Research on Optimization of Fuzzy Network Control System Based on New Smith Predictive Time Delay Compensation

Abstract

Aiming at the problem of network delay when the network control system transmits data, this paper adopts a new type of fuzzy control method of Smith predictor to compensate for the delay. In actual application scenarios, it is difficult to accurately match the Smith prediction model with the actual model. At the same time, the quantization factor and scale factor in the fuzzy PID controller are too dependent on experience, which makes the system’s adaptability to actual working conditions very poor. In this paper, genetic algorithm is used to optimizes fuzzy PID. According to the simulation results, when the Smith predictive model does not match the actual model, the steady-state performance and dynamic performance of the system under the fuzzy PID control optimized by the genetic algorithm are improved.

Optimization of Internal Control for Budget Operations in Public Institutions Based on Random Forest Algorithm

“…According to the process of big data auditing, the audit doubts obtained by auditors through big data auditing methods are classified according to audit indicators, and should ultimately be summarized to form a summary Table of audit doubts. Then, according to the actual audit business needs, tasks are assigned for audit evidence collection, and audit doubt point evidence collection reports are prepared to provide the final audit results [16]. When issuing audit results, summarize the audit opinions obtained through the implementation of big data audits and audit evidence collection, and combine the business background of the big data audit and the initially established audit objectives to obtain the final audit results.…”