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检索条件"主题词=Backpropagation Algorithms"

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A self-organizing neural structure for concept formation from incomplete observation

A self-organizing neural structure for concept formation fro...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： N. Homma M.M. Gupta Department of Radiological Technology College of Medical Sciences University of Tohoku Sendai Japan Intelligent Systems Research Laboratory College of Engineering University of Saskatchewan Saskatoon Canada

We propose a self-organizing neural structure with dynamic and spatial changing weights for a feature space representation of concept formation. An essential core of this self-organization is based on an unsupervised learning with incomplete information for the dynamic changing and an extended Hebbian rule for the spatial changing. A concept formation problem requires the neural network to acquire the complete feature space structure of a concept information using an incomplete observation of the concept. The connection structure or self-organizing network can store with the information structure by using the two rules. The Hebbian rule can create a necessary connection corresponding to a feature space substructure of the complete information. On the other hand, unsupervised learning can delete unnecessary connections. Finally concept formation ability of the proposed neural network is proven under some conditions.

关键词： Biological neural networks Unsupervised learning Cognition backpropagation algorithms Space technology Educational institutions Shape Intelligent systems Intelligent structures Laboratories

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A brief discussion on moderatism based local gradient learning rules

A brief discussion on moderatism based local gradient learni...

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International Symposium on Signal Processing and Its Applications (ISSPA)

作者： M.T. Islam Y. Okabe Department of Electronic Engineering University of Tokyo Bunkyo Tokyo Japan

Moderatism [Y. Okabe et al., 1988], which is a learning rule for ANNs, is based on the principle that individual neurons and neural nets as a whole try to sustain a "moderate" level in their input and output signals. In this way, a close mutual relationship with the outside environment is maintained. In this paper, two potential moderatism-based local, gradient learning rules are proposed. Then, a pattern learning experiment is performed to compare the learning performances of these two learning rules, the error based weight update (EBWU) rule [Tanvir Islam, M et al., December 2001][Tanvir Islam, M et al., September 2001], and error backpropagation [Bishop, CM et al., 1995].

关键词： Neurons Error correction Neural networks Multi-layer neural network Biological system modeling Cost function Artificial neural networks backpropagation algorithms Equations

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Discrete-time nonlinear system identification using recurrent neural networks

Discrete-time nonlinear system identification using recurren...

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IEEE Conference on Decision and Control

作者： W. Yu X. Li Departamento de Control Automatico CINVESTAV Mexicali Mexico Seccióon de Computacióon Departamento de Ingenieria Eléectrica CINVESTAV Mexicali Mexico

In this paper we proposed a novel discrete-time recurrent neural networks. Input-to-state stability (ISS) approach is applied to access robust training algorithms. We conclude that for discrete-time nonlinear system identification, the gradient descent law and the backpropagation-like algorithm for the weights adjustment are stable in the sense of L/sub /spl infin// and robust to any bounded uncertainties.

关键词： Nonlinear systems Recurrent neural networks Neural networks backpropagation algorithms Robustness Uncertainty Feedforward neural networks Stability analysis Robust stability Function approximation

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Spectral histogram based face detection

Spectral histogram based face detection

引用

International Joint Conference on Neural Networks (IJCNN)

作者： C. Waring Xiuwen Liu Department of Computer Science Florida State University Tallahassee FL USA

This paper adopts a generic feature representation and applies it to the task of face detection as an appearance-based case. The distribution of faces and non-faces are modeled from the marginal distribution of filter responses. The face detection algorithm proposed here uses the spectral representation of a 21/spl times/21 image window as input to a multiple layer perceptron for classification. The classifier is trained with the backpropagation learning rule. A simple method to correct nonuniform illuminance is used to normalize all training and test images. Testing is done on a standard data set and compared to the work of others.

关键词： Histograms Face detection Testing Object detection Image edge detection Mutual information Snow Computer science Filters backpropagation algorithms

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Regularization and Feedforward artificial neural network training with noise

Regularization and Feedforward artificial neural network tra...

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International Joint Conference on Neural Networks (IJCNN)

作者： P. Chandra Y. Singh School of Information Technology G.G.S. Indraprastha University New Delhi India

Regularization is a method used for controlling the complexity of models. Explicit regularization uses a modifier term, incorporating a-priori knowledge about the function to be approximated by Feedforward Artificial Networks, that is added to the risk functional and implicit regularization where noise is added to the system variables during training, are two of the commonly used techniques for model complexity control. The relationship between these two type of regularization is explained. A regularization term is derived based on the general noise model. The interplay between the various noise mediated regularization terms is described.

关键词： Artificial neural networks backpropagation algorithms Information technology Supervised learning Input variables Minimization methods Computer errors Phase noise Training data Euclidean distance

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Initializing artificial neural networks by genetic algorithm to calculate the resonant frequency of single shorting post rectangular patch antenna

Initializing artificial neural networks by genetic algorithm...

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Antennas and Propagation Society International Symposium

作者： S. Devi D.C. Panda S.S. Pattnaik B. Khuntia D.K. Neog FIETE FIETE India NERIST Nirjuli India Dhemaji College Assam India

ISBN: (纸本)0780378466

A simple and accurate method for calculating the resonant frequency of a rectangular microstrip patch antenna with a single shorting post by using the artificial neural networks is presented in this paper. Genetic algorithm is used to select the initial weights of artificial neural networks. The calculated resonant frequency is compared with experimental results. The results are in very good agreement with experimental results with decreased computational time.

关键词： Artificial neural networks Genetic algorithms Resonant frequency Patch antennas Neural networks Microstrip antennas Multi-layer neural network backpropagation algorithms Educational institutions Computer simulation

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A hierarchical neuro-fuzzy system based on S-implications

A hierarchical neuro-fuzzy system based on S-implications

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International Joint Conference on Neural Networks (IJCNN)

作者： R. Nowicki R. Scherer L. Rutkowski Department of Computer Engineering Czestochowa University of Technology Czestochowa Poland

In this paper, we present a neuro-fuzzy structure of the hierarchical prioritized structure (HPS) proposed by Yager. The HPS allows for easy hierarchization of a fuzzy rule-base. Our neuro-fuzzy system can be learned ... 详细信息

关键词： Fuzzy neural networks Fuzzy systems Telephony Fuzzy sets Artificial intelligence backpropagation algorithms Hierarchical systems

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Fast and efficient second-order training of the dynamic neural network paradigm

Fast and efficient second-order training of the dynamic neur...

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International Joint Conference on Neural Networks (IJCNN)

作者： C. Gruber B. Sick Chair of Computer Architectures University of Passau Passau Germany

In many applications neural networks must process or generate time series, and various network paradigms exist for this purpose. Two prominent examples are time-delay neural networks (TDNN), which are known for their noise suppression capability, and NARX (nonlinear autoregressive models with exogenous inputs) networks, which have a powerful modeling ability (at least Turing equivalence). In this article, we suggest a combination of these two approaches, called dynamic neural network (DYNN), which unifies the particular advantages. Efficient training algorithms are needed to adjust the weights of DYNN. Here, we describe an algorithm for the computation of first-order information about the error surface: temporal backpropagation through time (TBPTT). Essentially, this algorithm is a combination of temporal backpropagation (used for TDNN) and backpropagation through time (used for NARX). The first-order information is then utilized to apply the scaled conjugate gradient (SCG) learning algorithm which approximates second-order with first-order information. The benefits of this approach are shown by means of two benchmark data sets: "logistic map" and "building". It is shown that SCG for DYNN is significantly faster and more accurate than other learning algorithms (e.g. TBPTT, resilient propagation, memoryless Quasi-Newton).

关键词： Neural networks backpropagation algorithms Delay Finite impulse response filter Computer architecture Application software Logistics Predictive models Neurofeedback Adaptive systems

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Kalman filtering as a multilayer perceptron training algorithm for detecting changes in remotely sensed imagery

Kalman filtering as a multilayer perceptron training algorit...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Y. Chibani H. Nemmour Laboratoire de Traitement du Signal Faculté d'Electronique et d'Informatique Université des Sciences et de la Technologie Houari Boumediene Algiers Algeria

The multilayer perceptron is usually trained by the backpropagation (BP) algorithm for computing the synaptic weights. In this paper, we investigate the use of Kalman filtering (KF) as a training algorithm for detecting changes in remotely sensed imagery. By using SPOT images and based on some evaluation criteria, the detailed comparison indicates that the KF algorithm is preferable compared to the BP algorithm in terms of convergence rate, stability and change detection accuracy.

关键词： Kalman filters Multilayer perceptrons Filtering algorithms Change detection algorithms backpropagation algorithms Neural networks Convergence Equations Mean square error methods Stability criteria

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A soft approach to ERA algorithm for hyperspectral image classification

A soft approach to ERA algorithm for hyperspectral image cla...

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International Symposium on Image and Signal Processing and Analysis

作者： J.D. Martin-Guerreo L. Gomez-Chova J. Calpe-Maravilla G. Camps-Valls E. Soria-Olivas J. Moreno Grup de Processament Digital de Senyals Valencia Univ. Spain Grup de Processament Digital de Senyals Universitat de València BURJASSOT Spain Departament de Termodinàmica Universitat de València Spain

This work presents a novel supervised algorithm to solve the problem of crop classification using Hymap hyperspectral images acquired under controlled conditions in the Barrax area (Spain). We considered a multilayer perceptron (MLP) trained with the expanded range approximation (ERA) algorithm where the training set was varied in order to avoid local minima on the function error, which is an important drawback of the classical backpropagation (BP) algorithm. Soft modifications to ERA are proposed in order to improve its performance and robustness by tailoring smooth homotopy functions of the training set expansion. Our proposal is benchmarked with MLP trained by the BP and ERA algorithms and with other classical techniques, such as the radial basis function (RBF) network and the maximum-likelihood Gaussian classifier (MLGC).

关键词： Hyperspectral imaging Image classification backpropagation algorithms Signal processing algorithms Hyperspectral sensors Proposals Surface reconstruction Space technology Image analysis Earth

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