulna Bangladesh Bangladesh Institute of Technology(BIT) Khulna Bangladesh Fukui University of Technology Fukui Japan

ISBN: (纸本)0780383524

This paper presents a pruning algorithm i.e., dynamic ensemble pruning algorithm (DEPA) by utilizing the knowledge of overfilling and importance of hidden node. The generalization performance of a machine learner depends on how much it avoids the overfilling. The main idea of this algorithm is to reduce the complexity of ensemble networks according to overfilling on progress toward training. DEPA emphasizes on avoiding "overfilling" by dynamically deleting individual neural networks and their hidden nodes starting from a large number of individual neural networks. DEPA has been tested on several standard benchmark problems in machine learning and neural networks, including breast cancer, diabetes and heart disease problems. The experimental results show that DEPA can produce neural network ensembles with good generalization ability.

关键词： Neural networks Machine learning algorithms Multi-layer neural network backpropagation algorithms Heuristic algorithms Training data Benchmark testing Machine learning Breast Diabetes

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An application of SMC theory for experimental learning control of robotic manipulators

An application of SMC theory for experimental learning contr...

引用

IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： U. Yildiran O. Kaynak Electrical and Eletronics Engineering Department Bogazici University Istanbul Turkey

Complexity of learning dynamics constitutes a prime difficulty in online neurocontrol schemes involving gradient computations in parameter update rules. This is because such complexities can make closed loop system sensitive to uncertainties. In this paper, we discuss a learning control approach, which is based on the sliding mode control (SMC) techniques instead of gradient computations. Due to properties of SMC, learning process becomes robust to uncertainties. In order to test the control scheme, we have chosen a robotic manipulator as the test bed. Experimental results show that the control approach achieves a good tracking performance.

关键词： Sliding mode control Robot control Uncertainty Control systems Manipulator dynamics Testing Artificial neural networks Intelligent networks backpropagation algorithms Closed loop systems

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Self-organizing neural networks by dynamic and spatial changing weights

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

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International Symposium on Uncertainty Modeling and Analysis (ISUMA)

作者： N. Homma M.M. Gupta M. Yoshizawa K. Abe College of Medical Sciences University of Tohoku Sendai Japan College of Engineering University of Saskatchewan Saskatoon SAS Canada Information Synergy Center University of Tohoku Sendai Japan Graduate School of Engineering University of Tohoku Sendai Japan

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

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A learning multi-agent system for personalized information filtering

A learning multi-agent system for personalized information f...

引用

International Conference on Information, Communications and Signal Processing

作者： Junhua Chen Zhonghua Yang Information Communication Institute of Singapore (ICIS) School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)0780381858

A multi-agent hybrid learning approach to the problem of personalized information filtering is proposed in this paper. There are four agents in the multi-agent model. The problem is modeled as Monte Carlo reinforcement learning. Our proposed algorithm is modified Monte Carlo method combined with features of unsupervised suffix tree clustering and supervised backpropagation network. We argue that this proposed approach could precisely capture the user's interest without repeatedly asking for his/her explicit rates and converge to the user's interest quickly. A conclusion is drawn that our approach is efficient, precise and converges more quickly compared with existing approaches. A prototype system is being developed.

关键词： Multiagent systems Information retrieval Information filtering Supervised learning Search engines Information filters Contacts Monte Carlo methods Clustering algorithms backpropagation algorithms

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A fast electric load forecasting using adaptive neural networks

A fast electric load forecasting using adaptive neural netwo...

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Power Tech Conference

作者： M.L.M. Lopes A.D.P. Lotufo C.R. Minussi UNESP Ilha Solteira Sao Paulo Brazil

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company.

关键词： Load forecasting Adaptive systems Neural networks Multi-layer neural network backpropagation algorithms Feedforward neural networks Iterative methods Fuzzy logic Convergence Finishing

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Tuning complex fuzzy systems by supervised learning algorithms

Tuning complex fuzzy systems by supervised learning algorith...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： F.J. Moreno-Velo I. Baturone R. Senhadji S. Sanchez-Solano Centro Nacional de Microelectronica-CSIC Instituto de Microelectronica de Sevilla (CNM-CSIC) Seville Spain

Tuning a fuzzy system to meet a given set of input/output patterns is usually a difficult task that involves many parameters. This paper presents an study of different approaches that can be applied to perform this tuning process automatically, and describes a CAD tool, named xfsl, which allows applying a wide set of these approaches: (a) a large number of supervised learning algorithms; (b) different processes to simplify the learned system; (c) tuning only specific parameters of the system; (d) the ability to tune hierarchical fuzzy systems, systems with continuous output (like fuzzy controller) as well as with categorical output (like fuzzy classifiers), and even systems that employ user-defined fuzzy functions; and, finally, (e) the ability to employ this tuning within the design flow of a fuzzy system, because xfsl is integrated into the fuzzy system development environment Xfuzzy 3.0.

关键词： Fuzzy systems Supervised learning backpropagation algorithms Inference algorithms Convergence Fuzzy neural networks Programmable control Adaptive control Neural networks Size control

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Intelligent modeling for hysteresis nonlinearity

Intelligent modeling for hysteresis nonlinearity

引用

IEEE International Symposium on Intelligent Control (ISIC)

作者： Chuntao Li Yonghong Tan A0103021 Department of Automation Shanghai Jiaotong University Shanghai China Department of computer Guilin Institute of Electronic Technology Guilin China

It is known that hysteresis is a non-differentiable nonlinearity with multi-value mapping. The neural networks, however, can only be applied to modeling the function with one-to-one mapping. Under a mild assumption, this paper obtains a theorem to derive an invertable mapping between the coordinate related to a certain Preisach model and the integral interface that is the key element in Preisach model. Then it proves that there exists a mapping, which can describe hysteresis nonlinearity and be implemented easily by neural networks.

关键词： Hysteresis Neural networks Mathematical model Automation Mechanical systems Numerical simulation backpropagation algorithms Computer networks Electronic mail

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