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检索条件"主题词=Backpropagation Algorithms"

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An analysis of underfitting in MLP networks

An analysis of underfitting in MLP networks

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International Joint Conference on Neural Networks (IJCNN)

作者： S. Narayan G. Tagliarini Dept. of Comput. Sci. North Carolina Univ. Wilmington NC USA Department of Computer Science Uni ersit of North Carolina Wilmington Wilmington NC USA

The generalization ability of an MLP network has been shown to be related to both the number and magnitudes of the network weights. Thus, there exists a tension between employing networks with few weights that have relatively large magnitudes, and networks with a greater number of weights with relatively small magnitudes. The analysis presented in this paper indicates that large magnitudes for network weights potentially increase the propensity of a network to interpolate poorly. Experimental results indicate that when bounds are imposed on network weights, the backpropagation algorithm is capable of discovering networks with small weight magnitudes that retain their expressive power and exhibit good generalization.

关键词： Intelligent networks Neural networks Training data Neurons Computer science backpropagation algorithms Multilayer perceptrons Multi-layer neural network Signal detection Interpolation

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Classification of Mental Tasks Using Fixed and Adaptive Autoregressive Models of EEG Signals

Classification of Mental Tasks Using Fixed and Adaptive Auto...

引用

International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Nai-Jen Huan R. Palaniappan Faculty of Information Science & Technology Multimedia University Malacca Malaysia Department of Computer Science University of Essex Colchester UK

Classification of EEG signals extracted during mental tasks is a technique for designing brain computer interfaces (BCI). In this paper, we classify EEG signals that were extracted during mental tasks using fixed autoregressive (FAR) and adaptive AR (AAR) models. Five different mental tasks from 4 subjects were used in the experimental study and combinations of 2 different mental tasks are studied for each subject. Four different feature extraction methods were used to extract features from these EEG signals: FAR coefficients computed with Burg's algorithm using 125 data points, without segmentation and with segmentation of 25 data points, AAR coefficients computed with least-mean-square (LMS) algorithm using 125 data points, without segmentation and with segmentation of 25 data points. Multilayer perceptron (MLP) neural network (NN) trained by the backpropagation (BP) algorithm is used to classify these features into the different categories representing the mental tasks. The best results for FAR was 92.70% while for AAR was only 81.80%. The results obtained here indicated that FAR using 125 data points without segmentation gave better classification performance as compared to AAR, with all other parameters constant

关键词： Brain modeling Electroencephalography backpropagation algorithms Feature extraction Neural networks Signal design Brain computer interfaces Data mining Least squares approximation Multilayer perceptrons

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Dynamical consistent recurrent neural networks

Dynamical consistent recurrent neural networks

引用

International Joint Conference on Neural Networks (IJCNN)

作者： H.-G. Zimmermann R. Grothmann A.M. Schafer C. Tietz Corporate Technology Information & Communication Division Department of Neural Computation Siemens AG Germany

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Automatic birdsong recognition based on autoregressive time-delay neural networks

Automatic birdsong recognition based on autoregressive time-...

引用

ICSC Congress on Computational Intelligence Methods and Applications, CIMA

作者： S.-A. Selouani M. Kardouchi E. Hervet D. Roy Université de Moncton Shippagan Canada Université de Moncton Moncton Canada

A template-based technique for automatic recognition of birdsong syllables is presented. This technique combines time delay neural networks (TDNNs) with an autoregressive (AR) version of the backpropagation algorithm in order to improve the accuracy of bird species identification. The proposed neural network structure (AR-TDNN) has the advantage of dealing with a pattern classification of syllable alphabet and also of capturing the temporal structure of birdsong. We choose to carry out trials on song patterns obtained from sixteen species living in New Brunswick province of Canada. The results show that the proposed AR-TDNN system achieves a highly recognition rate compared to the baseline backpropagation-based system

关键词： Neural networks Birds Pattern recognition Pattern matching Spectrogram Delay effects backpropagation algorithms Pattern classification Buildings Signal analysis

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Fast training of multilayer perceptrons with a mixed norm algorithm

Fast training of multilayer perceptrons with a mixed norm al...

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International Joint Conference on Neural Networks (IJCNN)

作者： S. Abid F. Fnaiech B.W. Jervis M. Cheriet ESSTT Tunis Tunisia Applied Electronics Research Group School of Engineering Sheffield Hallam University Sheffield UK Laboratory for Image Vision and artificial intelligence ETS Montreal QUE Canada

A new fast training algorithm for the multilayer perceptron (MLP) is proposed. This new algorithm is based on the optimization of a mixed least square (LS) and a least fourth (LF) criterion producing a modified form of the standard back propagation algorithm (SBP). To determine the updating rules in the hidden layers, an analogous back propagation strategy used in the conventional learning algorithms is developed. This permits the application of the learning procedure to all the layers. Experimental results on benchmark applications and a real medical problem are obtained which indicates significant reduction in the total number of iterations, the convergence time, and the generalization capacity when compared to those of the SBP algorithm.

关键词： Multilayer perceptrons backpropagation algorithms Least squares methods Back Convergence Least squares approximation Neurons Laboratories Artificial intelligence Biomedical imaging

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Comparison of TDNN training algorithms in brain machine interfaces

Comparison of TDNN training algorithms in brain machine inte...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yiwen Wang Sung-Phil Kim J.C. Principe Computational NeuroEngineering Laboratory University of Florida Gainesville FL USA

Linear or non-linear models are used in brain machine interfaces (BIMIs) to map the neural activity to the associated behavior, typically the primate's hand position. Linear models assume a linear relationship between neural activity and hand position that may not be the case. A solution would be time-delay neural network (TDNN) that provides effectively a nonlinear combination of linear models. However, this model results in a drastic increase of free parameters and slow convergence when trained by an error backpropagation learning rule. We propose to train the TDNN by scaled conjugate gradient, which avoids time-consuming linear search, coupled with weight decay to reduce the free parameters number and produce generally faster convergence.

关键词： Convergence Brain modeling Error correction Biological neural networks Cost function Finite impulse response filter backpropagation algorithms Brain computer interfaces Computer interfaces Neural engineering

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On the evaluation of relevance learning by a multi-layer perceptron

On the evaluation of relevance learning by a multi-layer per...

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International Joint Conference on Neural Networks (IJCNN)

作者： K. Suzuki S. Hashimoto Department of Intelligent Interaction Technologies University of Tsukuba Tsukuba Japan Department of Applied Physics Waseda University Tokyo Japan

In this paper, we introduce a novel method of relevance learning by a multi-layer perceptron. The relevance learning is regarded as learning from the relationship among two or more outputs of the network. The learning network architecture is based on a simple multi-layer perceptron with a modified back-propagation learning algorithm. Unlike the conventional multi-layer perceptron that learns from a set of an input feature vector and the target output, the proposed network can obtain a nonlinear mapping between a set of two or more vector inputs and the desired relevance. For instance, the desired relevance represents the dissimilarity among given objects. We show the performance of the proposed network with some experiments with four artificially generated data set. We then discuss the theoretical and mathematical background underlying the network learning with some related works. We evaluate the obtained arrangement of objects in comparison with the result of principle component analysis (PCA) and multidimensional scaling method (MDS). This work also contributes to the measurement of human subjective evaluation for multidimensional perceptual scaling. Some experimental results on the low-dimensional representation of color hue data set and emotional facial images were presented.

关键词： Multilayer perceptrons Multidimensional systems backpropagation algorithms Feature extraction Learning systems Principal component analysis Anthropometry Humans Pattern recognition Resonance

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Bistatic fixed-receiver parasitic SAR processor based on the back-propagation algorithm

Bistatic fixed-receiver parasitic SAR processor based on the...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： J. Sanz-Marcos P. Prats J.J. Mallorqui Signal Theory and Communications Department Universitat Politecnica de Catalunya Barcelona Spain

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Simulated annealing dynamic RPROP for training recurrent fuzzy systems

Simulated annealing dynamic RPROP for training recurrent fuz...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： P.A. Mastorocostas I.T. Rekanos Department of Informatics & Communications Technological Educational Institute of Serres Serres Greece

An adaptive learning method for recurrent fuzzy systems is proposed. The method modifies the SARPROP algorithm, originally developed for static neural models, in order to be applied to dynamic models. A comparative analysis with dynamic RPROP and back propagation through time is given, indicating the enhanced learning capabilities of the proposed algorithm

关键词： Simulated annealing Fuzzy systems Learning systems Informatics Communications technology Educational technology Convergence Neural networks backpropagation algorithms Error correction

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Genetic programming approach for fault modeling of electronic hardware

Genetic programming approach for fault modeling of electroni...

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Congress on Evolutionary Computation

作者： A. Abraham C. Grosan School of Computer Science and Engineering Chung-Ang University Seoul South Korea Department of Computer Science Babes Bolyai University Cluj-Napoca Romania

This paper presents two variants of genetic programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the stressor - susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after preprocessing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.

关键词： Genetic programming Hardware Circuit faults Condition monitoring Electronic circuits Stress measurement Standardization Predictive models Artificial neural networks backpropagation algorithms

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