Simulation of Electronic Equipment Control Method Based on Improved Neural Network Algorithm

Abstract

In recent years, with the continuous improvement of the degree of industrial automation in our country, the intelligent requirements for electronic equipment are also rising. For this reason, some scholars propose to apply artificial neural network algorithm to electronic equipment to control the equipment. However, the current artificial neural network algorithm is generally implemented by FPGA. This scheme has the disadvantages of high cost and lack of applicability. Therefore, the purpose of this paper is to study the simulation of electronic equipment control method based on improved neural network algorithm. This paper uses PSpice circuit simulation software to model, simulate and optimize the design of the circuit. And the nonlinear function generator circuit, adder circuit and analog multiplier circuit are modeled, simulated and optimized by PSpice simulation software, and finally the adder circuit and the multiplier circuit are used to form a neural network overall circuit that meets the requirements to achieve Algorithmic function. After training and simulating the curve obtained by the boiling water experiment with the method in this paper, the experiment shows that the curve fitted by the polynomial is basically consistent with the curve obtained by the experiment, and the maximum error obtained by calculation is 1.35%. Through the research of this paper, it fills the blank of our country's neural algorithm in the control simulation of electronic equipment, and promotes the economic development of our country.