Simulation Research on Predictive Wavelet Neural Network Intelligent Control System

doi:10.16182/j.issn1004731x.joss.201810022

Journal of System Simulation ›› 2018, Vol. 30 ›› Issue (10): 3770-3780.doi: 10.16182/j.issn1004731x.joss.201810022

Previous Articles Next Articles

Simulation Research on Predictive Wavelet Neural Network Intelligent Control System

Liu Jingwei¹, Zhou Rui², Zhao Hui³, Zhu Minling⁴, Meng Xianghua⁵, Zhang Yuhao^1,6

1. Information College, Capital University of Economics and Business, Beijing 100070, China;
2. School of Chinese Materia, Beijing University of Chinese Medicine, Beijing 100029, China;
3. Research Institute of Information Technology, Tsinghua University, Beijing 100084, China;
4. School of Computer Science, Beijing Information Science & Technology University, Beijing 100101, China;
5. School of Applied Science, Beijing Information Science and Technology University, Beijing 100192, China;
6. Computational Transportation Science Center, Capital University of Economics and Business, Beijing 100070, China;

Received:2016-02-17 Revised:2016-07-30 Online:2018-10-10 Published:2019-01-04

Abstract

Abstract: In order to realize control parameters online tuning and optimizing applications in the high risk and high pollution environment, vector time series prediction based intelligent control methods are proposed, which are combined autoregressive moving average vector time series predictive method and wavelet neural network method. Based on theoretical and comparative research: feasibility, stability, better dynamic characteristics, less steady-state error of the proposed methods are verified. A comparative simulation platform for the above intelligent control system is provided. Stability guarantee method of the above new methods is given.

Key words: VARMA, vector time series predictive control, control parameters online tuning, wavelet neural network, intelligent control system simulation

CLC Number:

U621
TP273

Liu Jingwei, Zhou Rui, Zhao Hui, Zhu Minling, Meng Xianghua, Zhang Yuhao. Simulation Research on Predictive Wavelet Neural Network Intelligent Control System[J]. Journal of System Simulation, 2018, 30(10): 3770-3780.

References

[1] Dinh Quang T, A Kyoung Kwan, Jong Il Yong, et al. Design of an online tuning modified-grey fuzzy PID controller for nonlinear systems[C]// 2011 International Conference on Fluid Power and Mechatronics (FPM),(S2185-6303). Beijing, China: IEEE, 2011: 481-486.
[2] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al.Human-level control through deep reinforcement learning[J]. Nature (S0028-0836), 2015, 518(7540): 529-533.
[3] 侯忠生. 无模型自适应控制的现状与展望[J]. 控制理论与应用, 2006, 23(4): 586-592.
Hou Zhongsheng.On model-free Adaptive Control: The State of the Art and Perspective.[J]. Control Theory & Applications, 2006, 23(4): 586-592.
[4] 席裕庚, 李德伟, 林姝. 模型预测控制——现状与挑战[J]. 自动化学报, 2013, 39(3): 222-236.
Xi Yugeng, Li Dewei, Lin Mei.Model Predictive Control — Status and Challenges[J]. Acta Automatica Sinica, 2013, 39(3): 222-236.
[5] 侯忠生. 再论无模型自适应控制[J]. 系统科学与数学, 2014, 34(10): 1182-1191.
Hou Zhongsheng.Highlight and perspective on model free adaptive control[J]. Journal of Systems Science and Mathematical Sciences, 2014, 34(10): 1182-1191.
[6] 陈小强, 马泳娟, 侯涛, 等. 预测模糊PID在高速列车速度控制中的研究[J]. 系统仿真学报, 2014, 26(1): 191-196.
Chen Xiaoqiang, Ma Yongjuan, Hou Tao, et al.Study on Speed Control of High-speed Train Based on Predictive Fuzzy PID Control[J]. Journal of System Simulation (S1004-731X), 2014, 26(1): 191-201.
[7] Weiwei Liu, Guojun Lu, Yanguang Huang, et al.Proposal of an expert PID control strategy for current controllers in parallel active power filters[C]// 2012 China International Conference on Electricity Distribution (S2161-7481). China: IEEE, 2012: 1-4.
[8] Yanmin Nie, Zhiqiang He.Optimization of the Main Steam Temperature PID Parameters Based on Improve BP Neural Network[C]// 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SMTA 2015). China: Aphasiology, 2015: 113-116.
[9] Sharifian M B B, Mirlo A, Tavoosi J, et al. Self-adaptive RBF neural network PID controller in linear elevator[C]// 2011 International Conference on Electrical Machines and Systems (ICEMS). China: IEEE, 2011: 1-4.
[10] 王普, 刘经纬, 李会民, 等. 自适应小波神经网络异常检测故障诊断分类系统及方法 [P]. 中国专利: 201110023943.2, 2012-08-30.
Wang Pu, Liu Jingwei, Li Huimin. Adaptive wavelet neural network abnormal detect and fault diagnose and classify method and system [P]. Chinese patent: 201110023943.2, 2012-08-30.
[11] 刘经纬, 王普, 杨蕾. 时间序列预测与智能控制结合的参数在线整定方法与系统 [P]. 中国专利: 201210365314.2, 2012-09-27.
Liu Jingwei, Wang Pu, Yang Lei. Parameters online tuning method and system by combination of time series forecasting and intelligent control [P]. Chinese patent: 201210365314.2, 2012-09-27.
[12] Daubechies I.Ten Lectures on Wavelets. [M]. Philadelphia, PA, USA: SIAM Press, 1992.
[13] 李刚, 许鹏程, 韩龙美. 基于小波分析的电力系统故障时空检测与诊断[J]. 系统仿真学报, 2015, 27(12): 3018-3024.
Li Gang, Xu Pengcheng, Han Longmei.Fault Spatial-temporal Detecting and Diagnosis for Power Grid Based on Wavelet Analysis[J]. Journal of System Simulation (S1004-731X), 2015, 27(12): 3018-3024.
[14] Kreinovich V, Sirisaengtaksin O, Cabren S.Wavelet neural networks are asymptotically optimal approximators for functions of one variable.[C]// Proceeding of IEEE ICNN. Florida, USA: IEEE, 1994, 1: 299-304.
[15] Loussifi Hichem, Nouri Khaled, Braiek Naceur Benhadj.A new efficient hybrid intelligent method for nonlinear dynamical systems identification: The Wavelet Kernel Fuzzy Neural Network[J]. Communications in Nonlinear Science and Numerical Simulation (S1007-5704), 2016, 32: 10-30.
[16] Lucian Liviu, Ciuiu Daniel.Simultaneous Diagonalization of two Matrices and Applications to VARMA Time Series[C]// International Work-Conference on Time Series. China: IEEE, 2014: 848-860.
[17] Aboagye-Sarfo Patrick, Mai Qun, Sanfilippo Frank M, et al.A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia[J]. Journal of Biomedical Informatics (S1532-0464), 2015, 57: 62-73.
[18] Kadiyala Akhil, Kumar Ashok.Vector Time Series Models for Prediction of Air Quality Inside a Public Transportation Bus Using Available Software[J]. Environmental Progress & Sustainable Energy (S1944-7442), 2015: 33(4): 1069-1073.

Simulation Research on Predictive Wavelet Neural Network Intelligent Control System

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 4

Recommended Articles

Metrics

Comments

[1]	Juan Wei, Lei You, Yangyong Guo, Zhihai Tang. Multi-floor Evacuation Model Based on Wavelet Neural Network [J]. Journal of System Simulation, 2022, 34(2): 269-277.
[2]	Wang Xiuyan, Liu Yanmin, Zhang Gewen, Li Zongshuai, Lin Jiaquan. Prediction of Aircraft Cabin Energy Consumption Based on PSO and CRO Algorithms [J]. Journal of System Simulation, 2018, 30(8): 3074-3081.
[3]	Liu Zengliang, Zhou Songlin, Zhou Tongxu. Short -term Prediction of Wind Power Based on IDE-WNN and Probabilistic Evaluation [J]. Journal of System Simulation, 2016, 28(2): 476-482.
[4]	Wang Chao, Liu Rongzhong, Hou Yuanlong, Gao Qiang, Wang Li. Servo Load Simulator Based on Improved Wavelet Neural Network [J]. Journal of System Simulation, 2015, 27(2): 344-351.