Open Access Open Access  Restricted Access Subscription or Fee Access

Training Echo State Neural Network Using Harmony Search Algorithm

Javad Saadat, Payman Moallem, Hamidreza Koofigar

Abstract


Echo State Networks (ESN) are a special form of recurrent neural networks (RNNs), which allow for the black box modeling of nonlinear dynamical systems. A unique feature of an ESN is that a large number of neurons (the “reservoir”), whose synaptic connections are generated randomly, is used in such that only the connections from the reservoir to the output modified by learning. The computation of optimal weights can then be achieved by a simple linear regression in an offline manner. ESNs have been applied to a variety of tasks from time series prediction to dynamic pattern recognition with great success. In many tasks, however, an online adaptive learning of the output weights is required. Harmony Search (HS) algorithm shows good performance when the search space is large. Here we propose HS algorithm for training echo state network in an online manner. In our simulation experiments, the ESNs are trained for predicting of three different time series including Mackey-Glass, Lorenz chaotic and Rossler chaotic time series with four different algorithms including Recursive Least Squares (RLS-ESN), Particle Swarm Optimization (PSO-ESN), and our proposed methods (HS-ESN and HS-RLS-ESN). Simulation results show that HS-ESN is significantly the fastest algorithm for training ESN whereas can effectively meet the requirements of the output precision. HS-RLS-ESN algorithm firstly uses HS to close to solution region then it uses RLS to obtain less error. HS-RLS-ESN is slower than HS-ESN and faster than RLS-ESN, but its generality power is very close to RLS-ESN.

Full Text: PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.