【摘要】：免疫系統(tǒng)是生命系統(tǒng)的主要系統(tǒng)之一,它通過從不同種類的抗體結(jié)構(gòu)中構(gòu)造自己與非己的非線性自適應(yīng)網(wǎng)絡(luò),在處理復(fù)雜變化的環(huán)境中起著重要的作用。受免疫系統(tǒng)原理啟發(fā)而發(fā)展起來的人工免疫算法具有良好的多樣性、耐受性、分布式并行處理、自組織、自學(xué)習(xí)、自適應(yīng)和魯棒性等特點(diǎn),能夠清晰地表達(dá)學(xué)習(xí)知識(shí),具有內(nèi)容記憶功能,為學(xué)者們提供了很好的進(jìn)化學(xué)習(xí)機(jī)理。它模擬的自然防御功能與異常檢測(cè)系統(tǒng)區(qū)分正常異常的功能有著驚人的相似,并且自身具有良好的學(xué)習(xí)記憶能力和強(qiáng)壯的魯棒性,因而為異常檢測(cè)和分類器構(gòu)造等問題的解決提供了一種新的選擇。 本文首先對(duì)生物免疫系統(tǒng)的一些基本概念、特點(diǎn)及機(jī)理進(jìn)行了描述,介紹了人工免疫算法領(lǐng)域的一些經(jīng)典算法及其優(yōu)缺點(diǎn)。然后針對(duì)傳統(tǒng)的陰性選擇算法中存在的“空洞”問題,模擬抗體多樣性這一特征改進(jìn)算法中抗體單一性的不足,提出了一種改進(jìn)的檢測(cè)器大小可變的免疫異常檢測(cè)算法,并通過實(shí)驗(yàn)分析驗(yàn)證了算法的有效性。針對(duì)單純的免疫分類算法在少量訓(xùn)練數(shù)據(jù)下精度不高問題,引入半監(jiān)督學(xué)習(xí)機(jī)制和投票決策的思想,提出了一種半監(jiān)督免疫分類算法,并給出了實(shí)驗(yàn)分析驗(yàn)證了算法的有效性。
[Abstract]:Immune system is one of the main systems of life system. It plays an important role in dealing with complex changing environment by constructing self and non-self-adaptive network from different kinds of antibody structures. The artificial immune algorithm, inspired by the principle of immune system, has the characteristics of good diversity, tolerance, distributed parallel processing, self-organization, self-learning, self-adaptation and robustness, and can express learning knowledge clearly. It has the function of content memory and provides a good evolutionary learning mechanism for scholars. Its simulated natural defense function is surprisingly similar to the ability of anomaly detection system to distinguish normal anomaly, and it has good learning and memory ability and strong robustness. Therefore, it provides a new choice for solving the problems of anomaly detection and classifier construction. In this paper, some basic concepts, characteristics and mechanisms of biological immune system are described, and some classical algorithms in the field of artificial immune algorithm are introduced, as well as their advantages and disadvantages. Then an improved immune anomaly detection algorithm with variable detector size is proposed to solve the "void" problem in traditional negative selection algorithm and to simulate the lack of single antibody in the improved antibody diversity algorithm. The validity of the algorithm is verified by experimental analysis. Aiming at the problem that the accuracy of the immune classification algorithm is not high under a small amount of training data, a semi-supervised immune classification algorithm is proposed by introducing the idea of semi-supervised learning mechanism and voting decision, and the validity of the algorithm is verified by experimental analysis.
【學(xué)位授予單位】：福建師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：R392.1

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