Genetic Dynamic Fuzzy Neural Network (GDFNN) for nonlinear system identification

Autor:	Meng Joo Er, Lianyin Zhai, Amin J. Torabi, Lin San, Imam Arifin, Xiang Li, Mahardhika Pratama, J. O. Richard
Rok vydání:	2011
Předmět:	Adaptive neuro fuzzy inference system Nonlinear system identification Neuro-fuzzy Artificial neural network Computer science business.industry Genetic algorithm Artificial Intelligence & Image Processing Artificial intelligence Machine learning computer.software_genre business computer
Zdroj:	Advances in Neural Networks – ISNN 2011 ISBN: 9783642210891 ISNN (2)
Popis:	This paper discusses an optimization of Dynamic Fuzzy Neural Network (DFNN) for nonlinear system identification. DFNN has 10 parameters which are proved sensitive to the performance of that algorithm. In case of not suitable parameters, the result gives undesirable of the DFNN. In the other hand, each of problems has different characteristics such that the different values of DFNN parameters are necessary. To solve that problem is not able to be approached with trial and error, or experiences of the experts. Therefore, more scientific solution has to be proposed thus DFNN is more user friendly, Genetic Algorithm overcomes that problems. Nonlinear system identification is a common testing of Fuzzy Neural Network to verify whether FNN might achieve the requirement or not. The Experiments show that Genetic Dynamic Fuzzy Neural Network Genetic (GDFNN) exhibits the best result which is compared with other methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f2214fa854242685e2cf77879d79b89 https://hdl.handle.net/10453/29301 Zobrazit plný text záznamu