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An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification

School of Information Science and Engineering, Central South University, Changsha 410083, China
School of Electrical and Information Engineering, Hunan University of Arts and Science, Changde 415000, China
School of Automation, China University of Geosciences, Wuhan 430074, China
Author to whom correspondence should be addressed.
Academic Editor: Stefano Cagnoni
Algorithms 2017, 10(2), 70;
Received: 14 March 2017 / Revised: 5 June 2017 / Accepted: 9 June 2017 / Published: 14 June 2017
PDF [2840 KB, uploaded 24 November 2017]


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. View Full-Text
Keywords: classification; brain emotional learning; reward; genetic algorithm; fitness classification; brain emotional learning; reward; genetic algorithm; fitness

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Mei, Y.; Tan, G.; Liu, Z. An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification. Algorithms 2017, 10, 70.

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