本文選題：多視角學(xué)習(xí) + 遷移學(xué)習(xí)　； 參考：《江南大學(xué)》2015年博士論文

【摘要】：人工智能技術(shù)發(fā)展已近70年,在此期間各種智能化方法被提出用于解決各種實(shí)際應(yīng)用問(wèn)題,其中模糊識(shí)別技術(shù)及模糊智能系統(tǒng)建模技術(shù)在醫(yī)療、控制、經(jīng)濟(jì)等領(lǐng)域得到了廣泛的關(guān)注及使用。然而,隨著人們生活及科技水平的提升,越來(lái)越多的新應(yīng)用場(chǎng)景被發(fā)現(xiàn)。在眾多新應(yīng)用場(chǎng)景中,多視角應(yīng)用場(chǎng)景及遷移應(yīng)用場(chǎng)景對(duì)人們的生活和生產(chǎn)有著廣泛的影響,本課題將主要關(guān)注上述兩個(gè)新興的應(yīng)用場(chǎng)景。在上述兩新應(yīng)用場(chǎng)景下,經(jīng)研究我們發(fā)現(xiàn)一些經(jīng)典的模糊識(shí)別技術(shù)及模糊智能系統(tǒng)建模技術(shù)的性能變得不再可靠,其通常面臨以下幾個(gè)挑戰(zhàn):1)在多視角應(yīng)用場(chǎng)景下,由于經(jīng)典的模糊識(shí)別技術(shù)及模糊智能系統(tǒng)建模技術(shù)均是在單視角學(xué)習(xí)場(chǎng)景下提出的,它們本身不具備多視角協(xié)同學(xué)習(xí)的能力,若執(zhí)意選擇這些經(jīng)典方法進(jìn)行多視角學(xué)習(xí)則只能在每個(gè)視角上獨(dú)立學(xué)習(xí),從而得到無(wú)法令人滿意的結(jié)果;2)在遷移應(yīng)用場(chǎng)景下,由于生產(chǎn)的保密性或一個(gè)產(chǎn)業(yè)本身就是一新興產(chǎn)業(yè)以往并無(wú)數(shù)據(jù)積累,這使得經(jīng)典的模糊識(shí)別技術(shù)及模糊智能系統(tǒng)建模技術(shù)在對(duì)該產(chǎn)業(yè)進(jìn)行數(shù)據(jù)處理或?qū)W習(xí)建模時(shí)可用數(shù)據(jù)極少,從而導(dǎo)致經(jīng)典方法的失效。為解決經(jīng)典的模糊識(shí)別技術(shù)及模糊智能系統(tǒng)建模技術(shù)在面對(duì)上述新興應(yīng)用場(chǎng)景時(shí)所面臨的問(wèn)題,本課題將主要分兩大部分分別在多視角及遷移場(chǎng)景下對(duì)模糊識(shí)別技術(shù)及模糊智能系統(tǒng)建模技術(shù)的相關(guān)方法做出適當(dāng)?shù)母倪M(jìn)以期提高模型的性能,具體如下:(1)第一部分為第2至4章節(jié),主要探討了模糊識(shí)別技術(shù)及模糊智能系統(tǒng)建模技術(shù)在多視角領(lǐng)域的應(yīng)用。首先,在第2章節(jié)針對(duì)一種已有的基于模糊聚類算法的多視角模型Co-FKM所存在的問(wèn)題,基于Havrda-Charvat熵理論重新構(gòu)造了新的多視角協(xié)同學(xué)習(xí)方法即異視角空間劃分逼近準(zhǔn)則,并針對(duì)多視角場(chǎng)景下各視角存在差異性的問(wèn)題,基于香農(nóng)熵理論提出了多視角自適應(yīng)加權(quán)策略,進(jìn)而提出了一種熵加權(quán)多視角協(xié)同劃分模糊聚類算法EW-Co P-MVFCM。其次,基于第2章節(jié)關(guān)于多視角模糊聚類的認(rèn)識(shí),于第3章節(jié)基于先前的模糊聚類工作即GIFP-FCM算法,提出了一種具備多視角協(xié)同學(xué)習(xí)能力的基礎(chǔ)算法Co-FCM,所提出的Co-FCM算法引入了4種不同的協(xié)同度量函數(shù)用于拓展方法的應(yīng)用范圍,進(jìn)一步地,考慮到各視角存在的差異性而在基礎(chǔ)的多視角模糊聚類Co-FCM算法上又加入了視角加權(quán)機(jī)制得到了最終的WV-Co-FCM算法,該算法較之Co-FCM算法不僅獲得了最佳視角的辨識(shí)能力,同時(shí)還擁有了更佳的聚類性能。最后,于第4章節(jié),探討了模糊智能系統(tǒng)建模技術(shù)在多視角領(lǐng)域的應(yīng)用,具體地先提出了一種基于大間隔分類機(jī)制的單視角模糊分類模型TSK-FC算法,并以此算法作為基礎(chǔ)模型通過(guò)融入多視角協(xié)同學(xué)習(xí)機(jī)制得到了一種具備雙視角協(xié)同學(xué)習(xí)能力的TSK模糊分類模型Two V-TSK-FC,該模型通過(guò)協(xié)同學(xué)習(xí)機(jī)制能夠在建模過(guò)程中利用各視角獨(dú)立信息的同時(shí)進(jìn)一步利用視角間的關(guān)聯(lián)信息增強(qiáng)算法的性能。(2)第二部分為第5至7章節(jié),主要探討了模糊識(shí)別技術(shù)及模糊智能系統(tǒng)建模技術(shù)在遷移學(xué)習(xí)領(lǐng)域的應(yīng)用。首先,第5章節(jié)針對(duì)非充分?jǐn)?shù)據(jù)集(數(shù)據(jù)貧乏)及噪聲對(duì)最終聚類結(jié)果產(chǎn)生嚴(yán)重干擾的問(wèn)題,依舊基于本章節(jié)所提及的一般化的增強(qiáng)模糊劃分聚類算法(GIFP-FCM),以此算法為基礎(chǔ)通過(guò)在該算法中融入具備聚類特性的遷移學(xué)習(xí)機(jī)制以使得GIFP-FCM算法獲得遷移能力,最終得到遷移GIFP-FCM算法T-GIFP-FCM。其次,針對(duì)傳統(tǒng)模糊系統(tǒng)建模方法在遷移場(chǎng)景下存在的問(wèn)題,以廣泛應(yīng)用的TSK型模糊系統(tǒng)作為研究對(duì)象,探討了具有遷移學(xué)習(xí)能力的模糊系統(tǒng),即TSK型遷移學(xué)習(xí)模糊系統(tǒng),所提的遷移學(xué)習(xí)TSK模糊系統(tǒng)不僅能充分利用當(dāng)前場(chǎng)景的數(shù)據(jù)信息,還能有效地利用歷史相關(guān)場(chǎng)景所積累得到的知識(shí)對(duì)當(dāng)前源場(chǎng)景的建模過(guò)程進(jìn)行輔助學(xué)習(xí),從而提高模型的泛化性能。最后,第7章節(jié)進(jìn)一步地針對(duì)第6章節(jié)提出的TSK型遷移模糊系統(tǒng)在模糊前件參數(shù)和后件參數(shù)遷移學(xué)習(xí)時(shí)所存在的一系列問(wèn)題提出了相應(yīng)的改進(jìn)方案,具體地結(jié)合第5章節(jié)提出的遷移模糊聚類理論以及一種改進(jìn)的遷移學(xué)習(xí)后件學(xué)習(xí)機(jī)制,提出了一種增強(qiáng)知識(shí)遷移的TSK遷移學(xué)習(xí)模糊系統(tǒng),該方法的提出有效地將遷移聚類和遷移模糊系統(tǒng)建模相結(jié)合,使得模糊系統(tǒng)的建模過(guò)程更為智能且學(xué)習(xí)能力更為優(yōu)秀,同時(shí)該方法的提出也為遷移學(xué)習(xí)在智能建模領(lǐng)域的發(fā)展提供一種新的研究思路。
[Abstract]:Artificial intelligence technology has been developed for nearly 70 years. During this period, various intelligent methods have been put forward to solve various practical application problems. Fuzzy recognition technology and fuzzy intelligent system modeling technology have been widely concerned and used in medical, control, economic and other fields. However, with the improvement of people's life and science and technology, more and more Many new application scenarios have been found. In many new application scenarios, multi view application scene and migration application scene have a wide impact on people's life and production. This topic will focus on the above two emerging application scenarios. Under the above two new application scenarios, we have found some classic fuzzy recognition techniques and models. The performance of paste intelligent system modeling technology is no longer reliable, and it usually faces the following challenges: 1) under the multi perspective application scene, because the classical fuzzy recognition technology and fuzzy intelligent system modeling technology are proposed in the single perspective learning scene, they do not have the ability of multi perspective collaborative learning, if they insist on choosing this Some classical methods for multi perspective learning can only be studied independently in every perspective, thus getting unsatisfactory results. 2) in the migration application scenario, the classic fuzzy recognition technology and fuzzy intelligent system modeling are made because of the secrecy of production or the industry itself is a new industry that has not accumulated data in the past. In order to solve the problems faced by the classical fuzzy recognition technology and the fuzzy intelligent system modeling technology in the face of the above emerging application scenarios, the subject will be divided into two parts in multi perspective and migration field respectively. The relevant methods of fuzzy recognition technology and fuzzy intelligent system modeling technology are improved to improve the performance of the model. The following is as follows: (1) the first part is chapter second to 4, mainly discusses the application of fuzzy recognition technology and fuzzy intelligent system modeling technology in the field of multi angle. There are some problems in the multi view model Co-FKM based on fuzzy clustering algorithm. Based on the Havrda-Charvat entropy theory, a new multi perspective cooperative learning method is rebuilt, that is, the different angle of view spatial partition approximation criterion. In view of the difference of various perspectives in multi view scenes, a multi angle adaptive addition based on Shannon entropy theory is proposed. Right strategy, and then proposed an entropy weighted multi view cooperative division fuzzy clustering algorithm EW-Co P-MVFCM. next, based on the second chapter on the understanding of multi perspective fuzzy clustering, the third chapter based on the previous fuzzy clustering work, GIFP-FCM algorithm, proposed a multi perspective collaborative learning ability of the basic algorithm Co-FCM, proposed. The Co-FCM algorithm introduces 4 different cooperative metric functions to extend the application scope of the method. Further, considering the differences in various perspectives, the final WV-Co-FCM algorithm is obtained by adding a visual angle weighting mechanism to the basic multi perspective fuzzy clustering Co-FCM algorithm. The algorithm is not only the best than the Co-FCM algorithm. At the same time, it also has better clustering performance. Finally, in the fourth chapter, the application of fuzzy intelligent system modeling technology in multi field of view is discussed. A single view fuzzy classification model TSK-FC algorithm based on large interval classification mechanism is put forward, and the algorithm is used as the basic model to integrate multi view. The angle cooperative learning mechanism has obtained a TSK fuzzy classification model, Two V-TSK-FC, which has the ability of dual perspective collaborative learning. Through collaborative learning mechanism, the model can make use of the independent information from various perspectives in the process of modeling and further enhance the performance of the algorithm. (2) the second part is the fifth to 7 chapters. The application of fuzzy recognition technology and fuzzy intelligent system modeling technology in the field of migration learning is discussed. First, the fifth chapter is still based on the general enhanced fuzzy partition clustering algorithm (GIFP-FCM), which is still based on this chapter, aiming at the serious interference of the incomplete data set (data poor) and noise to the final clustering results. By integrating the migration learning mechanism with clustering characteristics in the algorithm, the migration ability of GIFP-FCM algorithm is obtained, and then the migration GIFP-FCM algorithm T-GIFP-FCM. is finally obtained. In view of the problems existing in the traditional fuzzy system modeling method in the migration scene, the TSK type fuzzy system is widely used as the research object. The fuzzy system with migration learning ability, that is, TSK type migration learning fuzzy system, the proposed migration learning TSK fuzzy system can not only make full use of the data information of the current scene, but also effectively use the knowledge accumulated in the history related scene to study the modeling process of the current source scene, thus improving the model. Finally, the seventh chapter puts forward the corresponding improvement scheme for the TSK type migration fuzzy system proposed by the sixth chapter in the learning of the fuzzy precursor parameters and the migration of the post parameters, and specifically combines the migration fuzzy clustering theory with the fifth chapter and an improved migration learning. A TSK migration learning fuzzy system is proposed to enhance knowledge migration. The proposed method effectively combines migration clustering and migration fuzzy system modeling, making the modeling process of the fuzzy system more intelligent and better learning ability, and the method is also proposed for the migration learning in the field of intelligent modeling. Development provides a new way of thinking.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP18;TP311.13

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