indexes and weights are firstly fuzzified, then relative membership degree to objectives is obtained by fuzzy operation, and the place of power stations is defined in whole valley 首先對(duì)指標(biāo)進(jìn)行模糊化,通過模糊多目標(biāo)運(yùn)算得到各目標(biāo)的相對(duì)隸屬度,進(jìn)而確定各個(gè)水電站在流域梯級(jí)開發(fā)中的位置。
the main advantage of rough sets data analysis is that it does n't require any prior or additional knowledge about the data, which is then used in this paper to analysis the database, acquiring automatically the hierarchical rule sets . in order to ensure maximum consistency of the quantiflcational data, the genetic algorithms is used to get the optimal number and points of division of quantification intervals . at the same time the quantification intervals is fuzzified and crisp rule sets are then transformed to fuzzy rule sets 粗糙集數(shù)據(jù)分析的主要優(yōu)點(diǎn)在于它不要求任何關(guān)于被處理數(shù)據(jù)的先驗(yàn)或額外的知識(shí),本文利用其對(duì)數(shù)據(jù)庫(kù)進(jìn)行分析計(jì)算,自動(dòng)獲取數(shù)據(jù)庫(kù)在各個(gè)層次上的規(guī)則集:在保證量化后的數(shù)據(jù)庫(kù)具有最大一致性的前提下,利用遺傳算法求取連續(xù)屬性值的最優(yōu)量化區(qū)間個(gè)數(shù)及各個(gè)區(qū)間分點(diǎn)值;同時(shí)將量化區(qū)間進(jìn)行模糊化,將多層次清晰規(guī)則集轉(zhuǎn)化為模糊規(guī)則集,利用模糊推理進(jìn)行決策以提高魯棒性。
in the neural networks control, the structure and the working principle of the cerebella model articulation controller ( cmac ) are discussed firstly, and then based on the mutual supplements and the similarities between fcmac and fuzzy logic, fuzzified cerebella model articulation controller ( fcmac ) is proposed . the learning control system based fcmac are introduced in detail . through the example of fcmac used in the swinging up a pendulum control, the excellent control effects are demonstrated 在神經(jīng)網(wǎng)絡(luò)控制方面,本文主要研究了小腦模型關(guān)節(jié)控制器(cmac),并進(jìn)而根據(jù)cmac與模糊邏輯的互補(bǔ)性與相似性,提出了模糊小腦神經(jīng)網(wǎng)絡(luò)控制器(fcamc),詳細(xì)討論了fcmac的學(xué)習(xí)控制系統(tǒng)與fcmac的自學(xué)習(xí)機(jī)理,通過fcamc對(duì)倒單擺控制的具體例子表明,fcmac控制具有優(yōu)良的控制效果和強(qiáng)的魯棒性。