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Neurocontroller for induction motors

Neurocontroller for induction motors

引用

International Conference on Microelectronics, ICM

作者： R.S. Toqeer N.S. Bayindir Department of Electrical and Electronics Engineering TRNC Eastern Mediterranean University Famagusta Mersin Turkey

ISBN: (纸本)9643600572

In this paper, the artificial neural network (ANN) is used to identify and control an induction machine by performing as a field-oriented control (FOC). The control system designed in this work is called the neurocontroller, and it is trained to reflect the nonlinear behavior of an indirect vector controller and of the PI controller used in the control system. Once the neurocontroller captures the nonlinear dynamics of the induction motor control, it can replace the conventional field-oriented controller (FOC) and the PI controller with a similar dynamic performance. The data used for the training of ANNs are obtained from computer simulation of the field oriented control (FOC). The methodology used to train the ANNs is the backpropagation algorithm (BP). Simulation results reveal some very interesting features of the neurocontroller and show that the network has good potential for use as an alternative to the conventional FOC decoupling control of induction motors, with the further advantage of being insensitive to parametric variations.

关键词： Neurocontrollers Induction motors Nonlinear control systems Control systems Artificial neural networks Nonlinear dynamical systems Induction machines Computer simulation backpropagation algorithms Computational modeling

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

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

引用

Brazilian Symposium on Neural Networks

作者： G.C.M. De Abreu R.L. Teixeira J.F. Ribeiro Laboratório de Dinâmica de Sistemas Mecânicos Department de Engenharia Mecânica University Fed. de Uberlândia Uberlandia Minas Gerais Brazil

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 plant using the backpropagation algorithm with simulated annealing. The inverse model of the plant is obtained by the training mechanism that uses 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 tests. 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 one degree of freedom actuated by magnetic actuators.

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

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Inverse scattering via linear and nonlinear tomography — A review

Inverse scattering via linear and nonlinear tomography — A ...

引用

European Signal Processing Conference (EUSIPCO)

作者： George A. Tsihrintzis University of Pireaus Piraeus GR

Nonlinear tomographic reconstruction algorithms are developed for inversion of data measured in scattering experiments in which the complex phase of the wavefields is modeled by an arbitrarily large (possibly infinite) number of terms in the Rytov series. The algorithms attain the form of a Volterra series of nonlinear operators, with the usual filtered backpropagation algorithm of Diffraction Tomography as the leading linear term.

关键词： Tomography Scattering Inverse problems Approximation methods Image reconstruction backpropagation algorithms Equations

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An analog on-chip learning circuit architecture of the weight perturbation algorithm

An analog on-chip learning circuit architecture of the weigh...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： F. Diotalevi M. Valle G.M. Bo E. Biglieri D.D. Caviglia Department of Biophysical and Electronic Engineering University of Genova Genoa Italy Consiglio Nazionale delle Ricerche Area di Ricerca di Napoli 1 Napoli Campania IT

In this paper we present the analog on-chip learning architecture of a gradient descent learning algorithm: the Weight Perturbation learning algorithm. From the circuit implementation point of view our approach is based on current mode and translinear operated circuits. The proposed architecture is very efficient in terms of speed, size, precision and power consumption; moreover it exhibits also high scalability and modularity.

关键词： Circuits Neurons Energy consumption Scalability CMOS technology Transfer functions backpropagation algorithms Feedforward systems Very large scale integration Hardware

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Adaptive fuzzy-neuro controller for speed of wound rotor induction motor with slip energy recovery

Adaptive fuzzy-neuro controller for speed of wound rotor ind...

引用

IEEE Region 10 International Conference TENCON

作者： S. Tunyasrirut J. Ngamwiwit T. Furuya Y. Yamamoto Kumamoto National College of Technology Japan Faculty of Engineering and Research Center for Communications and Information Technology King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand Kitakyushu National College of Technology Japan

In this paper, the authors proposed an adaptive fuzzy-neuro controller to control the speed of a wound rotor induction motor with slip energy recovery. An adaptive fuzzy-neuro controller has been designed by integrating two neural network models with a basic fuzzy logic controller. Using the backpropagation algorithm, the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance online. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The scheme is applied to the control of the speed of the wound rotor induction motor process. Simulation and experimental results show that the adaptive fuzzy-neuro controller gives constant speed and good transient response without overshoot.

关键词： Programmable control Adaptive control Wounds Neural networks Rotors Induction motors Fuzzy neural networks Fuzzy logic backpropagation algorithms Fuzzy control

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Applying a clustering genetic algorithm for extracting rules from a supervised neural network

Applying a clustering genetic algorithm for extracting rules...

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International Joint Conference on Neural Networks (IJCNN)

作者： E.R. Hruschka N.F.F. Ebecken COPPE Federal University of Rio de Janeiro Curitiba Parana Brazil COPPE Federal University of Rio de Janeiro Rio de Janeiro Rio de Janeiro Brazil

The main challenge to the use of supervised neural networks in data mining applications is to get explicit knowledge from these models. For this purpose, a clustering genetic algorithm for rule extraction from artificial neural networks is developed. The methodology is based on the clustering of the hidden units activation values. A simple encoding scheme that yields to constant-length chromosomes is used thus allowing the application of the standard genetic operators. A consistent algorithm to avoid some of the drawbacks of this kind of representation is also developed. In addition, a very simple heuristic is applied to generate the initial population. The individual fitness is determined based on the number of objects belonging to each cluster, as well as on the Euclidean distances among the objects. The developed algorithm is experimentally evaluated in the Australian credit approval database.

关键词： Genetic algorithms Neural networks Clustering algorithms Data mining Encoding backpropagation algorithms Biological cells Australia Databases Knowledge acquisition

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An efficient implementation of a learning method for Mamdani fuzzy models

An efficient implementation of a learning method for Mamdani...

引用

Brazilian Symposium on Neural Networks

作者： L. Schnitman T. Yoneyama Instituto Tecnológico de Aeronáutica Sao Paulo Brazil

This paper presents an efficient implementation of a supervised learning method based on membership function training in the context of Mamdani fuzzy models. The main idea is to adjust the antecedent and consequent membership functions that are of asymmetric trapezoidal form by backpropagating the output error through the fuzzy net. The proposed implementation is analogous to the training scheme commonly used with Takagi-Sugeno fuzzy models but it requires additional procedures that are related to some specific characteristics of the Mamdani fuzzy structures. Some numerical results are provided as illustrations.

关键词： Learning systems Power system modeling Supervised learning Takagi-Sugeno model Optimization methods Mathematical model backpropagation algorithms Context modeling Control system synthesis Fuzzy neural networks

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Classification and function approximation using feed-forward shunting inhibitory artificial neural networks

Classification and function approximation using feed-forward...

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International Joint Conference on Neural Networks (IJCNN)

作者： A. Bouzerdoum School ofEirgineering and Mathematics Edith Cowan University Joondalup WA Australia

In this article we propose a new class of artificial neural networks for classification and function approximation. These networks are referred to as shunting inhibitory artificial neural networks (SIANN). A SIANN consists of one or more hidden layers comprised of shunting neurons, the outputs of which are combined linearly to form the desired output. The basic synaptic interaction of the hidden units is shunting inhibition. Due to the inherent nonlinearity mediated by shunting inhibition, SIANN networks are capable of constructing a large repertoire of decision surfaces, ranging from simple hyperplanes to very complex nonlinear hypersurfaces. Therefore, developing efficient training algorithms for these networks should simplify the design of very powerful classifiers and function approximators. In this paper some examples of complex decision regions formed by SIANN are illustrated. Furthermore, a method for training feedforward SIANN is developed based on the error backpropagation algorithm. Finally, simulation results which illustrate the performance of SIANN in function approximation and classification tasks are presented and compared with results obtained from multilayer perceptron networks.

关键词： Function approximation Feedforward systems Neurons Artificial neural networks backpropagation algorithms Neural networks Pattern recognition Motion detection Image edge detection Mathematics

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Learning in neural networks by normalized stochastic gradient algorithm: local convergence

Learning in neural networks by normalized stochastic gradien...

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Seminar on Neural Network Applications in Electrical Engineering

作者： V. Tadic S. Stankovic Automatic Control Laboratory Mihajlo Pupin Institut Belgrade Serbia Department of Electrical and Electronic Engineering University of Melbourne Melbourne Australia Faculty of Electrical Engineering University of Belgrade Belgrade Serbia

ISBN: (纸本)0780355121

In this paper, a normalized stochastic gradient algorithm is proposed for learning in feedforward neural networks. By using a new methodology based on the martingale convergence results, asymptotic properties of the algorithm are analyzed. It is proved that, in general, the sequence of the algorithm states converges with probability one to the set of zeroes of the gradient of the criterion function locally on the event where it is bounded. Then, these results are applied to learning in multilayer perceptrons.

关键词： Intelligent networks Neural networks Stochastic processes Convergence Feedforward neural networks Algorithm design and analysis backpropagation algorithms Multilayer perceptrons Multi-layer neural network Automatic control

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Training neurocontrollers by local and evolutionary search

Training neurocontrollers by local and evolutionary search

引用

Congress on Evolutionary Computation

作者： K.W.C. Ku M.W. Mak Department of Computer Science City University of Hong Kong Hong Kong China Department of Electronic & Information Engineering Hong Kong Polytechnic University Hong Kong China

Training of neural networks by local search such as gradient based algorithms could be difficult. This calls for the development of alternative training algorithms such as evolutionary search. However, training by evolutionary search often requires long computation time. The authors investigate the possibilities of reducing the time taken by combining the efforts of local search and evolutionary search. There are a number of approaches to combine these search strategies, but not all of them are successful. The paper provides a review of these approaches. Experimental results indicate that while the Baldwinian and the two-phase approaches are inefficient in improving the evolution process for difficult problems, the Lamarckian approach is able to speed up the training process. Moreover in the case where no local search method is appropriate for learning the desired task directly, the paper demonstrates that allowing the local search to learn another related task can assist the evolutionary search.

关键词： Neurocontrollers Neural networks Recurrent neural networks Search methods Educational institutions backpropagation algorithms Feedforward neural networks Biological cells Computer science Genetics

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