K-聚类的模糊神经网络对DO的控制研究

运用一种基于K-聚类算法的模糊径向基函数（RBF）神经网络对污水处理中的溶解氧质量浓度进行控制，该方法结合了模糊控制的推理能力强与神经网络学习能力强的特点，将模糊控制、RBF神经网络以及K-聚类学习算法相结合以在线调整隶属函数，优化控制规则。通过对阶跃输入仿真分析，其结果表明基于RBF的模糊神经网络控制器具有良好的动态性能、较强的鲁棒性和抗干扰能力，使其快速、准确地达到期望水平。

K-Clustering fuzzy neural network to DO control research

Using a fuzzy Radial Basis Function（RBF） neural network based on K-clustering algorithm controls the concentration of quality of the dissolved oxygen（do） in the sewage treatment. This method combines fuzzy control reasoning ability and neural network learning ability characteristic. Fuzzy control, RBF neural network and K-clustering learning algorithm are applied in order to adjust subjection function on-line, optimize control rules. By the step input simulation analysis, the results show that fuzzy neural network controller based on the RBF has a good dynamic performance, strong robustness and anti-interference ability, make it fast and accurately to achieve the desired level.