Simulated annealing algorithm-based Elman network for dynamic system identification

Abstract

One of the well-known recurrent neural networks is the Elman network. Recently, it has been used in applications of system identification. The network has feedforward and feedback connections. It can be trained essentially as a feedforward network by means of the basic backpropagation algorithm, but its feedback connections have to be kept constant. For training success, it is important to select the correct values for the feedback connections. However, finding these values manually can be a lengthy trial-and-error process. This paper investigates the use of the simulated annealing (SA) algorithm to obtain the weight values of both the feedforward and feedback connections of Elman networks used for dynamic system identification. The SA algorithm is an efficient random search procedure, which can simultaneously obtain the optimal weight values of both the feedforward and feedback connections.