FPGA realization of a radial basis function based nonlinear channel equalizer

Poyueh Chen, Hungming Tsai, Cheng Jian Lin, Chiyung Lee

研究成果: Conference article同行評審

4 引文 斯高帕斯(Scopus)

摘要

In this paper we propose a radial basis function (RBF) neural network for nonlinear time-invariant channel equalizer. The RBF network model has a three-layer structure which is comprised of an input layer, a hidden layer and an output layer. The learning algorithm consists of unsupervised learning and supervised learning. The unsupervised learning mainly adjusts the weight among input layer and hidden layer. The supervised learning adjusts the weight among output layer and hidden layer. We will implement RBF by using FPGA. Computer simulation results show that the bit error rates of the RBF equalize using software and hardware implements are close to that of the optimal equalizer.

原文English
頁(從 - 到)320-325
頁數6
期刊Lecture Notes in Computer Science
3498
發行號III
DOIs
出版狀態Published - 2005
事件Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China
持續時間: 2005 五月 302005 六月 1

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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