FPGA implementation of a probabilistic neural network for a bioelectric human interface
Nan Bu, Takao Hamamoto, Toshio Tsuji, Osamu Fukuda · 2004
Since a probabilistic neural network (PNN) provides a stochastic perspective of pattern discrimination, it has been proven to be efficient for complicated data such as bioelectric signals. As for practical implementation, however, a general-purpose computer is usually necessary, so that a compact design of an application system is difficult to be realized. This paper describes a field programmable gate array (FPGA) implementation of a PNN, with which system on chip (SoC) design of a bioelectric human interface device becomes possible. Its effectiveness is then verified with a practical application, and it is shown that the hardware implementation provides comparable performance with the software solution on a general-purpose computer.