To effectively identify the particulate and aggregative fluidization of fluidized bed , bp neural network model has been developed . the results show that bp neural network model is more efficient than the traditional method 散式流化和聚式流化的判別仿真實(shí)驗(yàn)表明, bp神經(jīng)網(wǎng)絡(luò)流型識(shí)別模型能夠準(zhǔn)確快速地識(shí)別這兩類(lèi)模型,識(shí)別的準(zhǔn)確率高于傳統(tǒng)判別方法。
Finally , bp neural network recognition model of particulate and aggregative fluidization and rbf neural network prediction model for chaotic time series of circulating fluidized bed have been set up , which provides new methods for on - line recognition of fluidization state and control and prediction of circulating fluidized bed systems 最后建立了散式流化和聚式流化bp神經(jīng)網(wǎng)絡(luò)識(shí)別模型和循環(huán)流化床中的混沌時(shí)間序列的rbf神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型,為流型在線(xiàn)識(shí)別和循環(huán)流化床系統(tǒng)的控制和預(yù)測(cè)等提供了新的方法。
Experiment datas under fluctuation complexity analysis of three different signals all show that there exist " fluctuation " regime between aggregative fluidization and particulate fluidization . the reason is that there exists mutual competition of obtaining resource ( energy and infonnation ) between all subsystem or variable in transition regime 漲落復(fù)雜性參數(shù)表征的過(guò)渡區(qū)域有明顯的上下起伏漲落的過(guò)程,是由于在過(guò)渡區(qū)流化床動(dòng)力系統(tǒng)中各個(gè)子系統(tǒng)或變量在獲取能量和信息方面存在相互竟?fàn)巺f(xié)同,也就是在獲取資源上存在優(yōu)勢(shì)上的差異造成的。