在此以一逆傳遞綱路來調諧比例積分類控制器的參數值，且在不必事先決定程序狀態與控制器參數值之函數結構的狀況下，利用類神經綱路的學習特性，由其鍵值來建立一個最佳的函數關係，因此，在設計上較具彈性。此一簡單的控制構與學習法則，經由pH中和程序的電腦模擬控制之測試後，由結果顯示比例積分類神經控制器可有效地執行非線性控制。
This article uses a hierarchical, multilayered neural network to provide parameters for a nonlinear PI controller in response to local operating conditions. The Generalized Delta Rule is adopted for use in training the connective weights of the network for subsequent on-line variation of the network-bused PI controller parameters during control, A highly nonlinear neutralization process are supplied to demonstrate the superior servo as well as regulatory control performance of the proposed neural net-based nonlinear PI control system.
