山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (1): 25-32.doi: 10.6040/j.issn.1672-3961.0.2022.383
• 机器学习与数据挖掘 • 上一篇
孙园1,2,曾惠权1,2*,欧阳苏建1,2,高佳倩1,2,王绮楠1,2,林智勇1,2
SUN Yuan1,2, ZENG Huiquan1,2*, OUYANG Sujian1,2, GAO Jiaqian1,2, WANG Qinan1,2, LIN Zhiyong1,2
摘要： 为提高神经网络模型在解决非线性系统辨识问题上的精度,提出一种基于粒子群算法的模糊大脑情感学习模型。该模型包含大脑情感学习网络,在利用系统历史数据对模型进行训练的基础上,通过适应度函数动态调整网络结构中的权重因子,提高网络学习效率和辨识精度。在对连续搅拌反应器系统辨识试验和对sin E强非线性对象逼近试验中,与常规模糊大脑情感学习模型、BP神经网络和RBF神经网络相比,本模型拥有更高的逼近能力和更快的收敛速度,解决了基于试错法导致的模型参数调整时间长、模型不稳定等问题,为辨识的实际应用提供了可行的模型选择。
中图分类号:
| [1] 伊泽曼·R, 明奇霍夫·M. 动态系统辨识:导论与应用[M].机械工业出版社, 2016. [2] 张剑, 林瑞昌, 毕天昊. 一种用于非线性动态辨识的新型神经网络[J/OL].控制工程.(2022-07-26)[2023-09-11].https://doi.org/10.14107/j.cnki.kzgc.20220102. [3] PAULO V D C S. Fuzzy neural networks and neuro-fuzzy networks: a review the main techniques and applications used in the literature[J]. Applied Soft Computing, 2020, 92: 106275. [4] GUO Xiaokai, YAN Xianguo. Optimization analysis of hydrog-en fuel cell hybrid power based on neural network and sensor networks[C] //2021 5th International Conference on Intelligent Computing and Control Systems(ICICCS). Madurai, India:IEEE, 2021: 172-175. [5] HE H, LIU Y, BA L. A nonlinear dynamic model of flywheel energy storage systems based on alternative concept of back propagation neural networks[J]. Journal of Computational and Nonlinear Dynamics, 2022, 17(9): 091006. [6] MARUGÁN A P, MÁRQUEZ F P G, PEREZ J M P, et al. A survey of artificial neural network in wind energy systems[J]. Applied Energy, 2018, 228: 1822-1836. [7] KARABACAK K, CETIN N. Artificial neural networks for controlling wind-PV power systems: a review[J]. Renewable and Sustainable Energy Reviews, 2014, 29: 804-827. [8] QIAO Junfei, HOU Ying, ZHANG Lu, et al. Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation[J]. Neurocomputing, 2018, 275: 383-393. [9] LEE H J, JHANG S S, YU W K, et al. Artificial neural network control of battery energy storage system to damp-out inter-area oscillations in power systems[J]. Energies, 2019, 12(17): 3372. [10] LIN C M, CHUNG C C. Fuzzy brain emotional learning control system design for nonlinear systems[J]. International Journal of Fuzzy Systems, 2015, 17(2): 117-128. [11] SUN Y, LIN C M. A fuzzy brain emotional learning classifier design and application in medical diagnosis[J]. Acta Polytechnica Hungarica, 2019, 16(4): 27-43. [12] BRAZ-CÉSAR M T, GONÇALVES J, COELHO J P, et al. Development of a brain emotional learning based controller for application to vibration control of a building structure under seismic excitation[C] //COMPDYN 2019 7th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering. Crete, Greece:ECCOMAS, 2019: 3473-3481. [13] MUTHUSAMY P K, GARRATT M, POTA H, et al. Real-time adaptive intelligent control system for quadcopter unmanned aerial vehicles with payload uncertainties[J]. IEEE Transactions on Industrial Electronics, 2021, 69(2): 1641-1653. [14] 魏民祥, 杨佳伟, 陈凯, 等. 基于改进大脑情感学习模型的车辆纵向跟随研究[J]. 吉林大学学报(工学版), 2022, 52(12): 2994-3005. WEI Minxiang, YANG Jiawei, CHEN Kai, et al. Research on vehicle longitudinal following based on improved brain emotional learning model[J]. Journal of Jilin University(Engineering Edition), 2022, 52(12): 2994-3005. [15] KENNED J, EBERHART R. Particle swarm optim-ization[C] //Proceedings of ICNN'95-international Conference on Neural Networks.Perth, Australia:IEEE, 1995, 4: 1942-1948. [16] LIN C M, LE T L, HUYNH T T. Self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control[J]. Neurocomputing, 2018, 275: 2239-2250. [17] LIGHTBODY G, IRWIN G W. Nonlinear control structures based on embedded neural system models[J]. IEEE Transactions on Neural Networks, 1997, 8(3): 553-567. [18] DE M B, DE G P, DE S B, et al. DAISY: a database for identification of systems[R]. K U LeuvenBE:Department of Electrical Engineering(ESAT), 1997. [19] QIAO J F, MENG X, LI W J. An incremental neuronal-activity-based RBF neural network for nonlinear system modeling[J]. Neurocomputing, 2018, 302: 1-11. |
| [1] | 程春蕊,毛北行. 一类非线性混沌系统的自适应滑模同步[J]. 山东大学学报 (工学版), 2020, 50(5): 1-6. |
| [2] | 朱晓强,钟麦英. 基于强跟踪H-/H∞优化的无人机系统故障检测[J]. 山东大学学报 (工学版), 2019, 49(1): 66-74. |
| [3] | 梁蒙蒙,周涛,夏勇,张飞飞,杨健. 基于PSO-ConvK卷积神经网络的肺部肿瘤图像识别[J]. 山东大学学报 (工学版), 2018, 48(5): 77-84. |
| [4] | 马汉杰,林霞,胥晓晖,张健,张智晟. 基于自适应粒子群算法的智能家居管理系统负荷优化模型[J]. 山东大学学报(工学版), 2017, 47(6): 57-62. |
| [5] | 赵煊,钟麦英,郭丁飞. 基于等价空间的无人机飞行控制系统故障检测[J]. 山东大学学报(工学版), 2017, 47(5): 150-156. |
| [6] | 陈杰,钟麦英,张利刚. 基于L2范数最小估计的无人机飞控系统故障检测[J]. 山东大学学报(工学版), 2017, 47(5): 89-95. |
| [7] | 李炜,王可宏,曹慧超. 基于新型ESF的一类非线性系统故障滤波方法[J]. 山东大学学报(工学版), 2017, 47(5): 7-14. |
| [8] | 易云飞,苗剑,林郭隆,殷智. 基于牛顿力学和博弈论模型的粒子网络优化算法[J]. 山东大学学报(工学版), 2017, 47(1): 28-36. |
| [9] | 王常顺,肖海荣. 基于自抗扰控制的水面无人艇路径跟踪控制器[J]. 山东大学学报(工学版), 2016, 46(4): 54-59. |
| [10] | 荆业飞1,张承慧1*,徐蓓蓓2，李珂1,褚晓广1. 基于阻抗匹配的小型风电系统功率输出优化方法[J]. 山东大学学报(工学版), 2013, 43(5): 39-43. |
| [11] | 王飞飞1,闫雪华2*,刘允刚3. 一类控制系数未知非线性参数化系统的 输出反馈实际跟踪控制[J]. 山东大学学报(工学版), 2013, 43(5): 55-67. |
| [12] | 张飞,耿红琴. 基于混沌粒子群算法的车间作业调度优化[J]. 山东大学学报(工学版), 2013, 43(3): 19-22. |
| [13] | 刘建华1,2, 黄添强2, 严晓明2. 融合PSO算法思想的进化算法[J]. 山东大学学报(工学版), 2010, 40(5): 34-40. |
| [14] | 尚芳 刘允刚 张承慧. 一类不确定非线性系统输出反馈扰动抑制[J]. 山东大学学报(工学版), 2010, 40(1): 19-27. |
| [15] | 张健 刘允刚. 一类不确定高阶随机非线性系统的自适应镇定[J]. 山东大学学报(工学版), 2009, 39(6): 35-47. |
|
||
