本文選題：數(shù)據(jù)與知識(shí)驅(qū)動(dòng) + 單輸入規(guī)則模塊�。� 參考：《山東建筑大學(xué)》2017年碩士論文

【摘要】：自從模糊集合的概念出現(xiàn)以來(lái),由于其能夠充分地利用人類或?qū)＜抑R(shí)來(lái)處理系統(tǒng)中存在的各種不確定性,使其在各個(gè)研究領(lǐng)域中得到了越來(lái)越多的應(yīng)用。特別是模糊邏輯系統(tǒng),已廣泛應(yīng)用于建模和控制領(lǐng)域。然而,在系統(tǒng)建�；蚩刂七^(guò)程中,當(dāng)輸入變量維數(shù)較高時(shí),模糊邏輯系統(tǒng)不可避免地面臨規(guī)則爆炸問(wèn)題,在這種情況下,很難實(shí)現(xiàn)模糊規(guī)則的建立以及系統(tǒng)參數(shù)的優(yōu)化。為解決以上問(wèn)題,本文提出了基于數(shù)據(jù)與知識(shí)的模糊系統(tǒng)的設(shè)計(jì)方法,其主要研究?jī)?nèi)容如下:首先,詳細(xì)介紹了單輸入規(guī)則模塊加權(quán)模糊推理系統(tǒng)的結(jié)構(gòu)及其單調(diào)性性能,并在此基礎(chǔ)上,提出了一種基于數(shù)據(jù)與知識(shí)的單輸入規(guī)則模塊加權(quán)模糊推理系統(tǒng)的設(shè)計(jì)方法。該方法在嵌入知識(shí)的基礎(chǔ)上,運(yùn)用基于數(shù)據(jù)的參數(shù)學(xué)習(xí)策略對(duì)系統(tǒng)的參數(shù)進(jìn)行優(yōu)化。將該方法應(yīng)用于熱舒適性預(yù)測(cè),仿真和比較結(jié)果證明了該方法對(duì)于熱舒適性預(yù)測(cè)的有效性,并且比一些其他現(xiàn)有方法表現(xiàn)得更好。其次,研究了單輸入規(guī)則模塊加權(quán)模糊推理系統(tǒng)的擴(kuò)展結(jié)構(gòu)—函數(shù)形單輸入規(guī)則模塊模糊推理系統(tǒng)(FSIRM-FIS),并在此基礎(chǔ)上加入神經(jīng)結(jié)構(gòu),提出了一種函數(shù)型單輸入規(guī)則模塊加權(quán)神經(jīng)模糊系統(tǒng)(FSIRMNFS),此系統(tǒng)結(jié)合了FSIRM-FIS和神經(jīng)網(wǎng)絡(luò)兩者的優(yōu)點(diǎn)。同時(shí),為了得到系統(tǒng)的最小訓(xùn)練誤差和最佳參數(shù),提出了一種基于最小二乘法的參數(shù)學(xué)習(xí)算法。將提出的FSIRMNFS及其參數(shù)學(xué)習(xí)算法應(yīng)用于小時(shí)風(fēng)速預(yù)測(cè),仿真和比較結(jié)果驗(yàn)證了該系統(tǒng)對(duì)于小時(shí)風(fēng)速預(yù)測(cè)的有效性。最后,提出了一種數(shù)據(jù)驅(qū)動(dòng)的二型模糊邏輯系統(tǒng)的構(gòu)建方法。首先通過(guò)自適應(yīng)神經(jīng)模糊推理系統(tǒng)(ANFIS)構(gòu)建一型模糊邏輯系統(tǒng),再通過(guò)集成構(gòu)建的多個(gè)一型模糊邏輯系統(tǒng)得到二型模糊邏輯系統(tǒng)。將此方法構(gòu)造的二型模糊邏輯系統(tǒng)應(yīng)用于風(fēng)速預(yù)測(cè)問(wèn)題,并與常用的BPNN和ANFIS方法進(jìn)行比較,仿真和比較結(jié)果表明,所提出的方法在達(dá)到類似性能的同時(shí),大大減少了訓(xùn)練時(shí)間。隨著數(shù)據(jù)量的爆炸性增長(zhǎng),該方法還有效地減少了二型模糊邏輯系統(tǒng)的建模時(shí)間。
[Abstract]:Since the concept of fuzzy set appeared, it has been applied more and more in various research fields because of its ability to make full use of human or expert knowledge to deal with all kinds of uncertainties in the system. Especially fuzzy logic systems have been widely used in modeling and control fields. However, in the process of system modeling or control, when the dimension of input variables is high, the fuzzy logic system inevitably faces the problem of rule explosion. In this case, it is difficult to realize the establishment of fuzzy rules and the optimization of system parameters. In order to solve the above problems, this paper presents a design method of fuzzy system based on data and knowledge. The main research contents are as follows: firstly, the structure and monotonicity of the weighted fuzzy reasoning system with single input rule module are introduced in detail. On this basis, a design method of a single input rule modular weighted fuzzy reasoning system based on data and knowledge is proposed. On the basis of embedding knowledge, the parameter learning strategy based on data is used to optimize the parameters of the system. The method is applied to thermal comfort prediction. The simulation and comparison results show that the proposed method is effective in predicting thermal comfort and is better than some other existing methods. Secondly, the extended structure of the single input rule module weighted fuzzy inference system, the function form single input rule module fuzzy inference system, is studied, and the neural structure is added to the system. A functional single-input rule modular weighted neurofuzzy system (FSIRMNFS) is proposed, which combines the advantages of FSIRM-FIS and neural network. At the same time, in order to obtain the minimum training error and optimal parameters, a parameter learning algorithm based on least square method is proposed. The proposed FSIRMNFS and its parameter learning algorithm are applied to hourly wind speed prediction. The simulation and comparison results show that the proposed system is effective for hourly wind speed prediction. Finally, a data-driven fuzzy logic system is proposed. First, a type of fuzzy logic system is constructed by adaptive neural fuzzy inference system (ANFIS), and then a type 2 fuzzy logic system is obtained by integrating multiple types of fuzzy logic systems. The type 2 fuzzy logic system constructed by this method is applied to the wind speed prediction problem and compared with the usual BPNN and ANFIS methods. The simulation and comparison results show that the proposed method achieves similar performance and greatly reduces the training time. With the explosive growth of data volume, the modeling time of type 2 fuzzy logic system is reduced effectively.
【學(xué)位授予單位】：山東建筑大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O159;TP181

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