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检索条件"主题词=Backpropagation Algorithms"

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A constrained optimization algorithm for training locally recurrent globally feedforward neural networks

A constrained optimization algorithm for training locally re...

引用

International Joint Conference on Neural Networks (IJCNN)

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

This paper presents a novel learning algorithm for training locally recurrent globally feedforward neural networks. The training task is formulated as a constrained optimization problem, whose objective is twofold: (i) minimization of an error measure, leading to successful approximation of the input/output mapping and (ii) optimization of an additional functional, which aims at accelerating the learning process. Simulation results on a benchmark identification problem demonstrate that, compared to other learning schemes, the proposed algorithm has enhanced qualities, including improved speed of convergence, accuracy and robustness.

关键词： Constraint optimization Neural networks Recurrent neural networks Feedforward neural networks Neurons Finite impulse response filter IIR filters Neurofeedback Output feedback backpropagation algorithms

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Locally recurrent neural networks optimal filtering algorithms: application to wind speed prediction using spatial correlation

Locally recurrent neural networks optimal filtering algorith...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： T.G. Barbounis J.B. Theocharis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

This paper focuses on a locally recurrent multilayer network with internal feedback paths, the IIR-MLP. The computation of the partial derivatives of the network's output with respect to its trainable weights is achieved using backpropagation through adjoints and a second order global recursive prediction error (GRPE) training algorithm is developed. Also, a local version of the GRPE is presented in order to cope with the increased computational burden of the global version. The efficiency of the proposed learning schemes, as compared to conventional gradient based methods, is tested on the wind prediction problem from 15 min to 3 h ahead on a site, using spatial correlation and facilitating measurements from nearby sites up to 40 km away.

关键词： Recurrent neural networks Filtering algorithms Wind speed Neurons Computer networks Neurofeedback backpropagation algorithms Application software Neural networks Finite impulse response filter

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Streamflow forecasting using neural networks and fuzzy clustering techniques

Streamflow forecasting using neural networks and fuzzy clust...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： I. Luna S. Soares M.H. Magalhaes R. Ballini DENSIS-FEEC-UNICAMP Campinas Brazil

Planning of hydroelectric systems is a complex and difficult task once it involves non-linear production characteristics and depends on numerous variables. A key variable is the streamflow. Streamflow values covering the entire planning period must be accurately forecasted because they strongly influence energy production. This paper suggests an application of a FIR neural network and a fuzzy clustering-based model to evaluate one-step and multi-step ahead predictions. Results are compared to the ones obtained by a periodic autoregressive model (PAR). It is interesting to apply a recurrent neural network for prediction task due to its ability for temporal processing and efficiency to solve nonlinear problems. The results show a generally better performance of the FIR neural network for the case studied.

关键词： Neural networks Fuzzy neural networks Predictive models Finite impulse response filter Recurrent neural networks Load forecasting Production planning Water resources Clustering algorithms backpropagation algorithms

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A neural network with localized receptive fields for visual pattern classification

A neural network with localized receptive fields for visual ...

引用

International Symposium on Signal Processing and Its Applications (ISSPA)

作者： Son Lam Phung A. Bouzerdoum School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong NSW Australia

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An improvement to the natural gradient learning algorithm for multilayer perceptrons

An improvement to the natural gradient learning algorithm fo...

引用

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

作者： M.R. Bastian J.H. Gunther T.K. Moon Department of Electrical and Computer Engineering Utah State University Logan UT USA

Natural gradient learning has been shown to avoid singularities in the parameter space of multilayer perceptrons. However, it requires a large number of additional parameters beyond ordinary backpropagation. The article describes a new approach to natural gradient learning in which the number of parameters necessary is much smaller than in the natural gradient algorithm. This new method exploits the algebraic structure of the parameter space to reduce the space and time complexity of the algorithm and improve its performance.

关键词： Multilayer perceptrons Random variables backpropagation algorithms Tensile stress Jacobian matrices Moon Computer networks Noise robustness

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Feature subset selection via multi-objective genetic algorithm

Feature subset selection via multi-objective genetic algorit...

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International Joint Conference on Neural Networks (IJCNN)

作者： H.C. Lac D.A. Stacey Computing & Information Science University of Guelph Guelph ONT Canada

Real-world datasets tend to be complex, large in size, and may contain many irrelevant features. Eliminating such irrelevant features can significantly improve the performance of a data mining algorithm. In this paper, we propose a multi-objective genetic algorithm that finds a set of Pareto-optimal feature subsets that works as a wrapper around a standard back-propagation algorithm. We also introduce a novel mechanism called the least-crowded selection algorithm that maximizes the diversity of the solutions returned by the algorithm. We justify the proposed method by theoretically and empirically comparing it to the backpropagation neural network and the simple genetic algorithm for feature selection.

关键词： Genetic algorithms Data mining backpropagation algorithms Error analysis Information science Neural networks Minimization methods Computer vision Noise reduction Filters

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Steepest descent as message passing

Steepest descent as message passing

引用

IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： J. Dauwels S. Korl H.-A. Loeliger Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

It is shown how steepest descent (or steepest ascent) may be viewed as a message passing algorithm with "local" message update rules. For example, the well-known backpropagation algorithm for the training of feedforward neural networks may be viewed as message passing on a factor graph. The factor graph approach with its emphasis on "local" computations makes it easy to combine steepest descent with other message passing algorithms such as the sum/max-product algorithms, expectation maximization, Kalman filtering/smoothing, and particle filters. As an example, parameter estimation in a state space model is considered. For this example, it is shown how steepest descent can be used for the maximization step in expectation maximization.

关键词： Message passing backpropagation algorithms Filtering algorithms Neural networks Feedforward neural networks Kalman filters Smoothing methods Particle filters Parameter estimation State-space methods

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Nonlinear time series prediction using wavelet networks with Kalman filter based algorithm

Nonlinear time series prediction using wavelet networks with...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Xuegin Zhao Jinaming Lu W.P.A. Putranto T. Yahagi Graduate School of Science and Technology Chiba University Chiba Japan

The idea of combining both wavelets and neural networks has resulted in the formulation of wavelet networks, whose basic functions are drawn from family of orthonormal wavelets. The usual method to train wavelet networks is the backpropagation algorithm described by Rumelhart et al. However, this algorithm converges slowly for large or complex problems such as speech recognition, where more than thousands of iterations may be needed for convergence, even with small data sets. In this paper, we propose to train wavelet network for nonlinear time series prediction using the unscented Kalman filter (UKF), which needs less iterations than backpropagation algorithm. UKF is a powerful nonlinear estimation technique and has been shown to be a superior alternative to the extended Kalman filter (EKF) in a variety of applications, including parameter estimation for time series modeling and neural network training. Several simulation results are presented to validate the proposed method

关键词： backpropagation algorithms Neural networks Nonlinear systems Artificial neural networks Electronic mail Speech recognition Convergence Parameter estimation Predictive models Time series analysis

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Dynamic neural network-based estimator for fault diagnosis in reaction wheel actuator of satellite attitude control system

Dynamic neural network-based estimator for fault diagnosis i...

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International Joint Conference on Neural Networks (IJCNN)

作者： E.S. Tehrani K. Khorasani S. Tafazoli Concordia University Montreal QC CA

This paper presents an approach to simultaneous fault detection and isolation in the reaction wheel actuator of the satellite attitude control system. A model-based adaptive nonlinear parameter estimation technique is used based on a highly accurate reaction wheel dynamical model while each parameter is an indication of a specific type of fault in the system. The estimation is based on the nonlinear finite-memory filtering strategy that is solved for optimal estimation functions. To make the optimization feasible for on-line application, the optimal estimation functions are approximated by MLP neural networks thus reducing the functional optimization problem to a nonlinear programming problem, namely, the optimization of the neural weights. The well-known standard back-propagation algorithm and backpropagation through-time algorithm were employed inside the neural adaptation algorithms to obtain the required gradients. Simulation results show the effectiveness of the methodology for the proposed application.

关键词： Neural networks Fault diagnosis Wheels Actuators Satellites backpropagation algorithms Nonlinear dynamical systems Fault detection Adaptive systems Parameter estimation

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Modular configuration Rprop/SF-ANN for microwave waveguide on EBG structures modeling

Modular configuration Rprop/SF-ANN for microwave waveguide o...

引用

Microwave, MTT-S International Symposium

作者： E.N.R. Queiroz Fernandes P.H. da Fonseca Silva M.A. Barbosa de Melo A.G. d'Assuncao UERN Mossoro Rio Grande do Norte Brazil CEFET-PB Joao Pessoa Paraiba Brazil UFCG Campina Grande Paraiba Brazil UFRN-CT-DEE Natal Rio Grande do Norte Brazil

This work describes a new class of modular network that has been used to analyze microwave waveguides on electromagnetic bandgap (EBG) structures. The proposed modular configuration employs three or more SF-ANN (Sample Function Artificial Neural Network) trained by the Resilient backpropagation (RProp) algorithm. Two types of waveguides are modeled by this new modular neurotechnique that are the rectangular waveguide and the parallel plate waveguide with EBG structures. The rectangular waveguide uses the UC-EBG structure, which consists of a metal pad and four connecting branches. This device is employed in the development of high-performance and compact circuit components for microwave and millimeter-wave frequencies. The parallel plate waveguide is formed by the microstrip ground plane and a metal plate with EBG elements on top that works like one metal shield. This structure has the capability of suppressing the propagation of unwanted parallel plate modes. Simulations through the proposed neural network models for microwave waveguides on EBG structures gave results in excellent agreement with measured results available in the literature. Furthermore, this technique is able to generalize for new values of input parameters and frequency ranges, validating it as a good estimator into the mapping regions where no previous numerical or experimental information is available.

关键词： Periodic structures Metamaterials Electromagnetic waveguides Rectangular waveguides Frequency estimation Electromagnetic analysis Artificial neural networks backpropagation algorithms Joining processes Microwave circuits

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