Design of the Nonlinear System Predictor Driven by the Bayesian-Gaussian Neural Network of Sliding Window Data | Zendy

Yijian Liu | Zendy; Yanjun Fang | Zendy

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Design of the Nonlinear System Predictor Driven by the Bayesian-Gaussian Neural Network of Sliding Window Data

Author(s) -

Yijian Liu,

Yanjun Fang

Publication year - 2009

Publication title -

computer and information science

Language(s) - English

Resource type - Journals

eISSN - 1913-8997

pISSN - 1913-8989

DOI - 10.5539/cis.v2n2p26

Subject(s) - computer science , nonlinear system , sliding window protocol , artificial neural network , gaussian , bayesian probability , identification (biology) , system identification , nonlinear system identification , artificial intelligence , data mining , window (computing) , pattern recognition (psychology) , machine learning , algorithm , control theory (sociology) , physics , botany , control (management) , quantum mechanics , biology , measure (data warehouse) , operating system

The model identification of the nonlinear system has been concerned by the industrial community all along. The relationship of the nonlinear dynamic system is contained in the data accumulated in the scene. To better utilize the data about the industrial objects, in this article, we put forward the nonlinear system predictor driven by the Bayesian-Gaussian neural network (NN) model, use the trained threshold matrix and sliding window data to realize the online output prediction for the nonlinear dynamic system. The simulation experiment indicates that the Bayesian-Gaussian NN based on the sliding window data can fulfill the demands of the online identification and prediction of the adaptive nonlinear system.

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