Fuzzy Nearest Neighbor Partitioning Neural Network for Classification The fuzzy nearest neighbor partitioning neural network (FNNP) is proposed to promote the capability of the neural network classifier. The fixed centroids problem restrains the further improvement of the neural network classifier. The original nearest neighbor partitioning method (NNP) have been proposed to address this problem. In the NNP, the learning method is employed to train the neural network in according with the distribution information of samples. However, the distribution information underutilization problem affects the learning method to obtains the neural network with expected mapping performance. Therefore, we propose the FNNP to overcome this problem. In the FNNP, the fuzzy logic theory is adopted to assist the learning method to comprehensively collect neglected distribution information that increases the probability to find the optimal neural network with expected mapping performance. Experiment results demonstrate that the FNNP achieves remarkable classification performance on various indictors.

Fuzzy Neural Networks (FNNs) Training Algorithm With Dropout via Its Equivalent Fully Connected Fuzzy Inference Systems (FCONFIS) Fuzzy neural network (FNN) often suffers from overfitting problem, especially when FNN has large number of parameters. In the FNN system, there are two types of adjustable parameters, one is control parameters, and the other is link weights of consequent part. To improve convergent rate, Dropout technique is first adopted for Fuzzy neural network. A new training algorithm with dropout technique for FNN is proposed via its equivalent FCONFIS. Illustrative examples are provided for checking the validity of the proposed method. Simulation attained satisfactory results. Proposed method for Fuzzy neural network via FCONFIS has its rising values in all practical applications, such as system identification, expert System and image information processing system xe2x80xa6, etc.