By João Guilherme Sauer (auth.), Nadia Nedjah, Leandro dos Santos Coelho, Viviana Cocco Mariani, Luiza de Macedo Mourelle (eds.)

The layout of latest engineering platforms includes the dignity of a great trade-off among the various ambitions specifications to be happy alongside the approach lifestyles reminiscent of excessive reliability, low redundancy and occasional operational expenditures. those features are usually in clash with each other, therefore a compromise resolution should be sought. cutting edge computing thoughts, resembling genetic algorithms, swarm intelligence, differential evolution, multi-objective evolutionary optimization, simply to identify few, are of serious assist in founding potent and trustworthy resolution for lots of engineering difficulties. every one bankruptcy of this ebook makes an attempt to utilizing an leading edge computing strategy to elegantly resolve a unique engineering problem.

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Algorithm 1 is describes the PSO optimization process. A given maximal iteration number and the predeﬁned ﬁtness values can be used as a stop criterion. Algorithm 1. Particle swarm-based optimization algorithm (PSO) 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: for i := 1 until total particulas do Initialize particle i information; Initialize random position of particle i; Initialize random velocity of particle i; end for repeat for i := 1 until total particulas do Calculate ﬁtness of particle i; if (ﬁtness better than pBesti ) then Update pBesti with the new position; end if if (ﬁtness better than gBest) then Update gBest with the new position; end if Update velocity of particle i; Update position of particle i; end for until (stopcriterion = true) The main characteristic of the PSO algorithm is the social interaction [6], which makes the individuals able to learn with the group and use the acquired knowledge.

51 in C3)] (4) PSO in Building Fuzzy Systems 43 Note that each data set generates one single rule. Considering a real system, it is very possible that these rules can be conﬂicting rules. To overcome this problem, one can associate degrees of conﬁdence to each generated rule, using the degree of relevance of each rule term. Equation 5 shows how this degree can be computed: C(Rule) = μ(x1 ) × μ(x2 ) × μ(y), (5) wherein C is degree of Rule and μ(x1), μ(x2) and μ(y) are the degree of relevance of each rule term.

A life cycle cost approach was adopted to evaluate the ﬁnancial beneﬁt using condition monitoring system, a tool for implementing CBM policy [10]. In [11] a multi-state Markov decision mechanism was used to estimate the wind turbine degradation process based on which the optimal maintenance scheme is devised. Tian et al. [8] developed a CBM method for wind turbine systems, based on the health condition prediction information obtained from ANN prediction models. ANN methods have been used to investigate various problems in wind turbine systems.