5:3 compressor based neural network with LM Algorithm in Multiplier as Application

Abstract In conventional approach for VLSI, implementing large numbers of operations in parallel is possible with NN. The fundamental task a neural network hardware not dependent on the implementation technology and are quite constructive simulating digital circuits. These NN representations can be incorporated various applications where behaviour these circuits essential to get solution discrete problems. Therefore, supervised learning algorithms used train computer software. One such algorithm L-M method which one powerful widely approaches. This paper accounts designing training model as computing technique 5:3 compressor Lavenberg-Marquardt Algorithm. performance parameters significantly remarkable real time implementation, presented paper.