Neural Network Classification, Clustering, Function, And Its Rules Extraction Research

Point of view base on data mining, the foundation on many scholars to research, the thesis study of classification , clustering funtion and rule extraction in Neural Networks, many counts results acquired. Study of classifier in Neural Networks, 1) This paper presents fix an initializing algorithm on classifier in BP Neural Networks. The algorithm can not only speed up convergence of BP neural networks and reduce the error of training, but also abstain abstain from converging to local minimum point. 2) The foundation on FTART2, this paper presents a fast adaptive neural network classifier (FANNC), at the same time, an adaptive fault-tolerant neural networks learning algorithm is proposed. 3) A new approach to classifier of neural network ensemble is proposed. Theoretical analyses and experimental results show that this approach outperforms the traditional ones that ensemble all of the individual networks.Study of clustering funtion in Neural Networks, 1) the foundation on analyses exist ' problem of SOM algorithm, this paper presents an algorithm of the fuzzy self-organizing feature map (FSOM), tide over shortcoming of the traditional SOM algorithm. 2) Demonstrated by analysis are weaknesses of the current learning algorithm of fuzzy clustering neural networks FCNN, and the algorithm is improved. The result of simulation verifies the proposal properly.Study of rule extraction in Neural Networks, we proposed a method that is able to extract if-then ruler from trained FANNC networks in this paper, Experimental result shows that those if-then rules are comprehensible, accurate.