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An improved brain emotional learning algorithm for accurate and efficient data analysis

Journal of Central South University 2018年第5期25卷 1084-1098页

作者：梅英谭冠政 School of Information Science and Engineering Central South University Electrical and Information Engineering College Hunan University of Arts and Science

To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introduced. BEL mimics the emotional learning mechanism in brain which has the superior features of fast learning and quick reacting. To further improve the performance of BEL in data analysis, genetic algorithm （GA） is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in BEL neural network. The integrated algorithm named GA-BEL combines the advantages of the fast learning of BEL, and the global optimum solution of GA. GA-BEL has been tested on a real-world chaotic time series of geomagnetic activity index for prediction, eight benchmark datasets of university California at Irvine （UCI） and a functional magnetic resonance imaging （fMRI） dataset for classifications. The comparisons of experimental results have shown that the proposed GA-BEL algorithm is more accurate than the original BEL in prediction, and more effective when dealing with large-scale classification problems. Further, it outperforms most other traditional algorithms in terms of accuracy and execution speed in both prediction and classification applications.

关键词： prediction classification brain emotional learning genetic algorithm

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Real-Time Facial Expression Recognition Based on the Improved brain emotional learning Model

Real-Time Facial Expression Recognition Based on the Improve...

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第37届中国控制会议

作者： Ying Mei Xin Yang Zhentao Liu He Wu Junyu Guo Information and Electric Engineering College Hunan University of Arts and Science College of Information Engineering Xiangtan University School of Automation China University of Geosciences NO.1 High School Changde

In this paper, a brain-inspired method is proposed for real-time facial expression recognition. The brain emotional learning（BEL） model mimics the high speed of the emotional learning mechanism in brain, it has the superior feature of fast learning and it can avoid slow convergence in traditional networks. To improve the performance of BEL model, particle swarm optimization（PSO） is applied to optimize the weights of BEL model. The integrated classifier named as PSO-BEL is tested on the well-known Cohn-Kanade database, JAFFE database, the average recognition rates reach 97.71% and 96.77%, respectively. Moreover, a real-world interactive interface is established. The comparisons of experiments with the traditional methods indicate the superiority of the proposed PSO-BEL in terms of recognition accuracy and computing time. It demonstrates that the proposed PSO-BEL can successfully meet the requirement of real-time facial expressions recognition.

关键词： Real-Time Facial Expression Recognition brain emotional learning Particle Swarm Optimization

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