Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
The fuzzy systems handbook: a practitioner's guide to building, using, and maintaining fuzzy systems
The fuzzy systems handbook: a practitioner's guide to building, using, and maintaining fuzzy systems
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Proceedings of the 5th International Conference on Genetic Algorithms
Fuzzy expert system for predicting pathological stage of prostate cancer
Expert Systems with Applications: An International Journal
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Soft Computing comprises principally of genetic algorithms, artificial neural networks, and fuzzy logic. Our main focus in this paper is to represent the use of fuzzy logics system trained with neural network models and prove the high prognostic result we obtained thru the models we used. Fuzzy modeling and identification methodologies have been successfully used in a number of real-world applications. The Takagi--Sugeno model has often been employed in the modeling and identification of nonlinear technical processes from data. In this context we propose a new fuzzy inference system designed specifically to predict the survival rate in a given medical data. In this study we are concerned with NPC because it is one of the most common cancers in Malaysia. Two training methods were used namely back propagation and a hybrid method to train the FIS model. These two models were performed to evaluate the predictive accuracy, and the results were found to be satisfactory.