| Predictive Inverse Neurocontrol： an experimental case study |
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| 引用本文： | 李胜波.Predictive Inverse Neurocontrol： an experimental case study[J].哈尔滨工业大学学报(英文版),2008,15(1):109-112. |
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| 作者姓名： | 李胜波 |
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| 作者单位： | Far Eastern State Technical University Automated Manufacturing System Department，10 Pushkinskaya，Vladivostok，690950，Russia，Far Eastern State Technical University，Automated Manufacturing System Department，10 Pushkinskaya，Vladivostok，690950，Russia，Mechanical Engineering School Heilongjiang Institute of Science and Technology，Far Eastern State Technical University，Automated Manufacturing System Department，10 Pushkinskaya，Vladivostok，690950，Russia，Far Eastern State Technical University，Automated Manufacturing System Department，10 Pushkinskaya，Vladivostok，690950，Russia，Harbin 150027，China |
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| 摘 要： | To increase predictive behaviors of neural network dynamic model, an experimental case study of a new approach to systems controller design is presented. The experiment is based on neural networks inverse plant model. Special rules for network training are developed. Such system is close to model-based predictive control, but needs much less computational resources. The approach advantages are shown by the control of laboratory complex plants.
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| 关 键 词： | 人工神经网络 实验研究 计算机 技术性能 |
Predictive Inverse Neurocontrol: an experimental case study |
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| Konstantin Zmeu,Boris Notkin,LI Sheng-bo,Vyacheslav Stepaniuk,Pavel Dyachenko.Predictive Inverse Neurocontrol: an experimental case study[J].Journal of Harbin Institute of Technology,2008,15(1):109-112. |
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| Authors: | Konstantin Zmeu Boris Notkin LI Sheng-bo Vyacheslav Stepaniuk Pavel Dyachenko |
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| Affiliation: | 1. Far Eastern State Technical University, Automated Manufacturing System Department, 10 Pushkinskaya, Vladivostok, 690950, Russia
2. Mechanical Engineering School, Heilongjiang Institute of Science and Technology, Harbin 150027, China |
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| Abstract: | To increase predictive behaviors of neural network dynamic model, an experimental case study of a new approach to systems controller design is presented. The experiment is based on neural networks inverse plant model. Special rules for network training are developed. Such system is close to model-based predictive control, but needs much less computational resources. The approach advantages are shown by the control of la-boratory complex plants. |
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| Keywords: | predictive control inverse control neural networks inverse plant model |
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