Enhancement of Artificial Emotional Neural Network Using JAYA Algorithm and the Investigation of Expanded Feature Selected for Wind Power Forecasting

Abstract

The Brain Emotional Learning (BEL) is a novel bio-inspired machine learning approach mentioned as a new class of artificial neural network (ANN). The artificial emotional neural network (AENN) is one of the BEL methods which used the genetic algorithm (GA) to compute proper weights, weights of the amygdala (AMYG), orbitofrontal cortex (OFC) weights and a bias value. AENN trained by GA has been reported that it could produce low error rates. However, AENN still has more rooms to enhance its prediction of performance, especially generalization and the prediction of performance. Therefore, this paper aims to propose a new training method for AENN. The JAYA optimization algorithm optimized the weights and the biases of AENN. Two new proposed models are named as AENN-Max-JAYA and AENN-Mean-JAYA. Their names are according to the way of selecting the additional expanded feature which obtained through either the max or the average of input patterns, respectively. From the experimental results for wind power forecasting dataset, the proposed methods proved that the results are better in generalization performance and give lower error rates which compared to the comparative AENN models and traditional ANNs.