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In this paper an intelligent technique for the determination of the oral cancer stages is proposed. Fuzzy If-then rules have been defined which governs the decision of the proposed system and then trained by neural network. The system is then simulated on adaptive neurofuzzy system toolbox and results have been generated. The final results show a training error equal to 0.219, which is very close...
In this paper, diagnosis of diabetes using soft computing is presented. This research work is based on the fuzzy if-then rules and tuning of the parameters by neural network. A variational method for determining, globally optimal learning parameters and learning rules for on-line gradient descent training has been proposed in the paper. Neurofuzzy system is put in the framework to facilitate learning...
In this paper, adaptive neurofuzzy inference system(ANFIS) approaches for automatically classified the diabetes is presented. ANFIS, used fuzzy logic expert system for decision making ability and neural network learning approach of backpropagation algorithm. Simulated result shows, the proposed work is effectively working with accuracy to classified the diabetes of reducing error rate with .0524.
In this paper, a Neuro-Fuzzy integrated system, which is based on fuzzy inference system using on-line learning ability of neural network is presented. By using on-line learning procedure, the proposed neuro-fuzzy integrated system (NFIS) can be used to construct an input-output mapping based on fuzzy if-then rules and the tuning of the parameters of membership function. The membership functions for...
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