Journal of Central South University

, Volume 25, Issue 5, pp 1084–1098 | Cite as

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

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Abstract

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.

Key words

prediction classification brain emotional learning genetic algorithm 

基于改进大脑情感学习算法的有效数据分类

摘要

提出了采用遗传算法优化大脑情感学习模型的方法。大脑情感学习(BEL)模型是一种计算模型, 由Morén 等人于2000 年根据神经生理学上的发现提出。该模型根据大脑中杏仁体和眶额皮质之间的 情感学习机制建立,不完全地模拟了情感刺激在大脑反射通路中的信息处理过程。大脑情感学习模型 具有结构简单、计算复杂度低、运算速度快的特点。为了进一步提高模型的精度,采用遗传算法优化 调整大脑情感学习模型的权值,构造具有强泛化能力的大脑情感学习数据分析模型,并用于数据预测 与数据分类两方面。在数据预测方面,采用典型的磁暴环电流指数Dst 时间序列作为测试数据。实验 结果表明,从均方差MSE 和线性相关性R 指标来看,GA-BEL 算法的误差小、相关度高,说明该算法用 于预测的有效性。在分类方面,采用8 个典型的UCI 数据集和一个典型的头部磁共振图像数据集(fMRI) 作为测试数据。分类实验结果表明,GA-BEL 算法的分类正确率高,运算速度快于传统算法,说明该算 法用于分类的有效性。

关键词

预测 分类 大脑情感学习 遗传算法 

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Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.Electrical and Information Engineering CollegeHunan University of Arts and ScienceChangdeChina

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