Optimization of Levenberg Marquardt Algorithm Applied to Nonlinear Systems

As science and technology advance, industrial manufacturing processes get more complicated. Back Propagation Neural Network (BPNN) convergence is comparatively slower for processing nonlinear systems. The system used in this study to evaluate the optimization of BPNN based on LM algorithm proved algorithm’s efficacy through a MATLAB simulation analysis. This paper examined application impact enhanced approach using Continuous stirred tank reactor (CSTR) control as an example. study’s findings demonstrate that identification error exceeds 10-5. research’s suggested reactant concentration CA CSTR systems provides better tracking effect stronger anti-interference capacity. Compared PI method, overall superior. result, model has greatly improved accuracy. With some data support accuracy neural network models systems, LM-BP evidently appropriate