Annealing robust neural fuzzy networks for modeling of mitogen-activated protein kinases systems with outliers

Abstract

In this paper, the annealing robust neural fuzzy networks (ARNFNs) are proposed to improve the problems of neural fuzzy networks for the modeling of mitogen-activated protein kinases (MAPK) systems with outliers. Firstly, the support vector regression (SVR) approach is proposed to determine the initial structure of ARNFNs for the modeling of the MAPK systems with outliers.Because of a SVR approach is equivalent to solving a linear constrained quadratic programming problem under a fixed structure of SVR, the number of hidden nodes, the initial parameters and the initial weights of ARNFNs are easy obtained via the SVR approach. Secondly, the results of SVR are used as initial structure in ARNFNs for the modeling of the MAPK systems with outliers. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for ARNFNs, and applied to adjust the parameters in the membership function as well as weights of ARNFNs. Hence, when an initial structure of ARNFNs are determined by a SVR approach, the ARNFNs with ARLA have fast convergence speed for the modeling of the MAPK systems with outliers.