Design And Performance Optimization Of A Deep Learning-Based Fourth-Order Fitting Algorithm For Pneumatic Measurement Instruments

Abstract

As an important tool in the field of precision measurement, pneumatic measuring instruments are widely used in aerospace, machinery manufacturing and other industries, and their measurement accuracy directly affects product quality and safety performance. Traditional fitting algorithms mostly rely on low-order models, which are difficult to accurately capture the nonlinear characteristics of complex aerodynamic data, resulting in large measurement errors. Deep learning technology has become an emerging means to improve pneumatic measurement accuracy due to its powerful nonlinear fitting ability and automatic feature extraction advantages. The design of fourth-order fitting algorithm based on deep learning aims to combine the advantages of high-order mathematical models and intelligent algorithms to achieve high-precision fitting and optimization processing of pneumatic measurement data. Experiments show that the proposed algorithm reduces the average error by 65% compared with the traditional fourth-order fitting (0.03%￫0.01%), and the calculation delay is less than 15ms, which meets the urgent demand for high-precision measurement in modern industry. This paper aims to explore the design of fourth-order fitting algorithm for pneumatic measuring instrument based on deep learning, improve fitting accuracy and computational efficiency, and promote the development of pneumatic measurement technology.