Particle Swarm Optimization (PSO), which is a robust stochastic evolutionary computation engine, belongs to the broad category of swarm intelligence (SI) techniques. SI paradigm has been inspired by the social behavior of ants, bees, wasps, birds, fishes and other biological creatures and is emerging as an innovative and powerful computational metaphor for solving complex problems in design, optimization, control, management, business and finance. SI may be defined as any attempt to design distributed problem-solving algorithms that emerges from the social interaction. The objective of this chapter is to present the use of PSO algorithm for building optimal fuzzy models from the available data. The fuzzy model identification procedure using PSO as an optimization engine has been implemented as a Matlab toolbox and is also presented in this chapter. For the purpose of illustration and validation of the approach, the data from the rapid Nickel-Cadmium (Ni-Cd) battery charger developed by the authors has been used.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 email@example.com
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 firstname.lastname@example.org