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International Joint Conference on Neural Networks (IJCNN)

作者： D. Erdogmus O. Fontenla-Romero J.C. Principe A. Alonso-Betanzos E. Castillo R. Jenssen Electrical Engineering Department University of Florida Gainesville FL USA Department of Computer Science University of A Coruna Coruna Spain Department of Applied Mathematics University of Cantabria Santander Spain

Proper initialization of neural networks is critical for a successful training of its weights. Many methods have been proposed to achieve this, including heuristic least squares approaches. In this paper, inspired by these previous attempts to train (or initialize) neural networks, we formulate a mathematically sound algorithm based on backpropagating the desired output through the layers of a multilayer perceptron. The approach is accurate up to local first order approximations of the nonlinearities. It is shown to provide successful weight initialization for many data sets by Monte Carlo experiments.

关键词： Neural networks Least squares approximation backpropagation algorithms Neurons Least squares methods Multilayer perceptrons Monte Carlo methods Computer science Mathematics Multi-layer neural network

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A Hybrid Model for Forecasting Local Traffic Parameters

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IFAC Proceedings Volumes 2003年第14期36卷 269-273页

作者： Heidrun Belzner Klaus Bogenberger Ronald Kates BMW AG Traffic Technology 80788 Munich Germany

Predicting traffic is certainly one of the greatest challenges facing current traffic engineering research. The work presented here focuses on the prediction of local traffic parameters such as the local mean speed aggregated over short periods such as one minute. For this purpose a new hybrid model comprising an ARIMA 1 model and a neural network has been developed and validated with real traffic data. This model has been evaluated and compared with conventional methods of traffic prediction using independent real test data. It was found that the new hybrid model produces more accurate and more reliable results than conventional methods.

关键词： Forecasts ARIMA models neural networks backpropagation algorithms stochastic realization

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Coordination of Electric Power System Emergency Control Using Artificial Neural Network

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IFAC Proceedings Volumes 2003年第20期36卷 163-168页

作者： Nikolai Voropai Dmitry Efimov Pavel Etingov Energy Systems Institute 130 Lermontov Str. 664033 Irkutsk Russia Tel.: +7 3952 426 495 Fax: +7 3952 426 796 Irkutsk State Technical University 83 Lermontov Str. 664038 Irkutsk Russia Tel.: +7 3952 421 357

The paper describes different principles of emergency control organization in electric power systems. Use of the artificial neural network is considered to form coordinated emergency control actions. The presented way... 详细信息

关键词： Artificial intelligence Automatic control systems backpropagation algorithms Control actions Disturbance parameters Electric power systems Feedforward networks Load flows Transient stability assessment

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A novel Boolean self-organization mapping based on fuzzy geometrical expansion

A novel Boolean self-organization mapping based on fuzzy geo...

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International Conference on Information, Communications and Signal Processing

作者： N.S. Chaudhari D. Wang School of Computing Engineering Nanyang Technological University Singapore

A novel self-organization mapping algorithm for Boolean neural networks (BSOM) based on geometrical expansion is proposed in this paper. The proposed BSOM algorithm possesses generalization capability. Compared with traditional self organization mapping (SOM) algorithms, BSOM algorithm is based on geometrical expansion, not gradient descent. BSOM algorithm memorizes more vectors in a hidden neuron, not only an exemplar in the center of SOM cell. Finally BSOM algorithm needs less number of iterations and simple training equations. Test results are given on simple Boolean functions, and a randomly generated Boolean function with 10 variables.

关键词： Neurons Neural networks Hamming distance backpropagation algorithms Equations Boolean functions Computer networks Testing Very large scale integration Data mining

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Fingerprint and speaker verification decisions fusion

Fingerprint and speaker verification decisions fusion

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International Conference on Image Analysis and Processing

作者： K.-A. Toh Institute for Infocomm Research Singapore

By exploiting the specialist capabilities of each classifier, a combined classifier may yield results which would not be possible in a single classifier. We propose to combine fingerprint and speaker verification decisions using a functional link network. This is to circumvent the non-trivial trial-and-error and iterative training effort as seen in backpropagation neural networks which cannot guarantee global optimal solutions. In many data fusion applications, as individual classifiers to be combined would have attained a certain level of classification accuracy, the proposed functional link network can be used to combine these classifiers by taking their outputs as the inputs to the network. The network is used to combine the fingerprint and speaker verification decisions with much improved receiver operating characteristics performance as compared to an optimal weighting method.

关键词： Fingerprint recognition Biometrics Neural networks Polynomials Feedforward neural networks RAKE receivers Probability backpropagation algorithms Predictive models Sensitivity analysis

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A new class of convolutional neural networks (SICoNNets) and their application of face detection

A new class of convolutional neural networks (SICoNNets) and...

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International Joint Conference on Neural Networks (IJCNN)

作者： F.H.C. Tivive A. Bouzerdoum School of Engineering and Mathematics Edith Cowan University Joondalup WA Australia

Artificial neural networks (ANNs), evolved from biological insights, have equipped computers with the capacity to actually learn from examples using real world data. With this remarkable ability, ANNs are able to extract patterns and detect trends that are too complex to be noticed or perceived by either humans or classical computer techniques. Nevertheless, as the amount of data to be processed increases significantly there is a demand for developing other types of artificial neural networks to perform complex pattern recognition tasks. In this article, a new class of convolutional neural networks, namely shunting inhibitory convolutional neural networks (SICoNNets), is introduced, and a training algorithm is developed using supervised learning based on resilient backpropagation with momentum. Three different network topologies, ranging from fully-connected to partially-connected, are implemented and trained to discriminate between face and non-face patterns. All three architectures achieve more than 96% correct face classification; the best architecture achieves 97.6% correct face classification at a false alarm rate of 3.4%.

关键词： Neural networks Face detection Artificial neural networks Biology computing Application software Computer networks Data mining Humans Pattern recognition backpropagation algorithms

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A new fast multilayer perceptron training procedure based on the Davidon Fletcher Powell algorithm

A new fast multilayer perceptron training procedure based on...

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International Symposium on Image and Signal Processing and Analysis

作者： S. Abid F. Fnaiech CEREP ESSTT Tunis Tunisia

The Davidon Fletcher Powell (DFP) optimization algorithm usually used for nonlinear least squares is presented and is combined with the standard backpropagation (SBP) algorithm yielding a new fast training multilayer perceptron (MLP) algorithm (DFP/SBP). The new algorithm is tested on several function approximation problems. The number of iterations required by this algorithm to converge is less than 40% of what is required by the SBP algorithm. Also it is less affected by the choice of initial weights and setup parameters. The DFP/SBP algorithm is much more efficient than either of other techniques when the network contains no more than few hundred weights.

关键词： backpropagation algorithms Signal processing algorithms Multilayer perceptrons Least squares methods Convergence Approximation algorithms Least squares approximation Acceleration Newton method Signal processing

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The real-time identification of dynamic systems by using the neural net approach

The real-time identification of dynamic systems by using the...

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IEEE International Conference on Industrial Technology (ICIT)

作者： J. Dohnal P. Pivonka Department of Control and Instrumentation Brno University of Technology Brno Czech Republic

Neural networks play a significant role in the sphere of control of a dynamic systems. It can be appropriately used for continuous identification of parameters of the system as neural controllers, etc. The neural networks must be correctly learnt to meet our requirements. There is a great number of learning algorithms. Among the most used learning algorithms belong the Levenberg-Marquardt and backpropagation.

关键词： Real time systems Neural networks backpropagation algorithms Neurons Control systems Instruments Adaptive control Least squares methods Sampling methods Three-term control

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Automatic detection of premature ventricular contraction using quantum neural networks

Automatic detection of premature ventricular contraction usi...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Jie Zhou Department of Computer Science Northern Illinois University Dekalb IL USA

ISBN: (纸本)0769519075

Premature ventricular contractions (PVCs) are ectopic heart beats originating from ventricular area. It is a common form of heart arrhythmia. Electrocardiogram (ECG) recordings have been widely used to assist cardiologists to diagnose the problem. In this paper, we study the automatic detection of PVC using a fuzzy artificial neural network named Quantum Neural Network (QNN). With the quantum neurons in the network, trained QNN can model the levels of uncertainty arising from complex classification problems. This fuzzy feature is expected to enhance the reliability of the algorithm, which is critical for the applications in the biomedical domain. Experiments were conducted on ECG records in the MIT-BIH Arrhythmia Database. Results showed consistently higher or same reliability of QNN on all the available records compared to the backpropagation network. QNN, however, has a relatively higher resource requirement for training.

关键词： Heart rate variability Neural networks Artificial neural networks Electrocardiography Heart beat Cardiology Fuzzy neural networks Neurons Uncertainty backpropagation algorithms

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State estimation for flexible-joint manipulators using stable neural networks

State estimation for flexible-joint manipulators using stabl...

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IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA)

作者： F. Abdollahi H.A. Talebi R.V. Patel Department of Electrical Engineering Amirkabir University of Technology슠 Tehran Iran Department of Electrical and Computer Engineering University of Western Ontario London Canada

A stable neural network based observer for general multivariable nonlinear system is presented in this paper. Unlike most previous neural network observers, the proposed observer uses nonlinear in parameter neural network (NLPNN). Therefore, it can be applied to systems with higher degrees of nonlinearity without any a priori knowledge of system dynamics. The learning rule of the neural network is based on backpropagation algorithm. backpropagation is a well known algorithm, which is easy to implement, and it has been successfully applied to many engineering problems. However, previous works on backpropagation suffer from lack of mathematical proof of stability. An e-modification term is also added to guarantee the robustness of the observer. No SPR or any other strong assumption is imposed on the proposed approach. The stability of the recurrent neural network observer is shown by Lyapunov's direct method. The proposed neural network observer is applied to a flexible-joint manipulator to evaluate its performance of the new scheme.

关键词： State estimation Neural networks Nonlinear systems Nonlinear dynamical systems Observers Manipulator dynamics backpropagation algorithms Stability MIMO Robustness

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