臺灣博碩士論文加值系統

本篇論文導出 diagonal recurrent neural network 內理想的最佳適應
學習速率且本適應學習速率是以符合誤差的收歛條件來選取的收歛條件經
討論後被修正為適用於實際控制系統的收歛條件最後以電腦模擬測試本適
應學習速率下 diagonal recurrent neural network 控制系統的控制效
果

In this paper, the ideal best adaptive learning rates
arederived out for diagonal recurrent neural network. The
adaptivelearning rates are chosen for fitting error convergence
requirements.And the convergence requirements are discussed then
modified for a practical control system. Finally the simulation
results are shownin diagonal recurrent neural network based
control system with the modified adaptive learning rates.