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Neuro-fuzzy modeling of nonlinear systems for control purposes

Neuro-fuzzy modeling of nonlinear systems for control purpos...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： T. Culliere A. Titli J.M. Corrieu LAAS CNRS Toulouse France IBM France

In this paper we propose a neuro-fuzzy model for the identification of the nonlinear dynamic systems. The new model is composed of two stages. The first stage consists of a cut-out of the input space in areas. This global treatment is done by the fuzzy module. The second stage consists of a local identification of the system by several simplified neural networks. This article describes the first stage with an independent simple fuzzy model and the second stage with a neural one. Then it presents the complete model and shows the modifications of the backpropagation algorithm for the multiple neural network's learning. Simulations on examples and in particular on invert pendulum showing the neuro-fuzzy's ability.< >

关键词： Nonlinear systems Control system synthesis Nonlinear control systems Neural networks backpropagation algorithms Fuzzy neural networks Power system modeling Linear systems Mathematical model Linearity

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On the use of neural networks for automatic vowel recognition

On the use of neural networks for automatic vowel recognitio...

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International IEEE/IAS Conference on Industrial Automation and Control: Emerging Technologies

作者： M. Giurgiu Communications Department Technical University of Cluj Napoca Cluj-Napoca Romania

An approach to face the problem of speaker-independent vowel recognition using artificial neural networks (ANN) is presented. The approach lies in algorithms and techniques for training the ANN with backpropagation rule and then for testing it with the spectral representations of vowels obtained by linear predictive coding (LPC). Speech patterns consist in 12 LPC spectral components obtained from the stationary zones of the vowels and they compose a speech database collected in laboratory conditions with computer noise from a number of speakers. A training set and a testing set of vowel patterns were designed. Network topology is a multilayer perceptron (MLP) with a variable number of processing units in the hidden layer. Three types of MLP were tested and the results on vowel recognition rate are presented under this framework.

关键词： Neural networks Artificial neural networks Testing Linear predictive coding Speech recognition Speech enhancement Face recognition backpropagation algorithms Databases Laboratories

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Semiautomated extraction of decision relevant features from a raw data based artificial neural network demonstrated by the problem of saccade detection in EOG recordings of smooth pursuit eye movements

Semiautomated extraction of decision relevant features from ...

引用

IEEE Workshop on Neural Networks for Signal Processing

作者： P.K. Tigges N. Kathmann R.R. Engel Department of Psychiatry University of Munich (LMU) Munich Germany

Visual identification of saccades in electrooculographic (EOG) recordings of smooth pursuit eye movements (SPEM) is a very time consuming process for the individual experts. Algorithmic approaches to overcome this problem automatically produce high rates of false positive errors. Artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low. An automated decision process based on modified raw data inputs showed successful proceeding of a backpropagation ANN with an overall performance of 87% correct classifications with previously unknown data. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a sparse and efficient data coding, based on a combination of expert knowledge and the internal representation structures of the ANN. Data coding obtained by this semiautomated procedure yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% and reached an overall correct classification of 92% with unknown data. The proposed method of extracting internal ANN knowledge is not restricted to EOG recordings, and could be used in various fields of signal analysis.

关键词： Data mining Artificial neural networks Electrooculography Signal to noise ratio Signal generators Electroencephalography Intelligent networks Psychiatry Electronic mail backpropagation algorithms

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A neural network system that controls and plans paths for a robot

A neural network system that controls and plans paths for a ...

引用

IEEE/IAS International Conference on Industrial Automation and Control, IA & C

作者： J. Garcia-Chamizo F. Ibarra-Pico Department of Tecnologia Informática y Computación University of Alicante Alicante Spain

Proposes to solve the problems of direct/inverse kinematics and control of trajectories by multilevel perceptrons. The authors' solution admits a parallel implementation in real time. It does not need either to solve kinematic equations or robot trajectories, because it learns gradually by examples adaptively. The control system consists of different networks each of which specialises in solving a particular problem. This structure enables a modular approach to the problem accelerating convergence. The system obtains an acceptable trajectory and gives a parallel solution that could be used in real-time applications.< >

关键词： Neural networks Control systems Robots Kinematics backpropagation algorithms Equations Acceleration Orbital robotics Adaptive systems Real time systems

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Stochastic convergence analysis of a two-layer perceptron for a system identification model

Stochastic convergence analysis of a two-layer perceptron fo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： J.L. Vaughn N.J. Bershad Department of Electrical and Computer Engineering University of California Irvine CA USA

This paper summarizes the results of the simulations and analysis of the learning behavior of a simple two-layer perceptron for a nonlinear system identification problem. Although it is difficult to generalize results for nonlinear systems, the analysis may improve our understanding of neural network training. Numerous sub-optimum stationary points occur for this problem and cause difficulties in the correct identification of the unknown system. The sub-optimum convergence points occur in the saturation regions of the various nonlinearities or for pathological cases. The size of the region of suboptimal convergence points may be reduced by increasing the dimensionality of the input data vector. Also, the range for the rate parameter is computed and an improvement to backpropagation is suggested.

关键词： Stochastic systems Convergence Multilayer perceptrons System identification Nonlinear systems backpropagation algorithms Training data Analytical models Neural networks Linear systems

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Self-organizing concept maps

Self-organizing concept maps

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： M. Hagiwara Department of Electrical Engineering Science and Technology Keio University Yokohama Japan

Self-organizing concept maps (SOCOMs) based on a neural network model are proposed in this paper. They can arrange concepts or words in a map space using Kohonen's self-organizing map algorithm. One of the most important advantages of the proposed maps is that they employ the idea of k-nearest neighbor (k-NN): they do not require all of the data among concepts or words. The author proposes two kinds of SOCOMs: one is a metric SOCOM, another is a non-metric one. The metric SOCOM uses the information about the metric data such as similarity. The non-metric one uses the information about the rank order of similarity among items. The combination of the idea of k-NN and a non-metric SOCOM is effective to relax the severe requirements on data: it does not require all of the detailed metric information among concepts or words. Computer simulation results have shown the effectiveness of the proposed SOCOM.

关键词： Neurons Computer simulation Artificial neural networks Feature extraction Clustering algorithms Space technology Supervised learning backpropagation algorithms Pattern recognition Image processing

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Data-dependent filters with fuzzy-neural network

Data-dependent filters with fuzzy-neural network

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International Conference on Neural Networks

作者： A. Taguchi H. Takashima Department of Electrical and Electronic Engineering Musashi Institute of Technology Setagaya Tokyo Japan

This paper presents a design method of data-dependent filters by using fuzzy inference for the purpose of restoring signals degraded by additive noise. Since the antecedents of fuzzy inference can be composed of many local characteristics, it is possible for the proposed filter to adjust its weights to adapt to local data in input signal. The proposed filter achieve maximum noise reduction in uniform areas and preserve details of input signals as well. Furthermore, the proposed filter can be constructed by fuzzy neural networks, and so the tuning of this results in backpropagation algorithm.

关键词： Filters Design methodology Signal restoration Degradation Additive noise Noise reduction Fuzzy neural networks backpropagation algorithms

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Basic-evolutive algorithms for neural networks architecture configuration and training [spacecraft control]

Basic-evolutive algorithms for neural networks architecture ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： J. Seijas J.L. Sanz-Gonzalez Parque Tecnológico de Mudrid SENER Ingenieria y Sistemas S.A. Tres cantos Spain Departamento de Ingenieria de Circuitos y Sistemas E. TSoI. Telecomunicacibn Universidad Politecnica de Madrid Madrid Spain

This paper presents a procedure for optimising a neural network architecture used in a system for spacecraft attitude and position determination. The procedure establishes the neural network structure and the training algorithm. A new version of Basic-Evolutive algorithm is presented, Basic-Evolutive 1 and Basic-Evolutive 2 algorithms are capable of setting the appropriate dimension of the neural network and the adequate weights interconnecting the neurons. The results produced by both versions are tested with a very wide set of different spacecraft manoeuvre simulations. The algorithm performance is contrasted with backpropagation training algorithm performances. The capability of the resulting neural network architecture for generalising is also verified.

关键词： Neural networks Space vehicles Neurons Cameras Charge coupled devices backpropagation algorithms Position measurement Charge-coupled image sensors Circuits Telecommunications

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Neural network control of a parallel robot

Neural network control of a parallel robot

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Hongrui Wang Chunna Xue W.A. Gruver School of Engineering Science Simon Fraser University Burnaby BC Canada Department of Automation Yanshan University슠 Qinhuangdao Hebei China

This paper presents a method for obtaining the inverse kinematics of a six degree of freedom error compensation device for a robotic manipulator. The error compensation device is based on a parallel link structure having two circular platforms connected by six links, each with double spherical joints. A three layer backpropagation neural network algorithm was applied to compute the kinematics and control the mechanism. Simulation results based on the proposed technique are described.

关键词： Neural networks Parallel robots Kinematics Error compensation Fasteners backpropagation algorithms Computational modeling End effectors Manipulators Grippers

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Colour bake inspection system using hybrid artificial neural networks

Colour bake inspection system using hybrid artificial neural...

引用

International Conference on Neural Networks

作者： J.C.H. Yeh L.G.C. Hamey T. Westcott S.K.Y. Sung Department of Computing Macquarie University NSW Australia Arnotts's Research Centre Arnott's Biscuits Limited Homebush NSW Australia

The bake level of biscuits is of significant value to biscuit manufacturers as it determines the taste, texture and appearance of the products. Previous research explored and revealed the feasibility of biscuit bake inspection using feedforward neural networks (FFNN) with a backpropagation learning algorithm and monochrome images. A second study revealed the existence of a curve in colour space, called a baking curve, along which the bake colour changes during the baking process. Combining these results, the authors proposed an automated bake inspection system with artificial neural networks that utilises colour instead of monochrome images. In this paper, the authors present the implementation of the inspection system with a hybrid neural network of self-organising maps and FFNNs. The system was tested and its grading performance on biscuit bake levels was evaluated and compared to that of a trained human inspector. The authors found that the proposed colour system with a hybrid neural network performed significantly better than the human inspector.

关键词： Inspection Artificial neural networks Neural networks Color Humans Manufacturing Feedforward neural networks backpropagation algorithms System testing

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