Application of bp nn in the measurement of dynamic liquid level 動態(tài)液位的神經(jīng)網(wǎng)絡測量方法研究
Discussed are the problem of determining the number of hidden layer's neural units, standardizing of input vectors and choosing the initial connecting weights etc . also this paper improved and optimize the basic bp arithmetic, and designed the measurement module of dynamic liquid level and the recognition module of incipient danger & hazards based bp nn 文中討論了網(wǎng)絡模型中隱含層神經(jīng)單元個數(shù)的選取問題,輸入矢量的標準化處理問題,以及網(wǎng)絡連接權值的初值選取問題等,同時還對基本的bp算法進行了改進、優(yōu)化。設計了基于bp神經(jīng)網(wǎng)絡的動態(tài)液位測量模型和生產(chǎn)重大隱患和危險源的識別模型。
This paper based the project of recognition and control of major incipient danger & hazards in manufacture of fosfomycin sodium in northeast general pharmaceutical factory, solved the problem of the measurement of dynamic liquid level, made a summary of the variational rule of liquid level, graded the incipient danger & hazards by using bp networks of artificial neural networks 本文以東北制藥總廠的磷霉素鈉生產(chǎn)重大隱患和危險源的識別和控制項目為背景,通過運用人工神經(jīng)網(wǎng)絡的bp網(wǎng)絡模型,實現(xiàn)了動態(tài)液位的測量,并對生產(chǎn)過程中液位變化規(guī)律進行了總結,對生產(chǎn)重大隱患和危險源進行了分級研究和應用。
The measurement network module of dynamic liquid level reflected the variational factors and their degrees, produced a simple way of incipient accident recognition, important of all, it made for bettering craft . the recognition network module of incipient danger & hazards divided them into several graduations, assured of safety-control & process-control 動態(tài)液位網(wǎng)絡測量模型反應出了影響實際液位變化的因素及其相關程度,為生產(chǎn)的事故隱患提供了簡易判定依據(jù),更重要的是為生產(chǎn)工藝的改進打下了基礎。