模糊神經網絡用于非線性系統(tǒng)模型辨識

【摘要】提出了一種非線性系統(tǒng)的模型辨識方法。在只有被辨識系統(tǒng)的輸入輸出數據的情況下，利用一種無監(jiān)督的聚類算法來進行結構辨識，從而自動獲得模糊規(guī)則庫，并可以得到模糊系統(tǒng)的初始參數。在聚類的基礎上，構造一個與之相匹配的模糊神經網絡，用它的學習算法來訓練網絡得到一個精確的模糊模型，從而實現參數辨識。同時，證明了所構造的模糊神經網絡具有通用逼近能力，這個能力在模糊建模和模糊控制方面非常有用。通過對兩個非線性系統(tǒng)辨識的仿真結果驗證了該方法的有效性。
關 鍵 詞 模糊神經網絡; 結構辨識; 參數辨識; 系統(tǒng)辨識

In the last few years, based on neural network and fuzzy system, a lot of new system identification and control methods of nonlinear system have been proposed [1～5]. Despite the fact that these methods are effective in some application area, most of them are only used in parameter identification not in structure identification. The proposed model identification of nonlinear system is composed of two parts: structure identification and parameter identification. At the same time, this FNN has universal approximation capability, a property very useful in, e.g., modeling and control applications.