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检索条件"主题词=Backpropagation Algorithms"

共 1892 条记录，以下是391-400 订阅

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Detection of Spikes with Multiple Layer Perceptron Network Structures

Detection of Spikes with Multiple Layer Perceptron Network S...

引用

IEEE Signal Processing and Communications Applications (SIU)

作者： Kutlu Isler Kuntalp Elektrik ve Elektronik Mühendisliği Bölümü Dokuz Eylül Üniversitesi Kaynaklar Kampüsü Buca İzmir yakup.kutlu@eee.deu.edu.tr

In this work, the spikes in the electroencephalogram (EEG) signals are analyzed by using artificial neural networks (ANN). Multiple layer perceptron (MLP) networks utilizing between 3 and 15 hidden neurons are used in the network architecture. For training the MLP network backpropagation algorithm, backpropagation with adaptive learning rate, Levenberg-Marquardt (LM) algorithm, early stopping and regularization methods are used. Principal components of feature vectors obtained from 41 consecutive sample values of each peak are used for training the networks. Performances of classifiers are examined for two cases depending on both sensitivity-specificity and sensitivity-selectivity properties

关键词： Electroencephalography Epilepsy Microstrip Artificial neural networks backpropagation algorithms Reactive power Signal analysis Testing Neurons Tin

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High Performance Text Categorization System Based on a Novel Neural Network Algorithm

High Performance Text Categorization System Based on a Novel...

引用

International Conference on Computer and Information Technology (CIT)

作者： Cheng Hua Li Soon Cheol Park Division of Electronics and Information Engineering Chonbuk National University of Korea Jeonju South Korea

This paper describes a novel approach for text categorization based on the improved backpropagation neural network (BPNN). BPNN has been widely used in classification and pattern recognition. However it has some generally acknowledged defects, such as slow convergence and easy to enter into local minima. In this paper, we introduce an improved BPNN that can overcome these defects. We tested the improved model on the standard Reuter-21578, and the result shows that the proposed model is able to achieve high categorization effectiveness as measured by the precision, recall and F-measure.

关键词： Text categorization Neural networks Neurons Computer errors Multi-layer neural network Feedforward systems Convergence Feedforward neural networks backpropagation algorithms Pattern recognition

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LearningWeights for Linear Combination of Forecasting Methods

LearningWeights for Linear Combination of Forecasting Method...

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Brazilian Symposium on Neural Networks

作者： Ricardo B. C. Prudencio Teresa B. Ludermir Departament of Information Science Federal University of Pernambuco (UFPE) Pernambuco Brazil Center of Informatics Federal University of Pernambuco (UFPE) Recife Brazil

ISBN: (纸本)9780769531960

The linear combination of forecasts is a procedure that has improved the forecasting accuracy for different time series. We present here the use of machine learning techniques to define numerical weights for the linear combination of forecasts. In this approach, a machine learning technique uses features of the series at hand to define the adequate weights for a pre-defined number of forecasting methods. In order to evaluate this solution, we implemented a prototype that uses a MLP network to combine two widespread methods. The performed experiments revealed significantly accurate forecasts.

关键词： backpropagation algorithms Machine learning Prototypes Neural networks Information science Informatics Convergence

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Online Detection of the Nature of Complex-Valued Signals

Online Detection of the Nature of Complex-Valued Signals

引用

IEEE Workshop on Machine Learning for Signal Processing

作者： Phebe Vayanos Su Lee Goh Danilo P. Mandic Imperial College London UK

A novel method for on-line tracking of the changes in the nature of a complex-valued signal is proposed. This is achieved by analysing the time variation of the mixing parameter within a hybrid complex-valued nonlinear adaptive filter. The proposed hybrid filter consists of a combination of split- and fully-complex nonlinear gradient descent algorithms, whose outputs are mixed in a convex manner. A learning algorithm for this scheme is derived and the potential of such an approach for tracking of signal modality changes is highlighted. The potential of the proposed approach is supported by simulations on both a synthetic benchmark signal and on real-world radar data.

关键词： Adaptive filters Signal processing Signal processing algorithms Machine learning algorithms Radar tracking Testing Neural networks backpropagation algorithms Neurons Educational institutions

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Clustering and Artificial Neural Networks as a Tool to Generate Membership Functions

Clustering and Artificial Neural Networks as a Tool to Gener...

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International Conference on Electronics, Communications and Computers (CONIELECOMP)

作者： A. Sanchez R. Alvarez J.C. Moctezuma S. Sanchez Benemérita Universidad Autónoma de Puebia Mexico

This work show an application of the classical clustering algorithm, c-mean mixed with Artificial Neural Networks to build membership functions of fuzzy systems, the simulations were done with MATLAB.

关键词： Artificial neural networks Fuzzy systems Fuzzy sets Clustering algorithms Partitioning algorithms MATLAB Biological neural networks Neurons backpropagation algorithms Computer networks

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Convergence Analysis of Generalized Back-propagation Algorithm with Modified Gradient Function

Convergence Analysis of Generalized Back-propagation Algorit...

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International Joint Conference on Neural Networks (IJCNN)

作者： S.C. Ng S.H. Leung A. Luk Yunfeng Wu School of Science & Technology Open University of Hong Kong Hong Kong China Department of Electronic Engineering City University of Hong Kong Hong Kong China Saint B and P Neural Investments Private Limited Westleigh NSW Australia School of Information Engineering Beijing University of Posts and Telecommunications Beijing China

In this paper, we further investigate the convergence properties of the generalized back-propagation algorithm using magnified gradient function (MGFPROP). The idea of MGFPROP is to increase the convergence rate by magnifying the gradient function of the activation function. The algorithm can effectively speed up the convergence rate and reduce the chance of being trapped in premature saturation. From the convergence analysis, it is shown that MGFPROP retains the gradient descent property, gives faster convergence and has better global searching capability than that of the back-propagation algorithm.

关键词： Convergence Algorithm design and analysis Neurons backpropagation algorithms Acceleration Investments Australia Error correction Equations Standards development

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Recognition Mechanism of a Neural Network for Character Recognition

Recognition Mechanism of a Neural Network for Character Reco...

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

作者： H. Shintani M. Akutagawa H. Nagashino Y. Kinouchi Faculty of Engineering University of Tokushima Tokushima Japan School of health sciences University of Tokushima Tokushima Japan

We have developed a small scale four-layered neural network (NN) model for simple character recognition, which can recognize the patterns transformed by affine conversion. In this study 24 patterns are presented as input patterns. An input pattern is divided into 64 local patterns and connected with the 1st hidden layer. After the training, we investigated the recognition mechanism of NN using Alopex algorithm. Effectiveness of this method is demonstrated

关键词： Character recognition Neural networks Pattern recognition Brain modeling Neurons Pixel backpropagation algorithms Multi-layer neural network Power system modeling Computer vision

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Fuzzy Data Mining by Heuristic Rule Extraction and Multiobjective Genetic Rule Selection

Fuzzy Data Mining by Heuristic Rule Extraction and Multiobje...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： H. Ishibuchi Y. Nojima I. Kuwajima Department of Computer Science and Intelligent Systems Graduate School of Engineering Osaka Prefecture University Sakai Osaka Japan

In this paper, we demonstrate that multiobjective genetic rule selection can significantly improve the accuracy-complexity tradeoff curve of fuzzy rule-based classification systems generated by a heuristic rule extraction procedure for classification problems with many continuous attributes. First a prespecifled number of fuzzy rules are extracted in a heuristic manner based on a rule evaluation criterion. This step can be viewed as fuzzy data mining. Then multiobjective genetic rule selection is applied to the extracted rules to find a number of non-dominated rule sets with respect to accuracy maximization and complexity minimization. This step can be viewed as a postprocessing procedure in fuzzy data mining. Experimental results show that multiobjective genetic rule selection finds a number of smaller rule sets with higher classification accuracy than heuristically extracted rule sets. That is, the accuracy-complexity tradeoff curve of heuristically extracted rule sets in fuzzy data mining is improved by multiobjective genetic rule selection. This observation suggests that multiobjective genetic rule selection plays an important role in fuzzy data mining as a postprocessing procedure.

关键词： Data mining Genetics Fuzzy sets Fuzzy systems backpropagation algorithms Neural networks Machine learning algorithms Evolutionary computation Character generation Radial basis function networks

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Back-Propagation and K-Means algorithms Comparison

Back-Propagation and K-Means Algorithms Comparison

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International Conference on Signal Processing Proceedings (ICSP)

作者： Vladislav Skorpil Jiri Stastny Department of Telecommunications Brno University of Technology Brno Czech Republic Department of Automation and Computer Science Brno University of Technology Brno Czech Republic

The paper describes the application of algorithms for object classification by using artificial neural networks. The MLP (multi layer perceptron) and RBF (radial basis function) neural networks were used. We compared results obtained by a using of learning algorithms back-propagation (BP) and K-means. The real technological scene for object classification was simulated with digitization of two-dimensional pictures. The principles and algorithms given below have been used in an application that was developed at Brno University of Technology

关键词： Neurons Iterative algorithms backpropagation algorithms Neural networks Artificial neural networks Computer vision Computer networks Shape Automation Computer science

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Effective Speech/Pause Discrimination Combining Noise Suppression and Fuzzy Logic Rules

Effective Speech/Pause Discrimination Combining Noise Suppre...

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

作者： R. Culebras J. Ramirez J.M. Gorriz Department of Signal Theory Networking and Communications University of Granada Spain

This paper shows an effective speech/pause discrimination method combining spectral noise filtering and fuzzy logic rules. The fuzzy system is based on a Sugeno inference engine with membership functions defined as combination of two Gaussian functions. Its operation is optimized by means of a hybrid training algorithm combining the least-squares method and the backpropagation gradient descent method for training membership function parameters. The fuzzy classifier consists of ten fuzzy rules defined in terms of the denoised subband signal-to-noise ratios (SNRs) and the zero crossing rate (ZCRs). An exhaustive analysis conducted on the Spanish SpeechDat-Car databases is conducted in order to assess the performance of the proposed method and to compare it to existing standard VAD methods. The results show improvements in detection accuracy over standard VADs and a representative set of recently reported VAD algorithms

关键词： Speech enhancement Fuzzy logic Filtering Fuzzy systems Engines Optimization methods Inference algorithms backpropagation algorithms Signal to noise ratio Speech analysis

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