Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Evolving fuzzy rule based controllers using genetic algorithms
Fuzzy Sets and Systems
Fuzzy model identification: selected approaches
Fuzzy model identification: selected approaches
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Intelligent control of a stepping motor drive using an adaptive neuro-fuzzy inference system
Information Sciences—Informatics and Computer Science: An International Journal
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
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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.