Based on analyzing the immune phenomena in nature and utilizing performances of the existent artificial neural network, a novel network structure, IMVFEWNN (immune multiple variant function estimation wavelet neural n...
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ISBN:
(纸本)0780374886
Based on analyzing the immune phenomena in nature and utilizing performances of the existent artificial neural network, a novel network structure, IMVFEWNN (immune multiple variant function estimation wavelet neural network), is proposed which integrates the immune mechanism and the structure of neural informationprocessing. The analysis in theory and the simulation test for a data mining problem show that, compared with the artificial neural network, IMVFEWNN is not only effective but also feasible.
For intelligent transportation systems, a new traffic flow time series prognostication is proposed in this paper. Compared with classical methods, support vector machine has a good generalize ability for limited train...
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For intelligent transportation systems, a new traffic flow time series prognostication is proposed in this paper. Compared with classical methods, support vector machine has a good generalize ability for limited training samples, which has a characteristic of rapid convergence and avoiding the local minimum. At the end of this paper, the simulation experiment for the traffic flow of one practice crossing proves the validity and efficiency and high application value in traffic flow prediction.
Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the propo...
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Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the proposed algorithm providing the capability of the fast convergence and high accuracy for extracting all the principal components. It is shown that all the information needed for PCA can be completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The convergence performance of the proposed algorithm is briefly *** relation between Oja’s rule and the least squares learning rule is also established. Finally, a simulation example is given to illustrate the effectiveness of this algorithm for PCA.
A robust nonlinear MPC was proposed for a class of nonlinear systems that can be modeled by a second-order Volterra series model with parametric uncertainty in the time domain, based on a simplified second-order Volte...
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A robust nonlinear MPC was proposed for a class of nonlinear systems that can be modeled by a second-order Volterra series model with parametric uncertainty in the time domain, based on a simplified second-order Volterra series model. Sufficient conditions were developed for the robust stability of the closed-loop system. Compared with MPC using the regular second-order Volterra series model, computation burden is highly decreased without the cost of much performance degradation. To prove the conclusion, a local form of small gain theorem for discrete-time Volterra series systems was proposed. A case study of a chemical reactor was presented to show the effectiveness of the controller proposed.
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