基于RBF网络的参数自学习模糊控制的研究

模糊控制以其自适应性、鲁棒性和易于实现等优点得到广泛应用。然而模糊控制规则的获得通常由专家经验给出,这就存在诸如控制规则不够客观、专家经验难以获得等问题。在模糊控制系统中,模糊规则库的构建是至关重要的,因此研究模糊规则的自动生成有着重要的理论和应用价值。本文首先以模糊控制理论和RBF神经网络理论为基础,提出了一种能够有效表达模糊系统可解释性的RBF网络结构;然后详细讨论在此网络结构下提取模糊规则的学习算法;最后依据上述方法进行仿真实验,实验结果表明,这种根据测量数据自动提取模糊规则的方法是有效的。

Parameter Self-learning Fuzzy Control Based on RBF Neural Network

Fuzzy control has been widely used due to its self- adaptability, robustness and easy implementation. However, fuzzy control rules are usually given by experts according to their experiences, which may not be objective and easy to acquire. It is important to structure the fuzzy rules store in the fuzzy control system. Therefore, researching automatic generation of fuzzy rules has important val- ues in the theory and application. In this paper, firstly, based on fuzzy control theory and radial basis function networks (RBFN) the- ory, a structure of RBF networks is proposed, which can expresses the interpretability of fuzzy systems efficiently. Then the learning algorithm of extracting fuzzy rules from this RBF networks is discussed in detail. Lastly, simulation studies are carried out on exam- ples, the results of simulation show that the algorithm of extracting fuzzy rules based on measured data is an effective method.