A neural fuzzy control system with structure and parameter learning
Fuzzy Sets and Systems - Special issue on modern fuzzy control
Fuzzy Gaussian Potential Neural Networks Using a Functional Reasoning
Selected papers from the IEEE/Nagoya-University World Wisepersons Workshop on Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms,
Influential Rule Search Scheme (IRSS)-A New Fuzzy Pattern Classifier
IEEE Transactions on Knowledge and Data Engineering
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer
Journal of Medical Systems
On multistage fuzzy neural network modeling
IEEE Transactions on Fuzzy Systems
Avoiding exponential parameter growth in fuzzy systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
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Breast cancer is the cause of the most common cancer death in women. Early detection of the breast cancer is an effective method to reduce mortality. Fuzzy Neural Networks (FNN) comprises an integration of the merits of neural and fuzzy approaches, enabling one to build more intelligent decision-making systems. But increasing the number of inputs causes exponential growth in the number of parameters in Fuzzy Neural Networks (FNN) and computational complexity increases accordingly. This phenomenon is named as "curse of dimensionality". The Hierarchical Fuzzy Neural Network (HFNN) and the Fuzzy Gaussian Potential Neural Network (FGPNN) are utilized to deal this problem. In this study, the HFNN and FGPNN by using new training algorithm, are applied to the Wisconsin Breast Cancer Database to classify breast cancer into two groups; benign and malignant lesions. The HFNN consists of hierarchically connected low-dimensional fuzzy neural networks. It can use fewer rules and parameters to model nonlinear system. Moreover, the FGPNN consists of Gaussian Potential Function (GPF) used in the antecedent as the membership function. When the number of inputs increases in FGPNN, the number of fuzzy rules does not increase. The performance of HFNN and FGPNN are evaluated and compared with FNN. Simulation results show the effectiveness of these methods even with less rules and parameters in performance result. These methods maintain the accuracy of original fuzzy neural system and have high interpretability by human in diagnosis of breast cancer.