類神經模糊推論模式在水文系統之研究

本研究介紹類神經－模糊推論模式之建構、學習歷程與應用。由於本模式組合了類神經網路模式與模糊推論模式之架構，使其兼具類神經網路理論及模糊推論模式之優點；且利用結合最小平方法及陡降法之綜合性演算法則，使類神經－模糊推論模式在網路參數的調節上可達更具效率之特性。經由定率非線性函數及大甲溪流域內流量推估之模擬結果可知，類神經－模糊推論模式之成效及適用性得到驗證。

關鍵字

類神經網路；模糊推論；最小平方法；陡降法；流量推估

並列摘要

This study presents the constructing procedure, learning process, and application of Artificial Neural-Fuzzy Inference Model (ANFIM). The model combines the traditional neural networks and the fuzzy inference theorem, consequently, it gains more advantages than each one of them. Moreover, our developed model uses the hybrid algorithms of least square estimation and the gradient descent method, it can, hence, adjust the model's parameters effectively. The results of model simulation on a deterministic nonlinear function and the ten-day streamflow of the Dai-Chia Creek basin show that the applicability and practicability of the proposed model.

並列關鍵字

Neural network ； Fuzzy inference ； Least square estimation ； Gradient descent method ； Streamflow estimation

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