Abstract:In control engineering, it is well known that many physical processes exhibit a chaotic component. In point of fact, it is also assumed that conventional modeling procedures disregard it, as stochastic noise, beside nonlinear universal approximators (like neural networks, fuzzy rule-based or genetic programming-based models,) can capture the chaotic nature of the process.In this chapter we will show that this is not always true. Despite the nonlinear capabilities of the universal approximators, these methods o… Show more
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