Training recurrent neural networks via dynamical trajectory-based optimization

NEUROCOMPUTING

卷:368

Article

Khodabandehlou, Hamid¹  Fadali, M. Sami² 

[1] Univ Nevada, Reno, NV 89557 USA

[2] Univ Nevada, Elect & Biomed Engn Dept, Reno, NV 89557 USA

关键词: Neural networks;    System identification;    Global optimization;    Trajectory-based optimization;    Lyapunov stability;   

DOI  :  10.1016/j.neucom.2019.08.058

来源: Elsevier

【 摘 要 】

This paper introduces a new method to train recurrent neural networks using dynamical trajectory-based optimization. The optimization method utilizes a projected gradient system (PGS) and a quotient gradient system (QGS) to determine the feasible regions of an optimization problem and search the feasible regions for local minima. By exploring the feasible regions, local minima are identified and the local minimum with the lowest cost is chosen as the global minimum of the optimization problem. Lyapunov theory is used to prove the stability of the local minima and their stability in the presence of measurement errors. Numerical examples show that the new approach provides better results than genetic algorithm and backpropagation through time (BPTT) trained networks. (C) 2019 Elsevier B.V. All rights reserved.

【 授权许可】

Free