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检索条件"主题词=Backpropagation Algorithms"

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Neural network state space identification model

Neural network state space identification model

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Kejun Wang Keeheng Wang Guobin Li Department of Automatic Control Harbin Engineering of Technology Harbin China

ISBN: (纸本)0780332806

Proposes a novel special neural network state space identification model for system identification (NNSSIM). This novel neural network model is composed of the state equation and the output equation of the system. The neurons and weights in the novel neural network have a clear physical significance. A dynamic backpropagation training algorithm is developed to train NNSSIM. The convergence of the algorithm is analyzed using the Lyapunov stability theorem and the conditions of convergence are given.

关键词： Neural networks State-space methods Equations backpropagation algorithms Convergence System identification Neurons Heuristic algorithms Algorithm design and analysis Lyapunov method

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Fast nonlinear adaptive filtering using a partial window conjugate gradient algorithm

Fast nonlinear adaptive filtering using a partial window con...

引用

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

作者： A.N. Birkett R.A. Goubran Department of Systems and Computer Engineering Carleton University Ottawa Canada

In this paper a modified form of the partial conjugate gradient algorithm is presented for use in nonlinear filtering using neural networks. The algorithm is based on using a gradient average window to provide a trade-off between convergence rate and complexity which, depending on the choice of averaging window, is (in both complexity and speed of convergence) intermediate between the conventional backpropagation (BP) algorithm and the Newton methods. An additional simplification is introduced by replacing the calculated optimum step size /spl alpha//sub k/ by a normalized step size /spl alpha/~, in the same manner as the normalized LMS algorithm. This new algorithm is applied to a cascaded neural network/nonlinear least mean squares structure for the identification of a nonlinear system. This proposed algorithm demonstrates improved convergence rates with even small choices of window size.

关键词： Adaptive filters Filtering algorithms backpropagation algorithms Convergence Cost function Neural networks Nonlinear systems Newton method Error correction Computer networks

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A two dimensional intelligent calibration of an ion sensor

A two dimensional intelligent calibration of an ion sensor

引用

IEEE Instrumentation and Measurement Technology Conference

作者： M. Attari M. Heniche F. Boudjema Institute of Electronics Instrumentation Department Houari Boumedienne University of Sciences and Technologies USTHB Algiers Algeria Electrical Engineering Department Ecole Nationale Polytechnique (ENP) Algiers Algeria

This paper focuses on the use of a multilayered neural network as an effective tool in the instrumentation field. The emphasis is on the linearization of nonlinear sensors' characteristics in the presence of disturbances. An artificial neural network reproduces the inverse sensor's characteristic so that the global system (sensor and ANN) operates like a linear one. Two algorithms widely known in artificial neural networks theory were jointly used to adapt the weights during training, namely backpropagation (BP) and random optimization method (RO). In order to have a good illustration, an ion selective electrode has been used because of its high sensitivity to interfering ions present in a solution of interest. Accuracy curves including the disturbing variable are shown to discuss the performance of this method.

关键词： Intelligent sensors Calibration Sensor phenomena and characterization Artificial neural networks Sensor systems Artificial intelligence Neural networks Multi-layer neural network Instruments backpropagation algorithms

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Regulation of nonlinear plants using radial basis function neural networks

Regulation of nonlinear plants using radial basis function n...

引用

International Conference on Neural Networks

作者： I. Kostanic F.M. Ham Electrical Engineering Program Florida Institute of Technology Melbourne FL USA

A large class of nonlinear discrete systems with accessible states can be controlled through feedback linearization. This paper develops a practical algorithm for state feedback control design using radial basis function neural networks, which are trained using dynamic backpropagation. Linear least-squares is coupled with a Gram-Schmidt orthogonalization procedure to perform size reduction of the neural networks. An example of regulating a nonlinear plant is included to illustrate the effectiveness of the proposed algorithm.

关键词： Radial basis function networks Neurofeedback State feedback Control systems Neural networks Regulators Nonlinear control systems backpropagation algorithms Nonlinear systems Vectors

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A methodology for implementing hybrid expert systems

A methodology for implementing hybrid expert systems

引用

Mediterranean Electrotechnical Conference (MELECON)

作者： L.M. Brasil F.M. de Azevedo R. Garcia J.M. Barreto Department of Electrical Engineering B Research Group Federal University of Santa Catarina Brazil Universidade Federal de Santa Catarina Florianopolis SC BR

This paper deals with a new methodology for developing an expert system (ES). It has the ability of learning to extract knowledge from a poor knowledge base using the learning by example paradigms. The choice of a poor knowledge base was motivated by the fact that in this case it is easier to have consistence in putting together the several pieces of knowledge. So, the problems attached to knowledge elicitation are simplified. The implementation leads to a hybrid expert system (HES). This system consists of a neural network based expert system (NNES) and a rule-based expert system (RBES). The main idea is that if the knowledge engineer has conditions to obtain some basic rules, and a set of examples, from the domain expert then it is possible to define the basic structure of the NNES using those basic rules. Then the NNES can be refined using the set of examples. In this stage structural changes of the network are allowed by the learning algorithm. Rules can be deduced after this refinement. Then it can be used to form a RBES and an explanatory expert system (EES). The methodology developed to HES is intended to be used in implementing decision support systems in the medical area.

关键词： Expert systems Neural networks Neurons Network topology Artificial intelligence backpropagation algorithms Knowledge representation Humans Artificial neural networks Decision support systems

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Improving the classification accuracy of cardiac patients

Improving the classification accuracy of cardiac patients

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： J. Goddard I. Lopez H.L. Rufiner Department Electrical Engineering UAMI Vicentina Italy Centro de Cómputo Universitario Universidad Autonoma Chapingo Chapingo Mexico Universidad Nacional de Entre Rios Oro Verde Entre Rios Argentina

In medicine, the cost of erroneously classifying an ill subject as healthy could have disastrous consequences. Many classification techniques however, do not try to improve the classification on one of the classes involved. In this paper different strategies are proposed to solve this problem using artificial neural networks (NN), and they are applied to a heart disease dataset. An important reason for using NN is that the methods the authors propose can be directly incorporated into the learning process. To achieve this, two forms of NN training are used. One involves a slight modification of the usual backpropagation algorithm (BP), and in the other, the training is achieved through an evolution program (EP). The latter is applied to take advantage of their ability to handle more complicated fitness functions.

关键词： Neural networks Cardiac disease Testing Costs Artificial neural networks Samarium backpropagation algorithms Genetic algorithms Classification algorithms Pain

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Efficient estimation of dynamically optimal learning rate using higher order derivatives

Efficient estimation of dynamically optimal learning rate us...

引用

International Conference on Neural Networks

作者： Xiao-Hu Yu National Communications Research LaboratoryDepartment of Radio Engineering South-East University Nanjing China

ISBN: (纸本)0780332105

Efficient estimation of the dynamically optimal learning rate is a critical problem in backpropagation learning. In this paper, a higher-order method for efficiently estimating the dynamically optimal learning rate is established, which explores the first four derivative information gathered from an extended feedforward propagation procedure. The near-optimal learning rate for each iteration is obtained with a moderate increase in computational and storage burden which remains the same scale as the standard backpropagation algorithm. Extensive computer simulations provided in this paper indicate that the present higher-order method can provide rapid convergence and very significant gains in running time savings.

关键词： Convergence Cost function Neurons backpropagation algorithms Computer simulation Application software Artificial neural networks Multi-layer neural network Recursive estimation Acceleration

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Control architecture based on a radial basis function network. Application to a fluid level system

Control architecture based on a radial basis function networ...

引用

International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing

作者： J. Fernandez de Canete A. Garcia-Cerezo I. Garcia-Moral A. Garcla-Gonzales C. Macias Dipartimento Ingenieria de Sistemas y Autom. Escuela de Ingenieros Ind. Malaga Spain

Nonlinear radial basis functions (RBF) at single layer hidden units of a neural net have proven to be effective in generating complex nonlinear mapping and at the same time facilitate fast learning. In the present paper off-line Gaussian control and identification of general nonlinear plants are realized. An iterative method to determine the desired controller output is described, and based upon this, a neural controller is adjusted by using the orthogonal least squares method. Neural identification of the plant is necessary to derive the parameter adjustments of the neural controller. The performance of the Gaussian approach has been demonstrated by off-line reference model neural control. Applications both to a general nonlinear plant and to a highly nonlinear fluid level system are detailed. It is shown that training times with orthogonal least squares method are dramatically reduced during control compared to standard backpropagation-of-the-error-through-the-plant technique. Finally, neural control is compared to PI control, showing the neural approach better generalization properties.

关键词： Radial basis function networks Neural networks Multi-layer neural network Least squares methods Iterative methods Error correction Pi control Least squares approximation Electrical equipment industry backpropagation algorithms

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algorithms for feature selection: An evaluation

Algorithms for feature selection: An evaluation

引用

International Conference on Pattern Recognition

作者： D. Zongker A. Jain Department of Computer Science Michigan State University East Lansing MI USA

A large number of algorithms have been proposed for doing feature subset selection. The goal of this paper is to evaluate the quality of feature subsets generated by the various algorithms, and also compare their computational requirements. Our results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al. (1994), dominates the other algorithms tested. This paper also illustrates the dangers of using feature selection in small sample size situations. It gives the results of applying feature selection to land use classification of SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. Application of feature selection to classification of handprinted characters illustrates the value of feature selection in reducing the number of features needed for classifier design.

关键词： Taxonomy Stochastic processes backpropagation algorithms Computer science Testing Pattern recognition Genetic algorithms Nonhomogeneous media

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Blind adaptive equalization techniques using the complex multi-layer perceptron

Blind adaptive equalization techniques using the complex mul...

引用

IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)

作者： Cheolwoo You Daesik Hong Department of Electronic Engineering Yonsei University Seoul South Korea

ISBN: (纸本)0780333365

Among the useful blind equalization algorithms, there are stochastic-gradient iterative equalization algorithms. Because these algorithms basically use a linear FIR filter except to adopt a memoryless nonlinear function of the equalizer output to generate the desired response, only linear channel distortion can be corrected well. To overcome nonlinear channel distortion two new blind equalization schemes using the structure of the complex multi-layer perceptron that can deal with signals of any constellation size are proposed. One is a modification of the constant modulus algorithm (CMA), the most widely used blind algorithm. The other is a modification of the radius-directed equalization (RDE) algorithm with multiple constant modulus that is more suitable for the node activation function having a multi-saturated output region. The experimental results show that the proposed schemes have very low MSE in the steady state and at the same time can recover the arbitrary phase rotation due to the channel when the major channel distortion is both linear and nonlinear.

关键词： Blind equalizers Adaptive equalizers Iterative algorithms Nonlinear distortion Least squares approximation backpropagation algorithms Finite impulse response filter Multilayer perceptrons Cost function Constellation diagram

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