Identification and Equalization of Nonlinear Channel Based on ANFIS

Abstract:

The demand of high-speed communication leads that the application for resource of channel exceeds the range of linear model. The channel should be described as a nonlinear model. So, the paper uses the adaptive neuron-fuzzy inference system (ANFIS) to identify and equalize the nonlinear channel of the high-speed communication system. Meanwhile, the subtract cluster is applied to identify the construction of the ANFIS and the hybrid learning algorithm based on the least square and back-propagation is used to train network. The simulation results show that the convergence rate and identification accuracy of the ANFIS are better than BP network and the efficiency of the ANFIS based on subtract cluster partition algorithm is higher than that of the ANFIS based on the grid partition algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 195-196)
• Pages: 240-244
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.195-196.240
• Online since: August 2012
• Authors: Qiang Qu, Xiao Li Wang, Xue Bo Chen
• Keywords: ANFIS, Back-Propagation Network, Channel Identification, Nonlinear

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DOI: 10.1109/21.256541

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