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检索条件"主题词=Backpropagation Algorithms"

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Using time-discrete recurrent neural networks in nonlinear control

Using time-discrete recurrent neural networks in nonlinear c...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： T. Kolb W. Ilg J. Wille Chair of Numerical and Applied Brandenburg University of Technology Cottbus Germany Section for Interactive Diagnosis Systems Research Center for Computer Science Karlsruhe Germany

We introduce a type of fully connected recurrent neural networks (RNN) with special mathematical features which allows one to determine its qualitative dynamical behaviour. Based on this family of RNN we describe a learning framework for the generation of trajectories with which we are able to solve adaptive control problems, which is illustrated by the realization of adaptive leg control of a six-legged walking machine.

关键词： Recurrent neural networks Intelligent networks Adaptive control backpropagation algorithms Machine learning Programmable control Leg Legged locomotion Biological system modeling Transfer functions

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A gradient descent learning algorithm for fuzzy neural networks

A gradient descent learning algorithm for fuzzy neural netwo...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： T. Feuring J.J. Buckley Y. Hayashi University of Münster Munster Germany University of Alabama Birmingham Birmingham AL USA Meiji University Kawasaki Japan

In order to train fuzzy neural nets fuzzy number weights have to be adjusted. Since fuzzy arithmetic automatically leads to monotonic increasing outputs a direct fuzzification of the backpropagation method does not work. Therefore, other strategies like evolutionary algorithms are being considered in the literature. In this paper we suggest a backpropagation based method of adjusting the weights. Furthermore, we show that by using the proposed method convergence can be guaranteed.

关键词： Fuzzy neural networks Neural networks Arithmetic Error analysis Fuzzy sets backpropagation algorithms Convergence Feedforward systems Computer networks

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Showing the equivalence of two training algorithms. I

Showing the equivalence of two training algorithms. I

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International Joint Conference on Neural Networks (IJCNN)

作者： M. Koch I. Fischer M.R. Berthold Technical University Berlin Germany University of Erlangen Nuremberg Germany University of California Berkeley CA USA

Graph transformations offer a powerful way to formally specify neural networks and their corresponding training algorithms. This formalism can be used to prove properties of these algorithms. In this paper graph transformations are used to show the equivalence of two training algorithms for recurrent neural networks; backpropagation through time, and a variant of real-time backpropagation. In addition to this proof a whole class of related training algorithms emerges from the used formalism.

关键词： backpropagation algorithms Neural networks Flow graphs Recurrent neural networks Network topology Computer networks Convergence Labeling

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Recognition of partial discharge in stator winding models based on 3-dimensional pattern using artificial neural network

Recognition of partial discharge in stator winding models ba...

引用

International Conference on Power System Technology (POWERCON)

作者： Zhide Yin Kexiong Tan Zhongdong Wang Lei Jiang Department of Electrical Engineering Tsinghua University Beijing China

ISBN: (纸本)0780347544

Partial discharge (PD) is an important phenomenon and has a close relation to the insulation condition of electrical apparatus. Usually PD accelerates insulation deterioration and before final breakdown, their activities will be much more serious than that in ordinary operation. Therefore PD is an adequate characteristic quantity for the inspection of insulation condition in order to avoid sudden failures, especially for online monitoring. An artificial neural network (ANN) group with the backpropagation algorithm was developed to identify the types and extent of PDs. Six different physical models, which could reflect PDs in the stator windings of large electrical machines, were made. Simulated PD types included surface discharges at end windings, slot discharges, delamination in three different positions of ground wall insulation as well as a standard PD level of new machines. Different levels of voltage were applied to models to obtain various extents of PD activities. The fingerprints of experimental PD data were extracted with the /spl phi/-q-n 3-dimensional pattern. The recognition ability of the ANN group was investigated. Different types and extents of discharge within the winding insulation of large electrical machines were identified with a satisfactory recognition rate.

关键词： Partial discharges Stator windings Artificial neural networks Dielectrics and electrical insulation Machine windings Acceleration Electric breakdown Inspection Condition monitoring backpropagation algorithms

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Automatic detection of epileptiform activity using wavelet and expert rule base

Automatic detection of epileptiform activity using wavelet a...

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

作者： S.B. Kim Y.H. Lee J.H. Kim S.I. Kim Department of Biomedical Engineering Hanyang University Seoul South Korea Department of Electronic Engineering Hanyang University Seoul South Korea Department of Electronic Engineering and Neurology Hanyang University Seoul South Korea

We present a new method to detect epileptiform activity based on wavelet transform (WT), an artificial neural network and an expert rule system. The method consists of three steps. First, we extract features of spike events on the wavelet subspace. It appears technically feasible to reduce computational complexity. Then, the features are trained and tested to decide epileptic events with three layer feedforward networks employing the backpropagation (BP) learning algorithm. Finally, to confirm and validate epileptiform activity, we apply an expert system based on rule base. The result shows that the wavelet transform reduced data input size and the preprocessed artificial neural network (ANN) are more accurate than those of ANN with the same input size of raw data. In clinical tests, our expert system was capable of rejecting artifacts commonly found in EEG recordings.

关键词： Epilepsy Artificial neural networks Wavelet transforms Expert systems Feature extraction Computational complexity backpropagation algorithms Data preprocessing System testing Electroencephalography

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Optimal Learning Rate For Training Time Lagged Recurrent Neural Networks With The Extended Kalman Filter Algorithm

Optimal Learning Rate For Training Time Lagged Recurrent Neu...

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International Joint Conference on Neural Networks (IJCNN)

作者： Pu Sun K. Marko Ford Research Laboratory Ford Motor Company Limited Dearborn MI USA

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An artificial neuron approach to speech processing based on a hidden Markov model

An artificial neuron approach to speech processing based on ...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： M.P. Beddoes Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

The paper discusses possible designs for a hidden Markov model using artificial neural network techniques. Applications are speech generation and speech recognition. The designs could be the basis for a computer program to realise an HMM; or they could be inspiration for a special purpose piece of hardware. The designs have two parts. The first determines the time sequence of hidden states. Although the design uses mainly standard logic components, the behaviour will be similar to a backpropagation layer of ANNs. The second part consists of one or more layers of feedforward AN processing elements in a standard backpropagation network with final outputs coded speech data. For training an HMM recognizer, the normal approach is to use the Viterbi Algorithm followed by fine tuning using the Baum Welch re-estimation algorithm. The ANN-HMM can be trained this way, but it can also be trained by the backpropagation algorithm, BP. The latter ties very directly to the unknown weights in the ANN circuits and may facilitate topological design.

关键词： Neurons Speech processing Hidden Markov models Artificial neural networks backpropagation algorithms Application software Speech recognition Hardware Logic design Code standards

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Artificial neural network based short term load forecasting

Artificial neural network based short term load forecasting

引用

IEEE Region 10 International Conference TENCON

作者： A.P. Rewagad V.L. Soanawane MSEB CLD India MSEB Mumbai India

The artificial neural network approach has attracted a number of applications, especially in the field of power systems since it is a model-free estimator. Progress in application of artificial neural network technology to power systems in the areas of load forecasting, security assessment and fault diagnosis among others, has led to overcoming some of the limitations in the short term load forecasting problem. Improvements in forecasting are of paramount importance as an SLF program must guide the decision making of power system operators and also respond accurately and consistently to system changes. In this paper, a fast emerging field of ANN is described for a short term load forecasting (ANNSTLF) model, which implements the multiLayer feedforward backpropagation algorithm. The backpropagation algorithm with MLP model of artificial neural network is developed for the problem of short term load forecasting with a lead time of at least 24 hours. The best performance was obtained for the load forecasting for the Tuesday for which the maximum and average percentage error of 2.00% and 0.20% respectively was found. This came very close to the precision obtained by the human forecaster. The ability of a neural network to generalize the information presented is used to train the network.

关键词： Artificial neural networks Load forecasting Power system modeling Power system security Power system faults Predictive models backpropagation algorithms Fault diagnosis Decision making Load modeling

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Hybrid speech recognition system with discriminative training applied for Romanian language

Hybrid speech recognition system with discriminative trainin...

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Mediterranean Electrotechnical Conference (MELECON)

作者： I. Gavat M. Zirra O. Cula Polytechnic University of Bucharest Bucharest Romania ADtech Advanced Technology Bucharest Romania Polytechnic University of Bucharest Romania

This paper describes a hybrid connectionist-statistical system consisting of a neural network integrated in a hidden Markov model (HMM). The neural network used is the multilayer perceptron (MLP) and that network is the mechanism that computes the a-posteriori probability of a sequence of HMMs states. The classifier is based on total scores computed by Viterbi alignment for each hybrid model corresponding to the words in the vocabulary. Because of the lack of discrimination between the models and the unintended discrimination between the states in each model, we propose a solution that improves the system, namely an additional training task based on a cost function that approximates the misclassification rate of the hybrid system. The optimization criterion is based on a descent algorithm and the result is a minimum classification error. Our experiments on a 35 word vocabulary, show an improvement of the recognition rate from 92.4% for the case of a statistical system based only on HMMs, to 94.7% for the case of a hybrid HMM-MLP system, and to 97.9% for the case of an improved hybrid system with an extra discriminative training.

关键词： Speech recognition Natural languages Hidden Markov models Power system modeling Artificial neural networks Neural networks Viterbi algorithm Vocabulary Speech processing backpropagation algorithms

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Practical considerations for sensorless induction motor drive system

Practical considerations for sensorless induction motor driv...

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Annual Conference of Industrial Electronics Society

作者： L. Ben-Brahim S. Tadakuma Faculty of Technology University of Qatar Doha Qatar Chiba Institute of technology Narashino Chiba Japan

Speed information is necessary for high performance vector controlled induction motor drives. This information is generally provided by a sensor which spoils the ruggedness and the simplicity of the induction motor. This paper describes the practical considerations for an implementation of the newly developed speed sensorless drive based on neural networks technique. The back-propagation neural networks technique is used to provide a real-time adaptive identification of the motor speed. The estimation objective is defined in terms of a reference or desired trajectory that the neural networks model output should match or track as closely as possible. The backpropagation algorithm is used to adjust the motor speed so that the neural model output follows the desired trajectory. This backpropagation mechanism forces the estimated speed to follow precisely the actual motor speed. The zero speed crossing phenomena is also described and experimental results are presented and analysed.

关键词： Induction motors Biological neural networks Neural networks Sensorless control Control systems Induction motor drives Trajectory Impedance matching backpropagation algorithms State estimation

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