This paper proposes a novel RBF training algorithm based on immune operations for dynamic problem solving. The algorithm takes inspiration from the dynamic nature of natural immune system and locally-tuned structure of RBF neural network. Through immune operations of vaccination and immune response, the RBF network can dynamically adapt to environments according to changes in the training set. Simulation results demonstrate that RBF equalizer based on the proposed algorithm obtains good performance in nonlinear time-varying channels.

In this letter, an area-efficient architecture for the hardware implementation of the real-time prime factor Fourier transform (PFFT) is presented. In the proposed architecture, a prime length DFT module with the one-point-per-cycle (OPPC) property is implemented by the parallel distributed arithmetic (DA), and a cyclic convolution feature is exploited to simplify the structure of the DA cells. Based on the proposed architecture, a real-time 65-point PFFT processor is designed, and the synthesis results show that it saves over 8% gates compared to the existing real-time 64-point DFT designs.