Optimal control of nonlinear system on the time series using RBF network

Author

Fukuoka, Yoshitaka ; Arakawa, Mototaka

Author_Institution

Eng., Kagawa Univ., Kagawa, Japan

fYear

2013

fDate

4-7 Aug. 2013

Firstpage

1429

Lastpage

1434

Abstract

In this paper, we will propose optimal control algorithm that using Radial Basis Function Networks (RBFN) as a type of the Neural Network. Approximation of using RBFN, which is good at nonlinear system, multimodal problem, and optimize locally-detail and globally-rough at once. But, when we use the RBFN at optimal control, the response speed will be important point. Then, we propose Experimental Learning Algorithm that set the basis optimal number and its point to be learning quickly. The basic rule of this algorithm was shown in this paper.

Keywords

learning (artificial intelligence); learning systems; neurocontrollers; nonlinear control systems; optimal control; optimisation; radial basis function networks; time series; RBF network; RBFN; experimental learning algorithm; globally-rough optimization; locally-detail optimization; multimodal problem; neural network; nonlinear system; optimal control algorithm; radial basis function networks; response speed; time series; Optimal control; Optimization; Prediction algorithms; Predictive control; Radial basis function networks; Real-time systems; Rockets; Experimental Learning Algorithm; Radial Basis Function Networks; optimal control;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Automation (ICMA), 2013 IEEE International Conference on

Conference_Location

Takamatsu

Print_ISBN

978-1-4673-5557-5

Type

conf

DOI

10.1109/ICMA.2013.6618123

Filename

6618123