Integral Factor Neural Computing System Optimization of Lagrange System based on Fractional Order Model

When using neural networks to solve practical problems, there will be different requirements for different analytic objectives, so various stability problems such as the asymptotic stability, Lagrange stability, multi-stability, and input-to-state stability have emerged. In this study, Lagrange stability is considered to discuss the integral factor neural computing system optimization of Lagrange system based on the fractional order model. Based on the Lyapunov stability theory, aiming at the problem of nonlinear input limitation in practice, taking the associated Lagrange system as the research object, the optimized neural network architecture is studied and tested the stability of the model in the prediction problem. After the testing, the prediction is tested on data set of 50, and error is acceptable.