New Noise-Tolerant Neural Algorithms for Future Dynamic Nonlinear Optimization With Estimation on Hessian Matrix Inversion

Nonlinear optimization problems with dynamical parameters are widely arising in many practical scientific and engineering applications, various computational models presented for solving them under the hypothesis of short-time invariance. To eliminate large lagging error solution inherently dynamic nonlinear problem, only way is to estimate future unknown information by using present previous data during process, which termed (FDNO) problem. In this paper, suppress noises improve accuracy FDNO problems, a novel noise-tolerant neural (NTN) algorithm based on zeroing dynamics proposed investigated. addition, reducing complexity, quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method employed intensively burden matrix inversion, NTN-BFGS algorithm. Moreover, theoretical analyses conducted, show that algorithms able globally converge tiny bound or without pollution noises. Finally, numerical experiments conducted validate superiority NTN online problems.