模糊神经网络在网络中心机房温度调节中的应用研究  

The Applications of Fuzzy Neural Network Used in Network Center Room Temperature Control

作　　者：牟浩[1] 任开春[1] 文迪[2] 秦恺[1] Hao Mu;Kaichun Ren;Di Wen;Kai Qin(Key Laboratory of Engineering, Chongqing Institute of Communication, Chongqing;Institute of Automotive Electronic and Embedded System, Chongqing University of Posts and Telecommunications, Chongqing)

机构地区：[1]重庆通信学院控制工程重点实验室,重庆 [2]重庆邮电大学汽车电子与嵌入式系统研究所,重庆

出　　处：《人工智能与机器人研究》2013年第1期43-47,共5页Artificial Intelligence and Robotics Research

摘　　要：网络中心机房的温度是大延时、大惯性的被控对象,针对此特点,以模糊控制和神经网络结合的思想,设计了一种基于模糊神经网络的控制器。建立了以T-S模糊模型为基础的5层模糊神经网络结构,并采用改进的BP算法和最小二乘法为模糊神经网络的学习算法。仿真结果表明,该方法下系统响应时间短、超调量小且稳态精度高,有较好的动态品质、稳定性和抗干扰性。The temperature of network center room is large time delay, great inertia controlled object. In view of the characteristics, combined with fuzzy control and neural network, a kind of fuzzy neural network controller was de-signed. On the basis of T-S fuzzy model, five-layers fuzzy neural network structure was established. The improved BP algorithm and least square method for fuzzy neural network learning algorithm was used. The simulation results show that system response time is short and small overshoot and steady state of high accuracy, good dynamic quality and sta-bility and anti-jamming.

关 键 词：模糊神经网络 BP学习算法 温度调节 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]