Optimization of ANN Applied to Non-Linear System Identification

O. Ludwig, Jr. (Brazil), P.C. Gonzalez (Spain), and A.C. de C. Lima (Brazil)


Genetic Algorithm, Back Propagation, hybrid optimization, arbitrary norms, ANN, system identification.


In this work is we propose a hybrid technique for artificial neural network optimization. This technique is a combination of Real-Coded Genetic (GA) and Back Propagation (BP) algorithms. The main idea is to apply the Back Propagation over the five more adapted individuals of each generation produced by the Real Coded GA, in order to allow the evolution of these individuals before the crossover operation. The combined algorithms are used in non-linear system identification. Some solutions are presented to solve common problems arisen from application of Genetic Algorithm. To illustrate the use of the technique we present an example of non-linear model identification.

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