A Neural Approach for Multiple Linear Regression and Its Advantages

In this paper a detailed algorithm for multiple linear regression coefficients evaluation is provided, which employs a training algorithm for fuzzy perceptron based on a socalled (e,δ)criteria . After the setting of the learning rate λ and the fuzzy degree (e,δ) are addressed, the neural method named FPMLR is compared to the classical least squares method for regression coefficients evaluation. As a result, we conclude that the neural method is superior to the least squares method in robustness aspect due to its feature of fuzziness, which may contribute to omitting noisy data. Finally, we suggest that the neural method be applied to the implementation of prediction and abnormal detection required by data mining.