自适应模糊神经网络智能协调控制系统在DCS上的实现

Proceedings of 2011 International Conference on Modelling, Identification and Control Pub Date : 2011-06-26 DOI:10.1109/ICMIC.2011.5973740

Yun Du, Hui-Qin Sun, Xueli Wu, Dong-hui Liu

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引用次数: 0

摘要

DCS的普及和改进为高级控制提供了强大的硬件和软件平台。为了充分发挥DCS系统的优势，结合复杂工业过程的特点，提出了一种自适应模糊神经控制器。重点研究了自适应模糊和神经网络的混合学习算法。它采用DCS的接口和编程语言来研究高级控制，包括人工智能、专家系统和模糊神经网络控制。通过仿真和在电阻炉温度控制系统中的实际应用，说明了该方案的有效性。实践证明了该方法的可行性，并取得了满意的控制效果。

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Realization of adaptive fuzzy neural networks intelligence coordination control system on DCS

DCS provides a powerful hardware and software platform for advanced control for its popularity and improvement. To take full advantage of DCS system, this paper proposes an adaptive fuzzy-neural controller considering of the characteristics of the complex industrial processes. It focuses on the hybrid learning algorithm of adaptive fuzzy and neural network. It uses the interface and programming language of DCS to study the advanced control, and comprises of artificial intelligence, expert system, and fuzzy neural network control. The effectiveness of the proposed scheme is illustrated through simulation and the practical usage of complex in resistance furnace temperature control system. It is proven its feasibility and achieves satisfactory control effect.

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Proceedings of 2011 International Conference on Modelling, Identification and Control

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