針對將圓錐體壓入薄壁筒的控制技術中， 對位移傳感器和壓力傳感器在線實時檢測及執行機構氣液增力缸的控制問題， 在分析受外界干擾的情況下， 為保證系統的穩定性和收斂性， 采用基于遞推最小二乘法的多層前向神經網絡算法， 并對它的條件參數進行了一般性的推導。該神經網絡為3 層前向網絡， 其隱含層神經元數為10 個， 輸出層神經元數1 個。包括輸出層與隱含層、輸入層與隱含層間權的調整算法。用典型PID 算法和神經網絡的控制算法分別仿真結果可見， 神經網絡控制解決了壓合過程純滯后的非線性問題， 達到了預期的技術要求。
關 鍵 詞 ： 最小二乘法； 神經網絡；PID 算法
Abstract: Aiming at a series of control problems about putting a cone into thin canister, online and real time test of displacement sensor and pressure sensor, and the control of increasing pressure jar as executive machine, the multi-layer front neural network algorithm based on least square method (LSM) was applied to ensure stability and astringency of the system under the conditions of he system disturbed by the environment, and its some of condition parameters were deduced. There are 3 layers in multi-layer front neural network, there are 10 nodes in hide layer, and is 1 node in the output layer. The network contains adjusted algorithm of the weight between output and hide layer, and between input and hide layer. The emulation results of typical PID algorithm and neural network control algorithm show that the nonlinear problem for pressed process lag was resolved with the neural network control, the expected technical requirement was achieved.
Key Words: LSM; Neural network; PID algorithm