Design of Fuzzy Ensemble Architecture Realized With the Aid of FCM-Based Fuzzy Partition and NN With Weighted LSE Estimation

Neural Networks (NNs) with Least Square Error (LSE) estimation form a certain type single hidden layer feed-forward neural networks. In this class of networks, the input connections (weights) and the biases of hidden neurons are generated randomly and fixed after being generated. The output connections are estimated by the LSE method rather than the back-propagation method. The random generation of the input connection weights and the hidden biases results in the larger number of hidden neurons to assure the quality of classification performance. To reduce the number of neurons in the hidden layer while maintaining the classification performance, we apply a “divide and conquer” strategy. In other words, we divide an overall input space into several sub-spaces by using information granulation technique (Fuzzy C-Means clustering algorithm) and determine the local decision boundaries among related sub-spaces. A decision boundary defined in the input space can be considered as being composed of several decision boundaries defined in sub-spaces which form the entire input space. For the decision boundaries defined in the sub-spaces, their nonlinearity becomes lower in comparison with the one being encountered when considering the entire input space. Through the weighted least square error estimation instead of using the least square error estimation method, the connections of several neural networks can be estimated without interfering with each other.