Support vector regression based on fuzzy sigmoid kernel
基于模糊sigmoid核的支持向量机回归建模

In the support vector machines(SVM) framework,kernel function must meet certain requirements to be symmetric and positive semi-definite(PSD) matrix.Although sigmoid function derived from neural network can become a PSD kernel for proper combinations of its free parameters,it has been used in several practical and successful cases.The sigmoid kernel is combined with fuzzy logic methodology first,which makes the computation of SVM simple and of ease implementation in hardware.Experiments for chaotic time series prediction and image filter are also carried out to show that the average CPU time can be decreased markedly in favor of hardware implementation,in spite of small decrease in prediction precision.