A sequential sampling algorithm or adaptive sampling algorithm is a sampling algorithm that obtains instances sequentially one by one and determines from these instances whether it has already seen enough number of in...
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A sequential sampling algorithm or adaptive sampling algorithm is a sampling algorithm that obtains instances sequentially one by one and determines from these instances whether it has already seen enough number of instances for achieving a given task. In this paper, we present two typical sequential sampling algorithms. By using simple estimation problems for our example, we explain when and how to use such sampling algorithms for designing adaptive learning algorithms. (c) 2005 Elsevier B.V. All rights reserved.
A sequential sampling algorithm or adaptive sampling algorithm is a sampling algorithm that obtains instances sequentially one by one and determines from these instances whether it has already seen enough number of in...
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ISBN:
(纸本)9783540412373
A sequential sampling algorithm or adaptive sampling algorithm is a sampling algorithm that obtains instances sequentially one by one and determines from these instances whether it has already seen enough number of instances for achieving a given task. In this paper, we present two typical sequential sampling algorithms. By using simple estimation problems for our example, we explain when and how to use such sampling algorithms for designing adaptive learning algorithms. (c) 2005 Elsevier B.V. All rights reserved.
A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rule...
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A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the Jacobian becomes widely independent of the eigenvalues of the covariance matrix. A number of coupled learning rule systems for principal component analysis, two of them new, is derived by applying Newton's method to an information criterion. The relations to other systems of this class, the adaptive learning algorithm (ALA), the robust recursive least squares algorithm (RRLSA), and a rule with explicit renormalization of the weight vector length, are established.
A RBF neural network based predictive control of active power filter is presented in this paper. RBF neural network is employed to predict future harmonic compensating current. In order to make the predictive model mu...
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ISBN:
(纸本)0780385608
A RBF neural network based predictive control of active power filter is presented in this paper. RBF neural network is employed to predict future harmonic compensating current. In order to make the predictive model much simpler and tighter, an adaptive learning algorithm for RBF network is proposed. Based on the model output, branch-and-bound optimization method is adopted to produce proper value of control vector. This control vector is adequately modulated by means of a space vector PWM modulator which generates proper gating patterns of the inverter switches to maintain tracking of reference current. The RBF neural network based predictive algorithm is used in internal model control scheme to compensate for process disturbances, measurement noise and modeling errors. Experiment on an actual system is implemented. The results show the RBF neural network based predictive control eliminates supply current and voltage harmonics greatly and is more effective than PI control.
In this article toe propose the adaptive learning algorithm, of neural network with respect to a rapid temperature change of forecasted day. The proposed adaptive learning algorithm is used to shift the learning range...
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In this article toe propose the adaptive learning algorithm, of neural network with respect to a rapid temperature change of forecasted day. The proposed adaptive learning algorithm is used to shift the learning range of previous year of forecasted day. Therefore, the proposed neural network can be trained by using learning data, including the maximum temperature to be forecasted. The suitability of the proposed approach is illustrated through an application to actual load data of Okinawa Electric Power Company in Japan.
Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learningalgorithm, especially when it is used in a hybrid-type...
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Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learningalgorithm, especially when it is used in a hybrid-type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learningalgorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid-type controller is proposed. The main features of the proposed learningalgorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learningalgorithm is a robust in stabilizing the control system. Also, this proposed learningalgorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learningalgorithm experimentally.
In this paper, adaptive learning algorithms to obtain better generalization performance are proposed. We specifically designed cost terms for the additional functionality based on the first- and second-order derivativ...
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In this paper, adaptive learning algorithms to obtain better generalization performance are proposed. We specifically designed cost terms for the additional functionality based on the first- and second-order derivatives of neural activation at hidden layers. In the course of training, these additional cost functions penalize the input-to-output mapping sensitivity and high-frequency components in training data. A gradient-descent method results in hybrid learning rules to combine the error back-propagation, Hebbian rules, and the simple weight decay rules. However, additional computational requirements to the standard error back-propagation algorithm are almost negligible. Theoretical justifications and simulation results are given to verify the effectiveness of the proposed learningalgorithms. (C) 2000 Elsevier Science B.V. All rights reserved.
An artificial neural network with an adaptive-Kalman-filter-based learningalgorithm is presented for forecasting weather-sensitive loads. The proposed model can differentiate between weekday and weekend loads, This n...
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An artificial neural network with an adaptive-Kalman-filter-based learningalgorithm is presented for forecasting weather-sensitive loads. The proposed model can differentiate between weekday and weekend loads, This neural-network model has been implemented using real load data, The results reveal the efficiency and accuracy of the proposed approach in terms of short learning time, rapid convergence and the adaptive nature of the learningalgorithm.
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