畢業(yè)論文 基于神經(jīng)網(wǎng)絡(luò)的空氣質(zhì)量檢測.doc
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畢業(yè)論文 基于神經(jīng)網(wǎng)絡(luò)的空氣質(zhì)量檢測,目錄摘要1關(guān)鍵詞2abstract2key words2引言21 bp神經(jīng)網(wǎng)絡(luò)概述31.1 基本原理31.2 bp算法學(xué)習(xí)過程42空氣質(zhì)量檢測模型的建立62.1樣本數(shù)據(jù)62.1.1收集和整理分組62.1.2輸入/輸出變量的確定及其數(shù)據(jù)的預(yù)處理72.2神經(jīng)網(wǎng)絡(luò)拓撲結(jié)構(gòu)的確定72.2.1隱層數(shù)72.2.2隱層節(jié)點數(shù)72.3...
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目錄
摘要 1
關(guān)鍵詞 2
ABSTRACT 2
KEY WORDS 2
引言 2
1 BP神經(jīng)網(wǎng)絡(luò)概述 3
1.1 基本原理 3
1.2 BP算法學(xué)習(xí)過程 4
2 空氣質(zhì)量檢測模型的建立 6
2.1樣本數(shù)據(jù) 6
2.1.1收集和整理分組 6
2.1.2輸入/輸出變量的確定及其數(shù)據(jù)的預(yù)處理 7
2.2神經(jīng)網(wǎng)絡(luò)拓撲結(jié)構(gòu)的確定 7
2.2.1隱層數(shù) 7
2.2.2隱層節(jié)點數(shù) 7
2.3神經(jīng)網(wǎng)絡(luò)的訓(xùn)練 8
2.4神經(jīng)網(wǎng)絡(luò)模型參數(shù)的確定 10
2.4.1隱層的數(shù)目 10
2.4.2隱層神經(jīng)元數(shù)的選擇 10
2.4.3 學(xué)習(xí)率 和動量因子 13
2.4.4 初始權(quán)值的選擇 13
2.4.5 收斂誤差界值Emin 13
2.4.6輸入數(shù)據(jù)的預(yù)處理 13
3 MATLAB實現(xiàn)和結(jié)果分析 14
3.1 MATLAB神經(jīng)網(wǎng)絡(luò)工具箱的應(yīng)用 14
3.2 基于MATLAB的BP算法的實現(xiàn)過程 14
3.3訓(xùn)練神經(jīng)網(wǎng)絡(luò) 15
4結(jié)語 23
致謝 23
參考文獻 23
基于神經(jīng)網(wǎng)絡(luò)的空氣質(zhì)量檢測
05級自動化 臧鵬娟
指導(dǎo)教師 史麗紅
摘要：空氣質(zhì)量的好壞反映了空氣污染程度，它是依據(jù)空氣中污染物濃度的高低來判斷的。污染物濃度由于受風(fēng)向、風(fēng)速、氣溫、濕度、污染源排放情況等多種因素的影響，使得空氣質(zhì)量問題具有很大的不確定性和一定的復(fù)雜性。神經(jīng)網(wǎng)絡(luò)作為一種描述和刻畫非線性的強有力工具,具有較強的自學(xué)習(xí)、自組織、自適應(yīng)能力等特點,特別適合于對具有多因素性、不確定性、隨機性、非線性和隨時間變化特性的對象進行研究。本文基于神經(jīng)網(wǎng)絡(luò)的BP算法，利用MATLAB神經(jīng)網(wǎng)絡(luò)工具箱建立了空氣質(zhì)量模型。文中，采用MATLAB 的rand()函數(shù)在各級評價標準內(nèi)按隨機均勻分布方式內(nèi)插生成訓(xùn)練樣本和檢驗樣本，利用premnmx()函數(shù)對數(shù)據(jù)進行預(yù)處理，調(diào)用激活函數(shù)對網(wǎng)絡(luò)權(quán)值進行訓(xùn)練，并同其他評價方法比較，取得了良好的評價結(jié)果。同時表明此方法具有一定的客觀性和積極性。
關(guān)鍵詞：BP神經(jīng)網(wǎng)絡(luò)；空氣質(zhì)量； MATLAB神經(jīng)網(wǎng)絡(luò)工具箱
The detection of air quality based on neural network
Student majoring in automation Zang Pengjuan
Tutor Shi Lihong
Abstract：The quality of air quality reflects the extent of air pollution, which is based on the concentration of pollutants in the air to determine the level of the air. Concentration of pollutants due to wind direction, wind speed, air temperature, humidity, pollutant emissions and other factors, makes the issue of air quality is a great uncertainty and a certain degree of complexity. Neural network description and characterization as a powerful tool for non-linear phenomenon, with strong self-learning, self-organization, the characteristics of adaptive capacity, especially suitable for multi-factor, uncertainty, randomness, non-linear and time-varying characteristics of the object of research. This design bases on the BP neural network algorithm, using MATLAB neural network toolbox to establish air quality model. In this text, using the MATLAB’s rand () function at all levels within the eva luation criteria uniformly distributes random interpolation methods to generate training samples and the samples tested. Then the paper uses premnmx () function on the data pre-processing, and transfers activation function of network weights training and compares with other eva luation methods, and achieved good results which indicate the objectivity and enthusiasm of the design.
Key words：BP neural network; Air quality; MATLAB neural network toolbox
摘要 1
關(guān)鍵詞 2
ABSTRACT 2
KEY WORDS 2
引言 2
1 BP神經(jīng)網(wǎng)絡(luò)概述 3
1.1 基本原理 3
1.2 BP算法學(xué)習(xí)過程 4
2 空氣質(zhì)量檢測模型的建立 6
2.1樣本數(shù)據(jù) 6
2.1.1收集和整理分組 6
2.1.2輸入/輸出變量的確定及其數(shù)據(jù)的預(yù)處理 7
2.2神經(jīng)網(wǎng)絡(luò)拓撲結(jié)構(gòu)的確定 7
2.2.1隱層數(shù) 7
2.2.2隱層節(jié)點數(shù) 7
2.3神經(jīng)網(wǎng)絡(luò)的訓(xùn)練 8
2.4神經(jīng)網(wǎng)絡(luò)模型參數(shù)的確定 10
2.4.1隱層的數(shù)目 10
2.4.2隱層神經(jīng)元數(shù)的選擇 10
2.4.3 學(xué)習(xí)率 和動量因子 13
2.4.4 初始權(quán)值的選擇 13
2.4.5 收斂誤差界值Emin 13
2.4.6輸入數(shù)據(jù)的預(yù)處理 13
3 MATLAB實現(xiàn)和結(jié)果分析 14
3.1 MATLAB神經(jīng)網(wǎng)絡(luò)工具箱的應(yīng)用 14
3.2 基于MATLAB的BP算法的實現(xiàn)過程 14
3.3訓(xùn)練神經(jīng)網(wǎng)絡(luò) 15
4結(jié)語 23
致謝 23
參考文獻 23
基于神經(jīng)網(wǎng)絡(luò)的空氣質(zhì)量檢測
05級自動化 臧鵬娟
指導(dǎo)教師 史麗紅
摘要：空氣質(zhì)量的好壞反映了空氣污染程度，它是依據(jù)空氣中污染物濃度的高低來判斷的。污染物濃度由于受風(fēng)向、風(fēng)速、氣溫、濕度、污染源排放情況等多種因素的影響，使得空氣質(zhì)量問題具有很大的不確定性和一定的復(fù)雜性。神經(jīng)網(wǎng)絡(luò)作為一種描述和刻畫非線性的強有力工具,具有較強的自學(xué)習(xí)、自組織、自適應(yīng)能力等特點,特別適合于對具有多因素性、不確定性、隨機性、非線性和隨時間變化特性的對象進行研究。本文基于神經(jīng)網(wǎng)絡(luò)的BP算法，利用MATLAB神經(jīng)網(wǎng)絡(luò)工具箱建立了空氣質(zhì)量模型。文中，采用MATLAB 的rand()函數(shù)在各級評價標準內(nèi)按隨機均勻分布方式內(nèi)插生成訓(xùn)練樣本和檢驗樣本，利用premnmx()函數(shù)對數(shù)據(jù)進行預(yù)處理，調(diào)用激活函數(shù)對網(wǎng)絡(luò)權(quán)值進行訓(xùn)練，并同其他評價方法比較，取得了良好的評價結(jié)果。同時表明此方法具有一定的客觀性和積極性。
關(guān)鍵詞：BP神經(jīng)網(wǎng)絡(luò)；空氣質(zhì)量； MATLAB神經(jīng)網(wǎng)絡(luò)工具箱
The detection of air quality based on neural network
Student majoring in automation Zang Pengjuan
Tutor Shi Lihong
Abstract：The quality of air quality reflects the extent of air pollution, which is based on the concentration of pollutants in the air to determine the level of the air. Concentration of pollutants due to wind direction, wind speed, air temperature, humidity, pollutant emissions and other factors, makes the issue of air quality is a great uncertainty and a certain degree of complexity. Neural network description and characterization as a powerful tool for non-linear phenomenon, with strong self-learning, self-organization, the characteristics of adaptive capacity, especially suitable for multi-factor, uncertainty, randomness, non-linear and time-varying characteristics of the object of research. This design bases on the BP neural network algorithm, using MATLAB neural network toolbox to establish air quality model. In this text, using the MATLAB’s rand () function at all levels within the eva luation criteria uniformly distributes random interpolation methods to generate training samples and the samples tested. Then the paper uses premnmx () function on the data pre-processing, and transfers activation function of network weights training and compares with other eva luation methods, and achieved good results which indicate the objectivity and enthusiasm of the design.
Key words：BP neural network; Air quality; MATLAB neural network toolbox
