DocumentCode :
2372218
Title :
A study of multi-objects hybrid heuristic searching approach for dynamic system modeling
Author :
Xia, Dingchun ; Qin, Xiaozhen
Author_Institution :
Dept. of Math. & Conputer Sci., Wuhan Textile Univ., Wuhan, China
fYear :
2012
fDate :
23-25 March 2012
Firstpage :
508
Lastpage :
511
Abstract :
A hybrid heuristic searching approach for dynamic system modeling is presented. The paper suggests that the model consists of two function parts - GAs and heuristic random searching algorithm (HRSA). GA is one of the adaptive search algorithms which are able to find global solutions or regions in optimal problem. This character is helpful for reducing the searching range in many optimal problems. Based on this foundation, the solutions within these separate regions will be located further by HRSA. Heuristic information is used to form the next possible searching directions in virtue of the gradient concepts. It reduces the computing time of modeling and speed up the identification of the nonlinear dynamic system. Sereral functions are used to test. The results and analysis are discussed. It shows the ability of model in the dynamic system modeling with the features of simplicity and flexibility.
Keywords :
genetic algorithms; heuristic programming; modelling; nonlinear dynamical systems; random processes; search problems; GA; adaptive search algorithms; dynamic system modeling; gradient concepts; heuristic information; heuristic random searching algorithm; multiobject hybrid heuristic searching approach; nonlinear dynamic system; Chemical engineering; Computational modeling; Heuristic algorithms; Neural networks; Sensors; Strips;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Technology (ICIST), 2012 International Conference on
Conference_Location :
Hubei
Print_ISBN :
978-1-4577-0343-0
Type :
conf
DOI :
10.1109/ICIST.2012.6221699
Filename :
6221699
Link To Document :
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