Title :
An immune symbiotic evolution learning for compensatory neural fuzzy networks and its applications
Author :
Chen, Cheng-Hung ; Lin, Cheng-Jian ; Lin, Chin-Teng
Author_Institution :
Dept. of Electr. Eng., Nat. Formosa Univ., Yunlin, Taiwan
Abstract :
This study presents an efficient immune symbiotic evolution learning algorithm for the compensatory neural fuzzy network (CNFN). The proposed immune symbiotic evolution learning method (ISEL) includes three major components initial population, subgroup symbiotic evolution and immune system algorithm. The advantage of the proposed ISEL method are that the subgroup symbiotic evolution method uses the subgroup based population to evaluate the fuzzy rules locally and the adopted immune system algorithm can accelerate the search and increase global search capacity. Finally, the simulation results have shown that the proposed CNFN-ISEL can outperform other methods.
Keywords :
evolutionary computation; fuzzy neural nets; learning (artificial intelligence); search problems; CNFN-ISEL method; compensatory neural fuzzy network; fuzzy rule; global search capacity; immune symbiotic evolution learning algorithm; immune system algorithm; initial population; subgroup symbiotic evolution; Algorithm design and analysis; Encoding; Entropy; Fuzzy systems; Immune system; Neural networks; Symbiosis; Compensatory fuzzy operator; control problems; immune system algorithm; neural fuzzy network; symbiotic evolution;
Conference_Titel :
Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7315-1
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2011.6007323