• Publications
  • Influence
Feature Selection for Specific Antibody Deficiency Syndrome by Neural Network with Weighted Fuzzy Membership Functions
TLDR
This paper presents selected membership functions extracted by a fuzzy neural network named NEWFM for UCI antibody deficiency syndrome diagnosis. Expand
  • 30
  • 12
A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome
TLDR
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
  • 51
  • 8
Finding obstacle-avoiding shortest paths using implicit connection graphs
  • S. Zheng, J. Lim, S. Iyengar
  • Mathematics, Computer Science
  • IEEE Trans. Comput. Aided Des. Integr. Circuits…
  • 1 December 1996
TLDR
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
  • 69
  • 4
  • PDF
Finding Fuzzy Rules for IRIS by Neural Network with Weighted Fuzzy Membership Function
  • J. Lim
  • Computer Science
  • Int. J. Fuzzy Log. Intell. Syst.
  • 1 September 2004
TLDR
This paper presents a neural network with weighted fuzzy membership functions. Expand
  • 17
  • 2
A Weighted Fuzzy Min-Max Neural Network for Pattern Classification and Feature Extraction
TLDR
A modified fuzzy min-max neural network model for pattern classification and feature extraction is described. Expand
  • 9
  • 1
Prediction of Time Series Microarray Data using Neurofuzzy Networks
TLDR
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
  • 3
  • 1
Learning similarity for semantic images classification
TLDR
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
  • 13
Economic Turning Point Forecasting Using The Fuzzy Neural Network and Non-Overlap Area Distribution Measurement Method
TLDR
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
  • 8
  • PDF