Competitive Brain Emotional Learning

Brain emotional learning (BEL) methods are a recently developed class of emotional brain-inspired algorithms, that enjoy feed-forward computational complexity on the order of O(n). BEL methods suffer from a major drawback related to the non-linear problem solving ability, i.e. they cannot solve n-bit parity problems in which n≥3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {n} \ge 3$$\end{document}. The present paper proposes a competitive BEL (C-BEL) capable of accommodating a higher number of bits in the parity problem. The proposed C-BEL is inspired by the competitive property of neucortex’s neurocircuits. The method is tested on n-bit parity, function approximation and a pattern recognition problem. Various comparisons with the reinforcement BEL (R-BEL), supervised BEL (S-BEL), evolutionary BEL (E-BEL), a Boltzmann machine and a convolutional neural network indicate the superiority of the approach in terms of its higher ability in non-linear problem solving, function approximation and pattern recognition.