Abstract
In this article, the brain emotional learning-based pattern recognizer (BELPR) is proposed to solve multiple input–multiple output classification and chaotic time series prediction problems. BELPR is based on an extended computational model of the human brain limbic system that consists of an emotional stimuli processor. The BELPR is model free and learns the patterns in a supervised manner and evaluates the output(s) using the activation function tansig. In the numerical studies, various comparisons are made between BELPR and a multilayer perceptron (MLP) with a back-propagation learning algorithm. The methods are tested to classify 12 UCI (University of California, Irvine) machine learning data sets and to predict activity indices of the Earth's magnetosphere. The main features of BELPR are higher accuracy, decreased time and spatial complexity, and faster training.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
