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研究生:楊元韶
研究生(外文):Yuan-Shao Yang
論文名稱:以類免疫系統為基礎之線上學習類神經模糊系統及其應用
論文名稱(外文):An Artificial Immune System based On-line Learning Neuro-Fuzzy System and Its Applications
指導教授:蘇木春蘇木春引用關係
指導教授(外文):Mu-Chun Su
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:80
中文關鍵詞:模糊系統類神經網路線上學習類免疫系統
外文關鍵詞:fuzzy systemsneural networkson-line learningartificial immune systems
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  • 被引用被引用:1
  • 點閱點閱:136
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在許多應用中,系統需要在不會破壞舊有的資訊的前提下,能夠快速地學習新的資訊和微調舊有的資訊,這就是所謂的線上學習的特性。對於一個有效的辨識系統來說,能具備線上學習的特性是相當吸引人的。
人體免疫系統是十分地複雜的,它的許多特性與機制吸引了許多的研究者注意,近幾年來,有很多類免疫系統(AIS)的產生,這些不同的類免疫系統採用了在人體免疫系統裡一些不同的機制,來解決所要處理的問題。
在本論文中,我們提出了一個新的線上學習的類神經模糊系統,採用了人體免疫系統中的某一些特性,我們稱此類神經模糊系統為“以類免疫系統為基礎的類神經模糊系統” 。此系統在學習的過程中,能夠以漸進式的方式來建構系統,並可以應用在圖形識別與函數逼近的問題上。除了用數個人造資料集,並且也以一些真實的資料集來測試其效能,尤其特別的是,我們也將此系統應用於背光影像的補償處理。
In some applications, systems should be able to learn new classes and refine existing classes quickly and without destroying old class information. This property is referred to as on-line learning and it is a very appealing property for an efficient pattern recognition system.
The immune system is a highly complicated system. Many properties of immune systems attract a great amount of attentions from compute scientists and engineers. In recently years, many artificial immune systems have been proposed. Different artificial immunes systems are inspired by different subsets of the available metaphors.
In this paper, we present an on-line learning neuro-fuzzy system which was inspired by part of the mechanisms in immune systems. We name the proposed neuro-fuzzy system as the artificial immune system based neuro-fuzzy system (AISNFS). During the learning procedure, a neuro-fuzzy system can be incrementally constructed. AISNFS can be applied in pattern recognition and function approximation problems. The performance of the propose AISNFS is evaluated by not only some artificial data sets but also some real data sets. Especially, we apply the proposed AISNFS in the compensation of backlight images.
摘要……………………………………………………………………………I
Abstract………………………………………………………………………III
誌謝……………………………………………………………………………V
目錄……………………………………………………………………………VI
圖目錄…………………………………………………………………………IX
表目錄…………………………………………………………………………XI
第一章 緒論……………………………………………………1
1.1 研究動機………………………………………………1
1.2 論文架構………………………………………………2
第二章 線上學習系統探討……………………………………3
2.1 模糊適應共振理論映射圖……………………………………3
2.1.1 網路架構………………………………………………3
2.2.2 模糊適應共振理論演算法……………………………4
2.2 簡化模糊適應共振理論映射圖………………………7
2.2.1 網路架構………………………………………………7
2.2.2 學習演算法……………………………………………8
2.3 模糊最大最小類神經網路……………………………10
2.3.1 網路架構………………………………………………10
2.3.2 學習演算法……………………………………………11
2.4 改良式簡化模糊適應共振理論映射圖………………12
2.4.1 網路架構………………………………………………12
2.4.2 學習演算法……………………………………………13
2.5 線上系統的特性分析…………………………………15
第三章 以類免疫系統為基礎的類神經模糊系統……………18
3.1 免疫系統介紹…………………………………………18
3.2 AISNFS與免疫系統……………………………………21
3.3 AISNFS之圖形辨別……………………………………23
3.3.1 AISNFS對於圖形識別之網路架構……………………23
3.3.2 AISNFS對於圖形識別之學習演算法…………………23
3.3.3 網路測試………………………………………………27
3.4 AISNFS之函數逼近……………………………………28
3.4.1 函數逼近之網路架構…………………………………28
3.4.2 AISNFS對於函數逼近的學習演算法…………………29
3.4.3 網路測試………………………………………………33
3.5 實驗模擬………………………………………………34
3.5.1 二維資料之579……………………………………… 34
3.5.2 雙螺旋資料……………………………………………36
3.5.3 語者辨識資料集………………………………………39
3.5.4 鳶花尾資料集(Iris)…………………………………41
3.5.5 UCI 資料庫……………………………………………42
3.5.5.1 教學評估資料集………………………… 43
3.5.5.2 大地衛星資料…………………………… 44
3.5.5.3 避孕方法的選擇………………………… 45
3.5.5.4 肝癌測試………………………………… 46
3.5.5.5 乳癌測試………………………………… 47
3.5.6 函數逼近測試…………………………………………48
第四章 背光影像補償…………………………………………51
4.1 背光影像研究探討……………………… 51
4.2 背光影像自動補償演算法……………… 54
4.3 實驗結果………………………………… 61
第五章 結論與建議……………………………………………73
參考文獻………………………………………………………………………74
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