An Hybrid Intelligent Computational Modular with Back-Propagation Network
Back-propagation neural network model (BPNN) is an intelligent computational model based on stylebook learning. This model is different from the traditional adaptability symbolic logic reasoning method based on knowledge and rules. At the same time, BPNN model has shortcomings such as: the slowly convergence speed and partial minimum. In the process of adaptability evaluation, the factors were diverse, complicated and uncertain, so an effectual model should adopt the technique of data mining method and fuzzy logic technologies. In this paper, the author ameliorated the back-propagation of BPNN and applied the fuzzy logical theory for dynamic inference of fuzzy rules. Authors also give detailed description on training and experiment process of the novel model.
BP network intelligent computational model,
Zuohua Miao Xianhua Wang Bin Liao
wuhan university of science and technology, wuhan hubei 430081 china university of geosciences, wuhan hubei 430062 hubei university, wuhan hubei 430062
国际会议
2007年IEEE灰色系统与智能服务国际会议(2007 IEEE International Conference on Grey Systems and Intelligent Services)
南京
英文
2007-11-18（万方平台首次上网日期，不代表论文的发表时间）