规则推理与神经计算智能控制系统改进及比较

Improvement and comparison research between intelligent control systems based on rule based reasoning and neural computation AI methods

针对生产生活实践中的智能系统在实施控制过程中关键参数的实时在线智能整定与优化问题与需求,实现将不同类型人工智能方法与经典的控制方法对接从而构成多种复合控制(AI-CC)方法,提出改进算法并进行理论分析与仿真对比研究。首先实现了基于规则与模糊推理机制的AI-CC方法,提出了增量式改进算法,进而提出基于小波神经网络的AI-CC方法,进一步对两类智能系统的稳定性进行理论分析,提出稳定性保证算法,最后对比研究不同类型的智能系统在智能程度与性能特征方面的差异。研究成果为该领域研究者提供了多种改进的智能控制算法及其对比参照和理论分析,为该方法在工程实践中低成本地升级并稳定可靠地应用提供可操作方案。

To solve problems, enable real-time online tuning, and to optimize intelligent system parameters during production and daily life usage, different artificial intelligent-classical （AI-CC） control methods and systems are proposed using a combination of different types of artificial intelligent methods and classical control methods. Algorithm improvements are made, and a theoretical analysis comparing the stability and simulation of the AI-CC methods is also implemented. This research achieves the following. Implementation of a fuzzy classical-based intelligent control, and proposal of an incremental improvement algorithm and further adaptive wavelet neural network classicalbased intelligent control （AI-CC）. A theoretical analysis and stability ensuring method is also proposed and a comparative study undertaken. This research provides results of different types of improved AI-CC methods and a comparative study for use in further academic research, and is expected to enable low cost upgrades and a reliable solution （theoretical guarantee method） for engineering practitioners.