Quantum-Inspired Optimization of Echo State Networks Applied to System Identification

Quantum-Inspired Evolutionary Algorithms (QIEA) represent an efficient alternative to the traditional genetic algorithms, being capable of finding good solutions with smaller populations. Echo State Networks (ESNs) are a simple and efficient implementation of the Reservoir Computing framework. The use of this kind of networks in system identification is advantageous due to its intrinsic dynamic behavior and fast training procedure. However, ESNs have global parameters that should be tuned in order to improve their performance in a determined task. Besides, the random generation of the reservoir weights of these networks may not be ideal in terms of performance. Thus, this work presents a method that automatically defines an ESN for system identification problems by using a real coded QIEA (QIEA-R) in a two-phase optimization procedure. The QIEA-R firstly searches for the best global parameters of an ESN; then, on a second stage, optimizes some of its reservoir weights. In two benchmark problems for system identification, the proposed method overcame the performance of a randomly generated ESN with the same global parameters and has presented comparable and, in most cases, better accuracy results in comparison to some methods which were applied to the same datasets.