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检索条件"主题词=Backpropagation Algorithms"

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Building exterior design system by hierarchical combination fuzzy model

Building exterior design system by hierarchical combination ...

引用

Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： N. Ikoma H. Maeda Y. Kobayashi T. Tabira D. Yamamoto Department of Computer Engineering Faculty of Engineering Kyushu Institute of Technology Tobata Japan Kouzou Keikaku Engineering Inc. Tokyo Japan

We propose a new "Kansei" design method for a building exterior by using a decomposed fuzzy model and its inverse problem solution. The Kansei of the human to CG image of the building exterior is measured by the SD technique. The decomposed fuzzy model is used to perform the human reactions. The model is divided into sub-models with a single output variable. Input variables are common in these sub-models. In one sub-model, each input variable is fed into the fuzzy inference unit separately, and the output signals of these fuzzy inference units are combined two by two, by combination unit. Parameters of the model are tuned by an error backpropagation algorithm using the date measured by the SD technique. For the design of the building exterior to satisfy a customer's Kansei request, the inverse problem of the model is solved. Firstly, inverse problems for all sub-models are solved separately, under a constraint of noninferior property to the solution. These solutions are unified by taking the intersection for all sub-models. In general, the unified solution is still a set of intervals, then the unique solution is required to draw a CG image of the building exterior, and is obtained by a numerical optimization method to satisfy the customer's requests as much as possible. An experimental result is reported, and the method is implemented on the WWW system using Java and CGI.

关键词： Buildings Fuzzy systems Inverse problems Humans Character generation Input variables Design methodology backpropagation algorithms Optimization methods World Wide Web

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Enhancing the generalization ability of neural networks by using Gram-Schmidt orthogonalization algorithm

Enhancing the generalization ability of neural networks by u...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： W. Wan K. Hirasawa J. Hu J. Murata Intelligent Control Laboratory Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (纸本)0780370449

The generalization ability of neural networks is an important criterion when determining whether one algorithm is powerful or not. Many new algorithms have been devised to enhance the generalization ability of neural networks. In this paper an algorithm using the Gram-Schmidt orthogonalization algorithm on the outputs of nodes in the hidden layers is proposed with the aim to reduce the interference among the nodes in the hidden layers, which is much more efficient than the regularizers methods. Simulation results confirm the above assertion.

关键词： Neural networks backpropagation algorithms Neurons Sparse matrices Interference Error correction Intelligent control Laboratories Information science Weight control

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A functional manipulation for improving tolerance against multiple-valued weight faults of feedforward neural networks

A functional manipulation for improving tolerance against mu...

引用

International Symposium on Multiple-Valued Logic

作者： N. Kamiura Y. Taniguchi N. Matsui Department of Computer Engineering Himeji Institute of Technology Himeji Japan Sharp Corporation Abeno Osaka Japan

ISBN: (纸本)0769510833

In this paper we propose feedforward neural networks (NNs for short) tolerating multiple-valued stuck-at faults of connection weights. To improve the fault tolerance against faults with small false absolute values, we employ the activation function with the relatively gentle gradient for the last layer, and steepen the gradient of the function in the intermediate layer. For faults with large false absolute values, the function working as filter inhibits their influence by setting products of inputs and faulty weights to allowable values. The experimental results show that our NN is superior in fault tolerance and learning time to other NNs employing approaches based on fault injection, forcible weight limit and so forth.

关键词： Neural networks Neurons Fault tolerance Feedforward neural networks backpropagation algorithms Filters Character recognition Computer networks Hardware

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Tracking a maneuvering target using neural fuzzy network

Tracking a maneuvering target using neural fuzzy network

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Fun-Bin Duh Chin-Teng Lin Department of Electrical and Control Engineering National Chiao Tung University Hsinchu Taiwan

A fast target maneuver detecting and highly accurate tracking technique using a neural fuzzy network based on Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking? To solve this problem, neural network and fuzzy algorithms have been issued recently. However, the normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size, and the fuzzy algorithms are not easy to partition the parameters. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. Without having to change the structure of Kalman filter nor modeling the maneuvering target, SONFIN algorithm, can always find itself an economic network size with a fast learning process. Simulation results show that the KF-SONFIN is superior to the traditional IE and VDF methods in estimation accuracy.

关键词： Target tracking Fuzzy neural networks Neural networks Fuzzy logic Partitioning algorithms Inference algorithms Filters backpropagation algorithms Economic forecasting Fuzzy systems

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Design of complex-valued CNN filters for medical image enhancement

Design of complex-valued CNN filters for medical image enhan...

引用

Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： K. Kondo M. Iguchi H. Ishigaki Y. Konishi K. Mabuchi Faculty of Engineering Himeji Institute of Technology Himeji Hyogo Japan Center for Collaborative Research University of Tokyo Meguro Tokyo Japan

In this paper, we present a new image enhancement technique, using cellular neural network (CNN) filters with complex weighting factors, that is applicable to medical images. Since CNN-type filters have only spatially local interconnections and the number of connections between neurons is relatively low, the required computation in the learning phase is a reasonable amount. However, the output/input behavior is restrictive. The proposed CNN filters are designed as complex-coefficient filters which can improve the output SNR and process the 2D analytic signals of input images. The filter parameters are determined by applying a complex domain backpropagation algorithm. Through several simulations, it is shown that the proposed filters are robust and noise-tolerant for medical images.

关键词： Cellular neural networks Filters Biomedical imaging Image enhancement Neurons Signal design Image analysis Signal analysis Signal processing backpropagation algorithms

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An evolving neural network with the amorphous structure

An evolving neural network with the amorphous structure

引用

Congress on Evolutionary Computation

作者： Hoon Kang Sung-Hwan Jang School of Electrical & Electronics Engineering Chung-Ang University Seoul South Korea

ISBN: (纸本)0780366573

Evolutionary transparent cellular neural networks (ETCNNs) are designed and developed based on non-uniform cellular automata (CA). The phylogenetic level of the ETCNNs shows fractal numbers of synaptic layers in their topological morphology, and an extended backpropagation (BP) learning algorithm is induced through a transparent signal propagation mechanism at the epigenetic level. Therefore, an ETCNN deserves to be considered as a new paradigm of complex adaptive systems in artificial life. In simulations, ETCNNs have been applied to various function approximation problems with successful results for a relatively large number of training sets, and have shown convergent behaviors, both in the neural network structure and in the synaptic weights.

关键词： Neural networks Amorphous materials Cellular neural networks Phylogeny Fractals Morphology backpropagation algorithms Adaptive systems Function approximation Artificial neural networks

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Texture classification based on DCT and soft computing

Texture classification based on DCT and soft computing

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： G. Sorwar A. Abraham L.S. Dooley School of Computing and Information Technology Monash University Churchill VIC Australia

We present a classification method based on the discrete cosine transform (DCT) coefficients of texture image. Since the DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images with DCT, we used two popular soft computing techniques, namely neurocomputing and neuro-fuzzy computing. We used a feedforward neural network trained by backpropagation algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. We also analyzed the effects of prolonged training of neural networks. It was observed that the proposed neuro-fuzzy model performed better than neural network.

关键词： Discrete cosine transforms Neural networks backpropagation algorithms Fuzzy neural networks Image segmentation Image analysis Image texture analysis Information retrieval Image databases Spatial databases

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Relation between weight initialization of neural networks and pruning algorithms: case study on Mackey-Glass time series

Relation between weight initialization of neural networks an...

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International Joint Conference on Neural Networks (IJCNN)

作者： W. Wan K. Hirasawa J. Hu J. Murata Intelligent Control Laboratory Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (纸本)0780370449

The implementation of weight initialization is directly related to the convergence of learning algorithms. We made a case study on the Mackey-Glass time series problem in order to try to find some relations between weight initialization of neural networks and pruning algorithms. The pruning algorithm used in simulations is the Laplace regularizer method, that is, the backpropagation algorithm with Laplace regularizer added to the criterion function. Simulation results show that different kinds of initialization weight matrices display almost the same generalization ability when using the pruning algorithm, at least for the Mackey-Glass time series.

关键词： Neural networks Computer aided software engineering backpropagation algorithms Data mining Convergence Concrete Intelligent control Laboratories Information science Glass

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Forward perturbation algorithm for a general class of recurrent network

Forward perturbation algorithm for a general class of recurr...

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International Joint Conference on Neural Networks (IJCNN)

作者： O. De Jesus M.T. Hagan School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

This paper introduces a general class of dynamic network, the layered digital dynamic network. It then derives the forward perturbation algorithm for computing the gradient of the network error with respect to the wei... 详细信息

ISBN: (纸本)0780370449

关键词： Computer networks backpropagation algorithms Computer errors Neural networks Output feedback Neurofeedback Computer architecture Feedback loop Differential equations Delay lines

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Motor speed identification using multilayer feedforward neural networks

Motor speed identification using multilayer feedforward neur...

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International Conference on Power Electronics and Drive Systems (PEDS)

作者： V. Tipsuwanporn C. Tarasantisuk A. Numsumran W. Sawaengsinkasikit Faculty of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electrical Engineering Faculty of Engineering Kasem Bundit University Bangkok Thailand

This paper presents an artificial neural networks (ANNs) based high performance speed identifier for a DC motor control system. The purpose is to achieve accurate trajectory control of the speed, especially when motor and load parameters are unknown. The unknown nonlinear dynamics of the motor and the load are captured by multilayer feedforward neural network containing three layers. The activation function of the hidden layers are logsigmoid function and the training technique is use the backpropagation technique.

关键词： Multi-layer neural network Neural networks Feedforward neural networks DC motors Artificial neural networks Control systems Nonlinear dynamical systems Electronic mail backpropagation algorithms Shafts

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