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This article describes a methodology using neural network models Adaline architecture and linguistically interpretable fuzzy systems, both algorithms were used to classify data signal rocks and metals, obtained through sonar. First, the neural network requires training match each input vector with the corresponding output vector for comparison with the desired output, and obtained, through feedback differential, an algorithm that minimizes the error. Secondly, the diffuse pattern contains overlapping of triangular sets to adjust the number of data sets for the antecedent, and singletons for to the consequent. In the evaluation of the rules are used instead of operators average T-norm and the consequents are adjust using recursive least squares. The promising aspect of this research was to achieve a good accuracy in the validation, after training of neural networks, and apply the fuzzy model without sacrificing the fuzzy system interpretability. Both methods are used making little modifications of parameters for obtain the best success percentage, the lowest mean square error (MSE), decreasing execution time, reducing effort in the processing machine and without recourse to other artificial intelligence techniques. |
