J. Lim

This paper presents selected membership functions extracted by a fuzzy neural network named NEWFM for UCI antibody deficiency syndrome diagnosis. Expand

This paper presents a neuro-fuzzy approach for diagnosis of antibody deficiency syndrome, where a new network with fuzzy activation functions (FAFs) at hidden layer is used. Expand

We introduce a framework for a class of algorithms solving shortest path related problems, such as the one to one shortest path problem, the one-to-many shortest paths problem and the minimum spanning tree problem, in the presence of obstacles. Expand

This paper presents a neural network with weighted fuzzy membership functions. Expand

A modified fuzzy min-max neural network model for pattern classification and feature extraction is described. Expand

We propose the approach to predict the expression values between the genes of the yeast cell using a neural network based on weighted fuzzy membership function. Expand

We develop a framework of learning similarity (LS) using neural networks for semantic image classification, where a LS-based k-nearest neighbors (k-NN"L) classifier is employed to assign a label to an unknown image according to the majority of k most similar features. Expand

This paper proposes a new forecasting model based on the neural network with weighted fuzzy membership functions (NEWFM) concerning forecasting of turning points in the business cycle by the composite index. Expand