2002/01/01 by Xinghuo Yu, M.O. Efe, Mehmet Önder Efe +2 · 1 citation
Computer Science · Engineering · #Control Systems and Identification #Fault Detection and Control Systems #Neural Networks and Applications
paper · doi:10.1109/72.977323
openalex publication_date 2002/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/23
A general backpropagation algorithm is proposed for feedforward neural network learning with time varying inputs. The Lyapunov function approach is used to rigorously analyze the convergence of weights, with the use of the algorithm, toward minima of the error function. Sufficient conditions to guarantee the convergence of weights for time varying inputs are derived. It is shown that most commonly used backpropagation learning algorithms are special cases of the developed general algorithm.