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Foundations of Generic Optimization
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 universaldoi:10.1007/978-1-4020-6668-9_4 fatcat:lragipoprbgqjbjafvgropq6uu