Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
