An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification

Abstract

Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN) is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL) algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA) is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI) datasets and two well-known databases (Japanese Female Facial Expression, Cohn-Kanade). The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.

Brain Science, Brain-inspired Artificial Intelligence, and Applications in Business and Management

“…The genetic algorithm (GA) was used for optimally tuning weights and biases of amygdala and orbitofrontal cortex in the BEL neural network to increase the accuracy of BEL in classification. The amygdale plays a key role in emotional learning and reacting; the orbitofrontal cortex helps the amygdala in processing emotional stimulus [16].…”

“…Machine learning, combinations of specific areas (psychology, brain functions, and technology), and technical indicators were used to present a complete picture of the tools associated with brain-inspired decision-making [21,23,24]. Japan has a consistent global technology research foundation and by looking at the fuzzy sets implementation in Japan, one can see they are far ahead of the competition on decision-making schemas [16,25]. Numerous international countries are participating in brain-inspired decision-making to improve life in their country [23,26].…”

“…The SIDA (Sense-Inference-Decide and Act) cycle [19] Because the neural trees and artificial intelligent risk management system (AIRMS) increased profitability on stocks, etc., it is necessary to research more complicated decision-making schematics [20]. Other researchers looked at specific parts of the brain such as the amygdala and psychology to develop a decision-making plan [16,21]. Future research is still necessary to present all the decision-making centers within the brain and all the factors which influence them [26].…”

Evolutionary-Based BEL Controller Applied to a Magneto-Rheological Structural System

“…In regard to this latter application context, [34] resorted to a genetic algorithm to find the weights and biases of both the amygdala and orbitofrontal cortex in order to improve a BEL-based neural network used to classify human face expressions.…”

Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

“…where V th is the weight and the corresponding update law is the same as Equation 10. Several techniques have been adopted for tuning the BELBIC parameters [26,[31][32][33][34][35]. In this paper, to significantly reduce the computational complexity, a heuristic approach is utilized for tuning the BELBIC parameters.…”