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基于大脑情感学习模型和自适应遗传算法的混沌时间序列预测

梅英 谭冠政 刘振焘 武鹤

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基于大脑情感学习模型和自适应遗传算法的混沌时间序列预测

梅英, 谭冠政, 刘振焘, 武鹤

Chaotic time series prediction based on brain emotional learning model and self-adaptive genetic algorithm

Mei Ying, Tan Guan-Zheng, Liu Zhen-Tao, Wu He
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  • 针对传统神经网络预测精度不高、收敛速度慢的问题,提出一种基于大脑情感学习模型和自适应遗传算法的混沌时间序列预测方法.大脑情感学习模型模拟了哺乳动物大脑中杏仁体和眶额皮质之间的情感学习机制,具有计算复杂度低、运算速度快的特点,因此可以大大提高混沌预测的快速性.为了进一步提高大脑情感学习模型的预测精度,采用自适应遗传算法优化其参数,将待优化的权值与阈值分布在染色体基因序列上,用适应度函数选出最佳参数,从而增强了模型的逼近能力.基于Lorenz混沌时间序列和实际地磁Dst指数序列的预测结果表明,本文方法较其他传统方法在预测精度、运算速度和稳定性上均具有明显优势.
    Chaos phenomenon is one of the most important physical phenomena, which has significant effects on one's production and life. Therefore, it is indispensable to find out the regularity of chaotic time series from a chaotic system for weather forecasting, space missions, alarm systems, etc. Although various models and learning algorithms have been developed to predict chaotic time series, many traditional methods suffer drawbacks of high computational complexity, slow convergence speed, and low prediction accuracy, due to extremely complex dynamic characteristics of chaotic systems. In this paper, a brain-inspired prediction model, i.e., brain emotional learning (BEL) model combined with self-adaptive genetic algorithm (AGA) is proposed. The establishment of BEL model is inspired by the neurobiology research, which has been put forward by mimicking the high-speed emotional learning mechanism between amygdala and orbitofrontal cortex in mammalian brain, it has advantages of lowcomputational complexity and fast learning. The BEL model employs reward-based reinforcement learning to adjust the weights of amygdala and orbitofrontal cortex. However, the reward-based method is modelsensitive and hard to generalize to other issues. To improve the performance of BEL model, AGA-BEL is proposed for chaotic prediction, in which the AGA is employed for parameter optimization. Firstly, weights and biases of orbitofrontal cortex and amygdala in BEL model are distributed to chromosomal gene sequence for optimization. Secondly, fitness function is employed to adjust the weights of amygdale and orbitofrontal cortex by self-adaptive crossover and mutation operations Therefore, the parameter optimization problem is transformed into a function optimization problem in the search space. Finally, the best chromosome that represents the best combination of weights and biases for BEL model is chosen, which is used for chaotic prediction. Prediction experiments on the benchmark Lorenz chaotic time series and a real-world chaotic time series of geomagnetic activity Dst index are performed. The experimental results and numerical analysis show that the proposed AGA-BEL prediction model achieves lower mean absolute deviation, mean square error, mean absolute percentage error, and higher correlation coefficient than the original BEL, levenberg marquardt-back propagation (LM-BP) and multilayer perceptron-back propagation (MLP-BP). Meanwhile, the BEL-based models take less computational time than the traditional BP-based models. Therefore, the proposed AGA-BEL model possesses the advantages of fast learning and low computational complexity of BEL model as well as the global optimum solution of AGA. It is superior to other traditional methods in terms of prediction precision, execution speed, and stability, and it is suited for online prediction in fast-varying environments.
      通信作者: 谭冠政, 63641214@qq.com
    • 基金项目: 国家自然科学基金(批准号:61403422,61703156)、湖南省教育厅科研基金(批准号:17C1084)和湖南文理学院重点科研项目(批准号:17ZD02)资助的课题.
      Corresponding author: Tan Guan-Zheng, 63641214@qq.com
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 61403422, 61703156), the Hunan Education Department Science Research Foundation, China (Grant No. 17C1084), and the Science Research Project of Hunan University of Arts and Science, China (Grant No. 17ZD02).
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    Singh S, Gill J 2014 Int. J. Intell. Syst. Appl. 6 55

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    Tian Z D, Gao X W, Shi T 2014 Acta Phys. Sin. 63 160508 (in Chinese)[田中大, 高宪文, 石彤. 2014 物理学报 63 160508]

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    Li D, Han M, Wang J 2012 IEEE Trans. Neural Netw. Learn. Syst. 23 787

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    Shi Z W, Han M 2007 IEEE Trans. Neural Netw. 18 359

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    Wang X Y, Han M 2015 Acta Phys. Sin. 64 070504 (in Chinese)[王新迎, 韩敏 2015 物理学报 64 070504]

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    Han M, Xi J H, Xu S G 2004 IEEE Trans. Signal Process. 52 3409

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    Su L Y 2010 Comput. Math. Appl. 59 737

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    Su L Y, Li C L 2015 Discrete. Dyn. Nat. Soc. 2015 329487

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    Tian Z D, Li S J, Wang Y H, Gao X W 2015 Acta Phys. Sin. 64 030506 (in Chinese)[田中大, 李树江, 王艳红, 高宪文 2015 物理学报 64 030506]

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    Balkenius C, Morn J 2001 Cybern. Syst. 32 611

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    Babaie T, Karimizandi L 2008 Soft. Comput. 12 857

    [15]

    Abdi J, Moshiri B, Abdulhai B 2012 Eng. Appl. Arti. Intell. 25 1022

    [16]

    Sharafi Y, Setayeshi S, Falahiazar A 2015 J. Math. Comput. Sci. 14 42

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    Milad H S A, Farooq U, Hawary M, Asad M U 2016 IEEE Access 23 569

    [18]

    Dorrah H T 2011 J. Adv. Res. 2 73

    [19]

    Mei Y, Tan G Z, Liu Z T 2017 Algorithms 10 70

    [20]

    Lotfi E, Akbarzadeh T M R 2016 Inf. Sci. 3 369

    [21]

    Srinivas M, Patnaik L M 2002 IEEE Trans. Syst. M. Cyber. 24 656

    [22]

    Guo Y Y 2013 M. S. Thesis (Harbin:Harbin Institute of Technology) (in Chinese)[郭圆圆 2013 硕士学位论文 (哈尔滨:哈尔滨工业大学)]

    [23]

    Takens F 1981 Lecture Notes in Mathematics (Berlin:Springer-Verlag) p366

    [24]

    Yang F 2012 Ph. D. Dissertation (Beijing:Beijing University of Posts and Telecommunications) (in Chinese)[杨飞 2012 博士学位论文(北京:北京邮电大学)]

    [25]

    Lotfi E, Akbarzadeh T 2013 Cybernet. Syst. 44 402

    [26]

    Amani J, Moeini R 2012 Sci. Iran. 19 242

    [27]

    Li J, Feng J, Wang W 2016 Sci. Geog. Sin. 36 780

    [28]

    Peng Y X, L J Y, Gu S J 2016 Chin. J. Space Sci. 36 866 (in Chinese)[彭宇翔, 吕建永, 顾赛菊 2016 空间科学学报 36 866]

  • [1]

    Lotfi E, Akbarzadeh T M R 2014 Neurocomputing 126 188

    [2]

    Singh S, Gill J 2014 Int. J. Intell. Syst. Appl. 6 55

    [3]

    Tian Z D, Gao X W, Shi T 2014 Acta Phys. Sin. 63 160508 (in Chinese)[田中大, 高宪文, 石彤. 2014 物理学报 63 160508]

    [4]

    Li D, Han M, Wang J 2012 IEEE Trans. Neural Netw. Learn. Syst. 23 787

    [5]

    Shi Z W, Han M 2007 IEEE Trans. Neural Netw. 18 359

    [6]

    Wang X Y, Han M 2015 Acta Phys. Sin. 64 070504 (in Chinese)[王新迎, 韩敏 2015 物理学报 64 070504]

    [7]

    Han M, Xi J H, Xu S G 2004 IEEE Trans. Signal Process. 52 3409

    [8]

    Su L Y 2010 Comput. Math. Appl. 59 737

    [9]

    Su L Y, Li C L 2015 Discrete. Dyn. Nat. Soc. 2015 329487

    [10]

    Tian Z D, Li S J, Wang Y H, Gao X W 2015 Acta Phys. Sin. 64 030506 (in Chinese)[田中大, 李树江, 王艳红, 高宪文 2015 物理学报 64 030506]

    [11]

    Mayer J D, Roberts R D, Barsade S G 2008 Annu. Rev. Psych. 59 507

    [12]

    Ledoux J E 1991 Concepts Neurosci. 2 169

    [13]

    Balkenius C, Morn J 2001 Cybern. Syst. 32 611

    [14]

    Babaie T, Karimizandi L 2008 Soft. Comput. 12 857

    [15]

    Abdi J, Moshiri B, Abdulhai B 2012 Eng. Appl. Arti. Intell. 25 1022

    [16]

    Sharafi Y, Setayeshi S, Falahiazar A 2015 J. Math. Comput. Sci. 14 42

    [17]

    Milad H S A, Farooq U, Hawary M, Asad M U 2016 IEEE Access 23 569

    [18]

    Dorrah H T 2011 J. Adv. Res. 2 73

    [19]

    Mei Y, Tan G Z, Liu Z T 2017 Algorithms 10 70

    [20]

    Lotfi E, Akbarzadeh T M R 2016 Inf. Sci. 3 369

    [21]

    Srinivas M, Patnaik L M 2002 IEEE Trans. Syst. M. Cyber. 24 656

    [22]

    Guo Y Y 2013 M. S. Thesis (Harbin:Harbin Institute of Technology) (in Chinese)[郭圆圆 2013 硕士学位论文 (哈尔滨:哈尔滨工业大学)]

    [23]

    Takens F 1981 Lecture Notes in Mathematics (Berlin:Springer-Verlag) p366

    [24]

    Yang F 2012 Ph. D. Dissertation (Beijing:Beijing University of Posts and Telecommunications) (in Chinese)[杨飞 2012 博士学位论文(北京:北京邮电大学)]

    [25]

    Lotfi E, Akbarzadeh T 2013 Cybernet. Syst. 44 402

    [26]

    Amani J, Moeini R 2012 Sci. Iran. 19 242

    [27]

    Li J, Feng J, Wang W 2016 Sci. Geog. Sin. 36 780

    [28]

    Peng Y X, L J Y, Gu S J 2016 Chin. J. Space Sci. 36 866 (in Chinese)[彭宇翔, 吕建永, 顾赛菊 2016 空间科学学报 36 866]

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出版历程
  • 收稿日期:  2017-09-22
  • 修回日期:  2018-02-08
  • 刊出日期:  2019-04-20

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