Evaluation of embedded RBF neural chip with back-propagation algorithm for pattern recognition tasks

This article presents the evaluation analysis of the radial function neural network embedded on an FPGA chip by experiments. The back-propagation algorithm has been embedded and tested for the feasibility for on-line learning tasks. The nonlinear pattern classification task of the XOR logic has been conducted by the designed hardware. Performances are evaluated extensively by different orders of the Taylor-Maclaurin series expansion for approximating nonlinear functions and compared with results by the MATLAB program. The effects on the performance by the nonlinear function approximation have been analyzed by experimental studies of the XOR classification task.