Summary
In this chapter, we are proposing an approach for integrating evolutionary computation applied to the problem of system identification in the well-known statistical signal processing theory. Here, some mathematical expressions are developed in order to justify the learning rule in the adaptive process when a Breeder Genetic Algorithm is used as the optimization technique. In this work, we are including an analysis of errors, energy measures, and stability.
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Montiel, O., Castillo, O., Melin, P., Sepulveda, R. (2005). The Evolutionary Learning Rule in System Identification. In: Wu, X., Jain, L., Graña, M., Duro, R.J., d’Anjou, A., Wang, P.P. (eds) Information Processing with Evolutionary Algorithms. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-117-2_14
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DOI: https://doi.org/10.1007/1-84628-117-2_14
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