A Modified Brain Emotional Learning Model Inspired By Online Recurrent Memory Sequential Extreme Learning Machine Based On Neural Networks

Document Type : Computer Article

Authors

1 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Systems and Control Engineering, K.N. Toosi University of Technology, Tehran, Iran

Abstract

Predicting data, in the form of complex and chaotic time series, is one of the fundamental issues in various scientific and industrial fields. Data-driven models such as artificial neural networks and fuzzy neural networks compared to other models have been received more attention due to their special features. To develop and improve these models, the concepts of the mammalian brain limbic system are used. Therefore, the brain emotional learning machine is introduced. In this paper, the online sequential extreme learning machine is used as the main component in the processing centers of the brain emotional learning machine. To interact between processing centers, the online sequential extreme learning machine is designed in the form of a recurrent memory network with transfer learning ability. The proposed model is named the brain emotional learning based on recurrent memory online extreme learning machine (BEL-ORMS-ELM). To evaluate and compare the efficiency of the proposed model, the initial parameters of the models are adjusted according to the Mackey-Glass and Lorenz time series data under the same conditions. Different models are evaluated and compared based on the valid measurable criteria in regression problems prediction. The simulation results show that the proposed model with sigmoid and hyperbolic tangent activation function for Mackey-Glass and Lorenz time series test data has the highest performance criteria compared to similar online models. It also has acceptable performance for training data compared to similar models.

Keywords

Main Subjects

References

[1] راضیه محمدی و فرشید کی نیا، " ارائه یک روش جدید انتخاب ورودی بر اساس دسته‌بندی نیمه نظارتی برای تخمین سری های زمانی"، نشریه مدل سازی در مهندسی، دوره 17، شماره 59، سال 1398، صفحه 153-163.
[2] احسان برنجکار، "ارزیابی عملکرد شبکه‌های عصبی مصنوعی تلفیق شده با الگوریتم های فراابتکاری وال و مورچگان در تخمین نرخ نفوذ حفاری و مقایسه با شبکه های عصبی ساده و مدل های ریاضی مرسوم"، نشریه مدل سازی در مهندسی، دوره 19، شماره 65، سال 1400، صفحه 115-135.
[3] محمد مهدی ذبیحی شش پلی، مهدی علیاری شوره دلی و علی معرفیان پور، "تحلیل پایداری لیاپانوف در آموزش سیستم فازی- عصبی نوع 2 با یک الگوریتم ترکیبی مبتنی بر گرادیان نزولی و فیلتر کالمن"، نشریه مدل سازی در مهندسی، دوره 19، شماره 67، سال 1400، صفحه.
[4] M. Parsapoor, "Brain emotional learning-based prediction model for long-term chaotic prediction applications", arXiv preprint arXiv:1605.01681, 2016,
[5] M. Parsapoor and U. Bilstrup, "Brain emotional learning based fuzzy inference system (belfis) for solar activity forecasting", 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, Vol. 1, 2012, pp. 532-539.
[6] J. Morén and C. Balkenius, "A computational model of emotional learning in the amygdala", From animals to animats, Vol. 6, 2000, pp. 115-124.
[7] C. B. MorÉn, Jan, "Emotional learning: A computational model of the amygdala", Cybernetics & Systems, Vol. 32, No. 6, 2001, pp. 611-636.
[8] J. E. LeDoux, "Emotion circuits in the brain", Annual review of neuroscience, Vol. 23, No. 1, 2000, pp. 155-184.
[9] E. Lotfi and M.-R. Akbarzadeh-T, "Supervised brain emotional learning", The 2012 International Joint Conference on Neural Networks (IJCNN) 2012, pp. 1-6.
[10] E. Lotfi and M.-R. Akbarzadeh-T, "Adaptive brain emotional decayed learning for online prediction of geomagnetic activity indices", Neurocomputing, Vol. 126, 2014, pp. 188-196.
[11] E. Lotfi and M.-R. Akbarzadeh-T, "A winner-take-all approach to emotional neural networks with universal approximation property", Information Sciences, Vol. 346, 2016, pp. 369-388.
[12] Z. Farhoudi, S. Setayeshi, and A. Rabiee, "Using learning automata in brain emotional learning for speech emotion recognition", International Journal of Speech Technology, Vol. 20, No. 3, 2017, pp. 553-562.
[13] Z.-T. Liu, Q. Xie, M. Wu, W.-H. Cao, Y. Mei, and J.-W. Mao, "Speech emotion recognition based on an improved brain emotion learning model", Neurocomputing, Vol. 309, 2018, pp. 145-156.
[14] E. Lotfi, O. Khazaei, and F. Khazaei, "Competitive brain emotional learning", Neural Processing Letters, Vol. 47, No. 2, 2018, pp. 745-764.
[15] C.-M. Lin, R. Ramarao, and S. H. Gopalai, "Self-organizing adaptive fuzzy brain emotional learning control for nonlinear systems", International Journal of Fuzzy Systems, Vol. 21, No. 7, 2019, pp. 1989-2007.
[16] N. A. Stillings, C. H. Chase, and M. H. Feinstein, Cognitive science: An introduction: MIT press, 1995.
[17] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: Theory and applications", Neurocomputing, Vol. 70, No. 1-3, 2006, pp. 489-501.
[18] N.-Y. Liang, G.-B. Huang, P. Saratchandran, and N. Sundararajan, "A fast and accurate online sequential learning algorithm for feedforward networks", IEEE Transactions on Neural Networks, Vol. 17, No. 6, 2006, pp. 1411-1423.
[19] L. Parhizkari, A. Najafi, and M. Golshan, "Medium term electricity price forecasting using extreme learning machine", Journal of Energy Management and Technology, Vol. 4, No. 2, 2020, pp. 20-27.
[20] H.-J. Rong, G.-B. Huang, N. Sundararajan, and P. Saratchandran, "Online sequential fuzzy extreme learning machine for function approximation and classification problems", IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 39, No. 4, 2009, pp. 1067-1072.
[21] J. Tang, C. Deng, and G.-B. Huang, "Extreme learning machine for multilayer perceptron", IEEE transactions on neural networks and learning systems, Vol. 27, No. 4, 2016, pp. 809-821.
[22] X. Jia, R. Wang, J. Liu, and D. M. Powers, "A semi-supervised online sequential extreme learning machine method", Neurocomputing, Vol. 174, 2016, pp. 168-178.
[23] W. Zong, G.-B. Huang, and Y. Chen, "Weighted extreme learning machine for imbalance learning", Neurocomputing, Vol. 101, 2013, pp. 229-242.
[24] L. Feng, S. Xu, F. Wang, S. Liu, and H. Qiao, "Rough extreme learning machine: A new classification method based on uncertainty measure", Neurocomputing, Vol. 325, 2019, pp. 269-282.
[25] L. Liu, Q. Zhang, D. Wei, G. Li, H. Wu, Z. Wang, et al., "Chaotic ensemble of online recurrent extreme learning machine for temperature prediction of control moment gyroscopes", Sensors, Vol. 20, No. 17, 2020, p. 4786.
[26] Y. Park and H. S. Yang, "Convolutional neural network based on an extreme learning machine for image classification", Neurocomputing, Vol. 339, 2019, pp. 66-76.
[27] C. Lucas, D. Shahmirzadi, and N. Sheikholeslami, "Introducing belbic: Brain emotional learning based intelligent controller", Intelligent Automation & Soft Computing, Vol. 10, No. 1, 2004, pp. 11-21.
[28] مهدی گلشن، محمد تشنه لب و آرش شریفی، "ماشین یادگیر هیجانی مغز مبتنی بر ماشین یادگیر محدود فازی"، کنگره مشترک سیستم های فازی و هوشمند ایران، بجنورد، ایران، 9 تا 11 بهمن ، دوره 7، سال 1397.
[29] مهدی گلشن، محمد تشنه لب و آرش شریفی، "بهبود ماشین یادگیر هیجانی مغز با الهام از ماشین یادگیر محدود فازی آنلاین حافظه‌دار بازگشی مبتنی بر سیستم عصبی فازی تاکاگی‌سوگنو"، مجله سیستم های فازی و کاربردها، دوره 4، شماره 1، سال 1400، صفحه 47-78.
[30] E. Lotfi and M.-R. Akbarzadeh-T, "Practical emotional neural networks", Neural Networks, Vol. 59, 2014, pp. 61-72.
[31] H. S. Milad, U. Farooq, M. E. El-Hawary, and M. U. Asad, "Neo-fuzzy integrated adaptive decayed brain emotional learning network for online time series prediction", IEEE Access, Vol. 5, 2017, pp. 1037-1049.
[32] S. H. Fakhrmoosavy, S. Setayeshi, and A. Sharifi, "A modified brain emotional learning model for earthquake magnitude and fear prediction", Engineering with Computers, Vol. 34, No. 2, 2018, pp. 261-276.
[33] S. H. Fakhrmoosavy, S. Setayeshi, and A. Sharifi, "An intelligent method for generating artificial earthquake records based on hybrid pso–parallel brain emotional learning inspired model", Engineering with Computers, Vol. 34, No. 3, 2018, pp. 449-463.
[34] S. Motamed, S. Setayeshi, and A. Rabiee, "Speech emotion recognition based on a modified brain emotional learning model", Biologically inspired cognitive architectures, Vol. 19, 2017, pp. 32-38.
[35] S. Motamed, S. Setayeshi, and A. Rabiee, "Speech emotion recognition based on brain and mind emotional learning model", Journal of integrative neuroscience, Vol. 17, No. 3-4, 2018, pp. 577-591
[36] C.-M. Lin and C.-C. Chung, "Fuzzy brain emotional learning control system design for nonlinear systems", International Journal of Fuzzy Systems, Vol. 17, No. 2, 2015, pp. 117-128.
[37] Q. Zhou, F. Chao, and C.-M. Lin, "A functional-link-based fuzzy brain emotional learning network for breast tumor classification and chaotic system synchronization", International Journal of Fuzzy Systems, Vol. 20, No. 2, 2018, pp. 349-365.
[38] C. M. Lin, H. B. Nguyen, and T. T. Huynh, "A new self-organizing double function-link brain emotional learning controller for mimo nonlinear systems using sliding surface", IEEE Access, Vol. 9, 2021, pp. 73826-73842.
[39] J. Zhao, Z. Zhong, C.-M. Lin, and H.-K. Lam, "H∞ tracking control for nonlinear multivariable systems using wavelet-type tsk fuzzy brain emotional learning with particle swarm optimization", Journal of the Franklin Institute, Vol. 358, No. 1, 2021, pp. 650-673.
[40] Q. Lin, Z. Xu, and C.-M. Lin, "Battery-supercapacitor state-of-health estimation for hybrid energy storage system using a fuzzy brain emotional learning neural network", International Journal of Fuzzy Systems, 2021,
[41] M. Affan and R. Uddin, "Brain emotional learning and adaptive model predictive controller for induction motor drive: A new cascaded vector control topology", International Journal of Control, Automation and Systems, Vol. 19, No. 9, 2021, pp. 3122-3135.
Journal of Modeling in Engineering
Volume 20, Issue 70
September 2022
Pages 1-21

Files

History

  • Receive Date: 12 December 2021
  • Revise Date: 10 March 2022
  • Accept Date: 04 April 2022

Share

How to cite

Statistics