A Neuroevolutionary Approach for System Identification

Through System Identification techniques, it is possible to obtain a mathematical model for a dynamic system from its input/output data. Due to their intrinsic dynamic behavior and simple and fast training procedure, the use of echo state networks (ESNs), a kind of neural network, for System Identification is advantageous. However, ESNs have global parameters that should be tuned in order to improve their performance in a determined task. Besides, a random reservoir may not be ideal in terms of performance. Due to their theoretical ability to obtain good solutions with few evaluations, the Real Coded Quantum-Inspired Evolutionary Algorithm (QIEA-R) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) represent efficient alternatives of evolutionary algorithms for optimizing ESN global parameters and weights. Thus, this work proposes a neuro-evolutionary method that automatically defines an ESN for System Identification problems. The method initially focuses on finding the best ESN global parameters by using the QIEA-R or the CMA-ES then, in sequence, selecting its best reservoir, which can be done by a second optimization focused on some reservoir weights or by doing a simple choice based on networks with random reservoirs. The method was applied to seven benchmark problems in System Identification produced good results when compared to traditional methods.