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潘立登

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期刊论文

RBF Neural Networks And Its Application In Establishing Nonlinear Self-tuning Model

潘立登， Pan Lideng ， Huang Xiaofeng ， Ma Junying ， Pan Yuying

，-0001，（）：

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摘要/描述

The principle and algorithm of neural networks using radial basis function (RBF) are discussed in this paper. The recursive least squares method is used to resolve the self-tuning problem of RBF neural network so that self-tuning models of nonlinear time-varying system is obtained. By using RBF neural networks, a self-tuning model of a reactor is established and compared with a BP neural networks model and a regression model. The results show that the RBF neural networks model is effective.

关键词: Neural network, ， Radial Basis Function Networks, ， Nonlinear Models, ， Self-tuning, ， Time-varying System, ， Identification, ， On-line

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