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題 名 | 集水區水質變遷特性之獨立成分分析=Independent Component Analysis of Water Quality Change in Watershed |
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作 者 | 楊士興; | 書刊名 | 中國地理學會會刊 |
卷 期 | 28 2000.07[民89.07] |
頁 次 | 頁175-203 |
分類號 | 436.124 |
關鍵詞 | 獨立成分分析; 主成分分析; 特徵獨特性; Independent component analysis; ICA; Principle component analysis; PCA; The features of the independent components; |
語 文 | 中文(Chinese) |
中文摘要 | 在面對大量資料時,其內涵特徵之型態不得而知,欲求其獨特性的工作就和當今日漸盛行之學科:資料開採(data mini,DM) ,類神經網路(neural networks,NN),機器學習(machine learning,ML),盲目來源區分(blind sources separation,BSS)等之基本理念有其相通之處,尤其是BSS,通常都是應用在演講辯識(speech recognition),資料連繫(data communication),感測器訊號處理(sensor signal processing) ,及醫藥科學(medical science)等方面,但這些研究理念卻至今未跨足與環境科學的研究結合在一起;本人的研究動機在尋求新方法來研究水資源這樣的多變數分析問題,從PCA的分析中,發現其不足,然後發現ICA為尋求特徵獨特性的新理念,至此才以環境水質的ICA研究為方法,更以探討ICA在地理環境議題研究上的功能優劣程度為研究目的。 獨立成分分析(ICA)可說是一個新的分析法,其理念為主成分分析( PCA) 之延續,PCA是以共變異數之計算為基礎,來取得多變數分析向度之降低,而ICA的目的則在探求多變數資料內含因子型態之獨立特性;在進行PCA之時,無法解釋其PC之真正含義,所指為何?不得而知;因此,以ICA來求獨立成分之獨特性質,正可補足PCA之缺點,而且,以PCA為ICA之前置處理程序,其dimension reduction的功能就非常重要。 經本研究對美國的二十一個主要水文分區的水質資料進行ICA分析研究,採用FastICA為運算程式,以平均降階程度百分比來代表影響水質之環境營力的複雜程度,其結果相當令人滿意,大致能吻合主要水文分區內的各種環境條件。而且,ICA還能清楚地將各個水質參數之值轉化為各IC與加權值之函數,能對IC之特徵作相當清楚的解釋,能方便地尋找到所代表的環境營力。 |
英文摘要 | In order to get real independence of the unknown feature patterns, ICA works like the jobs in the processes of data mining (DM), neural networks (NN), machine learning (ML) and blind source separation (BSS). The researches concerning BSS are about speech recognition, data communication, sensor signal processing, and medical science. It's the same job that ICA want to do, and we have advanced ideas to run ICA in the studies of environmental science. It's multivariate analysis to solve the problems of the water resources. What 1 want to do is to find out a new approach to do that in my study here. Independent Component Analysis (ICA) is a new analysis tool. It's called advanced Principle component Analysis. Computation of covariance is the basis of the PCA to reduce the dimensions of the multivariate analysis. It's the goal of ICA to get the independence to the unknown patterns of the multivariate data. Sometimes it is difficult to point out what principle components are and what do they mean. I got the USGS water quality data from the internet, including 21 major hydrologic basins of the USA. There's very good results of running ICA in my study to show out the independence of the IC to be the functions of water quality variables and weights of independent components. We could explain what they are and how they act in the environment by looking into the features of the independent components. |
本系統中英文摘要資訊取自各篇刊載內容。