Echo state network optimization using hybrid-structure based gravitational search algorithm with square quadratic programming for time series prediction

Abstract EN

The Echo-State Network (ESN) is a robust recurrent neural network and a generalized form of classical neural networks in time-series model designs.

ESN inherits a simple approach for training and demonstrates the high computational capability to solve non-linear problems.

However, input weights and the reservoir's internal weights are pre-defined when optimizing with only the output weight matrix.

This paper proposes a Hybrid Gravitational Search Algorithm (HGSA) to compute ESN output weights.

In Gravitational Search Algorithm (GSA), Square Quadratic Programming (SQP) is united as a local search strategy to raise the standard GSA algorithm's efficiency.

Later, an HGSA-SQP and the validation data set to establish the relation configuration of the ESN output weights.

Experimental results indicate that the proposed configuration of HGSA-SQP-ESN is more efficient than the other conventional models of ESN with the minimum generalization error.