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ASME Press Select Proceedings

International Conference on Mechanical and Electrical Technology, 3rd, (ICMET-China 2011), Volumes 1–3

By
Yi Xie
Yi Xie
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ISBN:
9780791859810
No. of Pages:
906
Publisher:
ASME Press
Publication date:
2011

A nonlinear multi-step predictive control strategy using Radial Basis Function Neural Network (RBFNN) as multi-step predictive model for nonlinear complicated industrialized process with time delay, slow time variety and highly disturbance is proposed in the paper. The modified elitist preserved genetic algorithm is used to obtain the online nonlinear optimization. Simulation results demonstrate that the strategy has good robustness and the resisting time variety ability.

Abstract
Key Words
1 Introduction
2. The Prediction of Nonlinear System Model Based on RBF Networks
3 Rolling Optimization Based on Genetic Algorithm
4. Simulation Research
5. Conclusion
References
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