Abstract—This study proposes a symbiotic particle swarm
optimization (SPSO) for the specific neural fuzzy controller
(NFC). The specific NFC model using compensatory fuzzy
operators of neural fuzzy networks makes fuzzy logic systems
more adaptive and effective. The proposed SPSO adopts a
multiple swarm scheme that uses each particle represents a
single fuzzy rule and each particle in each swarm evolves
separately to avoid falling in a local optimal solution.
Furthermore, the SPSO embeds the symbiotic evolution scheme
in a specific particle swarm optimization (PSO) to accelerate the
search and increase global search capacity.
Index Terms—Water bath temperature system, neural fuzzy networks, symbiotic evolution, particle swarm optimization.
C. H. Chen and W. H. Chen are with the Department of Electrical Engineering, National Formosa University, Yunlin County, Taiwan, ROC (e-mail: email@example.com).
Cite: Cheng-Hung Chen and Wen-Hsien Chen, "Symbiotic Particle Swarm Optimization for Neural Fuzzy Controllers," International Journal of Machine Learning and Computing vol. 4, no. 5, pp. 433-436, 2014.