FPGA Implementation of Multi Layer Perceptron Neural Network for Signal Processing

Artificial Neural Networks support their processing capabilities in a parallel architecture. It is widely used in pattern recognition, system identification and control problems. Multilayer Perceptron is an artificial neural network with one or more hidden layers. This paper presents the digital implementation of multi layer perceptron neuron network using FPGA (Field Programmable Gate Array) for pattern recognition. If the pattern matches with the original then process continued else it is rejected. This network was implemented by using three types of non linear activation function: hardlims, satlins and tansig. A neural network was implemented by using VHDL hardware description Language codes and XC3S250E-PQ 208 Xilinx FPGA device. The results obtained with Xilinx Foundation 9.2i software are presented. The results are analyzed by using device utilization and time delay.