Abstract: This paper proposes a parallel fixed point radial basis function (RBF) artificial neural network (ANN), implemented in a field programmable gate array (FPGA) trained online with a least mean square (LMS) algorithm. The processing time and occupied area were analyzed for various fixed point formats. The problems of precision of the ANN response for nonlinear classification using the XOR gate and interpolation using the sine function were also analyzed in a hardware implementation. The entire project was developed using the System Generator platform (Xilinx), with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA.
In REF a hardware Radial Base Functios (RBF) Artificial Neural Network (ANN) was implemented and trained with the Least Mean Square (LMS) algorithm.
Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Computer Science Medicine