COMPARISON OF FEED FORWARD NETWORK AND ELMAN NETWORK FOR FAULT DIAGNOSIS OF CA-THODE RAY OSCILLOSCOPE USING ARTIFICIAL NEURAL NETWORK

Artificial neural networks (ANN) are an information-processing method of a simulation of the structure for bio-logical neurons. This paper makes a research on the ap-proach of the artificial neural network for fault diagnosis of cathode ray oscilloscope. In the last five years, the field of diagnosis has attracted the attention of many researchers, both from the technical area as well as medical area. In this paper, I present my efforts in developing the fast algorithm for the fault diagnosis of CRO using Artificial Neural Net-work. Eight different fault indications have been considered and numbers of faults for each indication have been taken. Network has eight input nodes, each for every indication and thirteen output nodes, each for every fault. The network ha trained by feed forward network and Elman network.