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检索条件"主题词=Backpropagation Algorithms"

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Adaptive equalization using complex-valued multilayered neural network based on the extended Kalman filter

Adaptive equalization using complex-valued multilayered neur...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Ren-Cheng Huang Mu-Song Chen Department of Electrical Engineering Da-Yeh University Changhua Taiwan

In this paper, a complex-valued neural network based on the Kalman filter is presented for channel equalization in a communication system. The complex-valued decoupled extended Kalman filter algorithm is derived which provides a faster convergence rate and better performance than those of complex back-propagation algorithms. Computer simulation results showed that the proposed scheme has a faster convergence behavior and smaller signal constellation than the gradient descent based learning algorithm via the linear and nonlinear channel equalization problem.

关键词： Adaptive equalizers Neural networks Multi-layer neural network Kalman filters Finite impulse response filter Nonlinear filters Adaptive algorithm Least squares approximation backpropagation algorithms Neurons

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Upper bounds on RBFN designed by input clustering

Upper bounds on RBFN designed by input clustering

引用

American Control Conference (ACC)

作者： Z. Uykan H.N. Koivo Control Engineering Laboratory Helsinki University of Technology Finland

In the design of radial basis function networks (RBFNs), several heuristic hybrid learning methods, including a clustering algorithm for locating the centers and a linear least-squares method for the linear weights, have been previously suggested. These hybrid methods can be put into two groups depending on whether the output vector is also involved in the clustering process, which is called the input clustering (IC) and input-output clustering (IOC). A recent paper by Uykan et al. (1998) presents a clustering-based upper bound on the RBFN output error designed by IOC. The main contribution of this paper is to obtain a similar upper bound for the IC case which explicitly depends on the RBFN parameters. This paper also presents some different upper bounds for l/sub 1/ and l/sub 2/ norms used for the quantisation error and the output error in both IC and IOC cases.

关键词： Clustering algorithms backpropagation algorithms Radial basis function networks Upper bound Neurons Algorithm design and analysis Vectors Quantization Iterative algorithms Control engineering

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Generalization improvement of a fuzzy classifier with pyramidal membership functions

Generalization improvement of a fuzzy classifier with pyrami...

引用

International Conference on Pattern Recognition

作者： S. Abe Graduate School of Science and Technology Kobe University Kobe Japan

ISBN: (纸本)0769507506

In this paper we discuss a fuzzy classifier with pyramidal membership functions and discuss a performance improvement by changing linear membership functions to quadratic membership functions. First we define the classifier with quadratic membership functions and then discuss the training method that maximises the recognition rate by counting the net increase in the recognition rate by changing the slope and the center of each membership function. Finally, we demonstrate the recognition improvement by quadratic membership functions for the iris and blood cell data sets.

关键词： Input variables Electronic mail Iris Blood Cells (biology) backpropagation algorithms Vectors

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Estimation of reduction in CO/sub 2/ emissions to shift commuters' travel mode from private cars to public transportation

Estimation of reduction in CO/sub 2/ emissions to shift comm...

引用

World Congress on Intelligent Control and Automation (WCICA)

作者： Bing Li H. Tamura Graduate School of Engineering Science Osaka University Toyonaka Osaka Japan

We develop a model for forecasting the amount of CO/sub 2/ emission due to urban commute travel. The model consists of three sub-models: a sub-model for estimating the number of commuters, a fuzzy mode choice sub-model and a sub-model for estimating the amount of CO/sub 2/ emission. Using this model, we analyze and forecast the effect of policies to shift the commuters' travel mode from private cars to public transportation for decreasing the amount of CO/sub 2/ emission.

关键词： Predictive models Neurons Road transportation Automobiles backpropagation algorithms Neural networks Air pollution Traffic control Logistics

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An event-driven and ontology-based approach for the delivery and information extraction of e-mails

An event-driven and ontology-based approach for the delivery...

引用

International Symposium on Multimedia Software Engineering

作者： Heng-Hsou Chang Yau-Hwang Ko Jang-Pong Hsu Institute of Computer Science and Information and Information National Cheng Kung University Tainan Taiwan National Cheng Kung University Tainan TW Department of Finance Tainan Woman''s College of Arts and Technology Taiwan

ISBN: (纸本)0769509339

In the field of information extraction (IE), the extraction of information from documents is usually event-oriented. Therefore, many information extraction machines have built their domain knowledge based on events. However, information extraction is often limited in its application in specific domains, because the events are simply detected by predefined keywords. We propose event detection driven intelligent information extraction by using the neural network paradigm. In this paper, the backpropagation (BP) learning algorithm is adopted to train the event detector. In order to detect the potential events in documents effectively, we apply natural language processing technology to aid the selection of nouns as feature words. Unrelated nouns are filtered by the analysis based on document frequency distribution. Finally, selected nouns are conceptualized into concepts. These concepts are supposed to characterize documents appropriately and they are stored in ontology as a knowledge base. In the experimental results, we achieved high accuracy both in the inside testing and outside testing of Internet documents. By means of the well-trained event detector, the information extraction task can be certainly applied in wider domains. Eventually, this event detection technology is introduced for the delivery and information extraction of e-mail.

关键词： Ontologies Data mining Event detection Detectors Testing Intelligent networks Learning systems Neural networks backpropagation algorithms Natural language processing

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Robust visual recognition with high-order Gaussian synapses networks

Robust visual recognition with high-order Gaussian synapses ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： J.L. Crespo J. Santos R.J. Duro Departmento Ingeniería Industrial Universidade da Coruna Spain Departmento Computació Universidade da Coruna Spain

In the context of visual systems for robots, we have made use of a high order gaussian synapses network and the Gaussian Synapses backpropagation Algorithm (GSBP) for the implementation of the detectors that constitute one part of the whole visual architecture. These detectors are trained to be sensitive to spatial patterns that are relevant for the decisions the robot must perform during its operation in an environment. The inclusion of gaussian functions in the synapses of the network allows the network to select the appropriate spatial information and filter out all that is irrelevant according to the training it has received. In this paper we will show how these networks are easily trained to ignore backgrounds. In addition, with a very simple training set and an appropriate input selection strategy, the networks detect objects independently of size and position. These systems, coupled with an attention mechanism result in a very efficient visual information processor.

关键词： Robustness Neurons Robot sensing systems Detectors Computer networks Visual system Service robots backpropagation algorithms Computer architecture Information filtering

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A new method to prune the neural network

A new method to prune the neural network

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Weishui Wan K. Hirasawa Jinglu Hu Chunzhi Jin Intelligent Control Laboratory Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

Using the backpropagation algorithm (BP) to train neural networks is a widely adopted practice in both theory and practical applications. However, its distributed weight representation, that is the weight matrix of final network after training by using BP are usually not sparsified, and prohibits its use in the rule discovery of inherent functional relations between the input and output data, so in this aspect some kinds of structure optimization are needed to improve its poor performance. In this paper, with this in mind, a new method to prune neural networks is proposed based on some statistical quantities of neural networks. Comparing with the other known pruning methods such as the structural learning with forgetting and RPROP algorithm, the proposed method can attain comparable or even better results over these methods without evident increase of the computational load. Detailed simulations using the Iris data sets exhibit our above assertion.

关键词： Neural networks backpropagation algorithms Iris Intelligent control Laboratories Information science Computational modeling Stochastic processes Data mining Convergence

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Multiple classes random neural network model and color pattern recognition problems

Multiple classes random neural network model and color patte...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： J. Aguilar V. Rossell CEMISID. Departmento de Computación Facultad de Ingeniería Universidad de los Andes Merida Venezuela

The purpose of the paper is to describe the use of the multiple classes random neural network model to learn patterns having different colors. We propose a learning algorithm for the recognition of color patterns based upon the nonlinear equations of the multiple classes random neural network model using gradient descent of a quadratic error function. In addition, we propose a progressive retrieval process with adaptive threshold value.

关键词： Neural networks Pattern recognition Neurons Recurrent neural networks Fires Nonlinear equations backpropagation algorithms Pixel Signal processing Probability

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Neural network ensemble for financial trend prediction

Neural network ensemble for financial trend prediction

引用

IEEE Region 10 International Conference TENCON

作者： M.H.L.B. Abdullah V. Ganapathy Multimedia University Cyberjaya Malaysia Faculty of Information Technology Multimedia University Cyberjaya Malaysia

Presents a study of an artificial neural network that works collectively as an ensemble for the task of predicting the future trends of a financial time series from a classification approach. The Kuala Lumpur Stock Exchange composite index is chosen as a case study. A multilayer perceptron neural network is trained by backpropagation algorithms to capture the relationship between the future trends and the past values of the index. The performance of the neural network ensemble is compared to the single neural network. A confidence measure using agreement (variance) within the ensemble and with the ensemble output is studied.

关键词： Neural networks Artificial neural networks Stock markets Testing Economic forecasting Information technology Multilayer perceptrons Multi-layer neural network backpropagation algorithms Finance

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Force learn algorithm-training a neural net with patterns which have highest errors

Force learn algorithm-training a neural net with patterns wh...

引用

Seminar on Neural Network Applications in Electrical Engineering

作者： V. Illic

Describes training methods of neural nets with the backpropagation learning algorithm in which priority is given to the patterns of the training set with the maximum error during the training.

ISBN: (纸本)0780355121

Describes training methods of neural nets with the backpropagation learning algorithm in which priority is given to the patterns of the training set with the maximum error during the training.

关键词： Neural networks Error correction Pattern recognition backpropagation algorithms Shape Intelligent networks

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