Hardware Realization ofArtificialNeural Networks Using Analogue Devices(English)

Abstract

In this work the analogue neural network has been realized by electronic devices likeOperational Amplifiers and Field Effect Transistor (FET). The FET transistor has been utilized to self adjust weight function for neural network. By use of drain and source resistanceR_ds as function of applied voltage on the gate in linear characteristic region , this resistance has been connected to the input of Operational Amplifiers which becomes as weight function of neural network. Implementing these mentioned characteristics for both FET transistor and operational amplifier , the analogue neural network structure like neuron , weights function and activation function have been realized individually by using the National Instrumentmultisim 10 (NI) Software,then the analogue neural network has been trained successfully by using supervised learning rule like single layer perceptron learning rule and delta learning rule. The results, show good fulfillment of a neural network with analogue hardware devices and verifying the learning rules to train network. Keywords:Analogue neural network, BP learning rule,Perceptron learning rule, NI circuit design suits software(NI multisim 10 software)