A soft computing technique for noise data with outliers

In this paper, a soft computing technique is proposed to select an initial structure of neuro-fuzzy networks (NFNs) for the noise data with outlier regression. That is, the proposed soft computing technique is fusion of neural network, fuzzy logic and support vector regression (SVR). Because the SVR approach is equivalent to solving a linear constrained quadratic programming problem under the fixed structure of SVR, the number of hidden nodes and adjustable parameters are easily obtained. That is, the SVR approach is used to obtain the initial structures of the NFNs in the noise data with outlier. Then, an annealing robust learning algorithm (ARLA) uses as the learning algorithm of NFNs. It is applied to adjust the parameters of promise parts and the fuzzy singleton of consequence parts in the NFNs. At the same time, the proposed method is applied to approximate a nonlinear model with outliers. Simulation results are provided to show the validity and applicability of the proposed soft computing technique.

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