本論文旨在針對類神經網路高木・菅野模糊型時延系統之時延相關穩定化 準則提出改善之方法。本文所提方法可處理解決時間延遲微分導數小於1 之限 制，使快速變動之時間延遲系統能獲得較大之延遲時間下確保時間延遲系統達到 好漸近穩定條件。首先提出類神經網路高木・菅野模糊型時延系統之時延相關漸 近穩定度之充分條件。基於線性矩陣不等式(LMIs)求解最大允許延遲時間的問 題。採用Lyapunov 泛函數最佳化演算法設計迴授控制器推導出基於LMI 的控 制器設計方法。根據此充分條件，推導成一個凸優化問題，使用LMI 工具箱求 解器，可得到該系統的最大允許延遲時間（MAUB）。文中舉例驗證與現有文獻 結果相比較可得較寬廣的時間延遲範圍使得系統仍為漸近穩定。

In this paper, the stability of Takagi-Sugeno (T-S) fuzzy system for neural networks with time-varying delays is investigated. The constraint on the time-varying delay function is removed, which means that a fast time-varying delay is allowed. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration, and new delay-dependent sufficient stability criteria are obtained in terms of linear matrix inequalities (LMIs) which can be solved by various optimization algorithms. Numerical examples are included to show that the proposed method is effective and can provide less conservative results.