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以類神經技術為基礎之柴油車輛油耗預估研究
http://ir.ncue.edu.tw/ir/handle/987654321/18384
title: 以類神經技術為基礎之柴油車輛油耗預估研究 abstract: 本篇報告提出了一種採用人工神經網絡技術為基礎的柴油車輛燃料消耗預測系統。本系統由三個主要部分組成:油耗數據搜集及分類,油耗的預估模式建立和預測性能的分析。在實際的行駛狀況下,柴油汽車的燃料消耗受到多種因素的影響。然而在本系統中的燃料消耗的影響因素簡化設定為車輛廠牌,車輛型式,車輛的重量,車輛類型,變速箱型式,共軌系統,渦輪增壓系統和傳輸模式。根據當前的燃料消耗標準,八項條件作為輸入,用於神經網絡的訓練和燃料消耗量的預測。在測試的數據中使用人工神經網絡的倒傳遞神經網絡(BP神經網絡)和徑向基底函數神經網絡(RBF神經網絡)。由測試的結果顯示,使用類神經網路技術於柴油車輛的燃料消耗量預測是有一定的準確度。在方法比較上,徑向基底函數神經網絡的效果比較傳統的倒傳遞神經網絡效果較佳。
<br>基於機車通過噪音為車種辨識之研究
http://ir.ncue.edu.tw/ir/handle/987654321/18383
title: 基於機車通過噪音為車種辨識之研究 abstract: 由於機車在不同行駛條件下會產生不同之通過噪音,本研究試圖利用經驗模態分解方法(EMD)結合類神經網路技術應用在機車種類的辨識上,期望能分辨出不同機車在不同時速下之行駛狀況。在實驗數據取得方面,利用環保署的機動車輛通過噪音測試之方法,將聲音訊號錄製。在訊號處理及分類上,先利用經驗模態分解技術將聲音訊號藉由一組有限的內建模態函數來呈現。再將信號內部變化的時間尺度做為能量與頻率的直接析出,並計算各函數中的能量分布情形當作其特徵。在訊號分類上,利用倒傳遞類神經(BPNN)與廣義回歸類神經(GRNN)做訓練,進而連到車輛識別的目的,並比較兩種類神經的性能。從實驗結果證明EMD特徵擷取方法結合GRNN分類器有良好的車種識別能力。
<br>Finger-Vein Pattern Identification Using SVM and Neural Network Technique
http://ir.ncue.edu.tw/ir/handle/987654321/18381
title: Finger-Vein Pattern Identification Using SVM and Neural Network Technique abstract: This paper presents a support vector machine (SVM) technique for finger-vein pattern identification in a personal identification system. Finger-vein pattern identification is one of the most secure and convenient techniques for personal identification. In the proposed system, the finger-vein pattern is captured by infrared LED and a CCD camera because the vein pattern is not easily observed in visible light. The proposed verification system consists of image pre-processing and pattern classification. In the work, principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the image pre-processing as dimension reduction and feature extraction. For pattern classification, this system used an SVM and adaptive neuro-fuzzy inference system (ANFIS). The PCA method is used to remove noise residing in the discarded dimensions and retain the main feature by LDA. The features are then used in pattern classification and identification. The accuracy of classification using SVM is 98% and only takes 0.015 s. The result shows a superior performance to the artificial neural network of ANFIS in the proposed system
<br>A Forecasting System for Car Fuel Consumption Using a Radial Basis Function Neural Network
http://ir.ncue.edu.tw/ir/handle/987654321/18382
title: A Forecasting System for Car Fuel Consumption Using a Radial Basis Function Neural Network abstract: A predictive system for car fuel consumption using a radial basis function (RBF) neural network is proposed in this paper. The proposed work consists of three parts: information acquisition, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors affecting the fuel consumption of a car in a practical drive procedure, in the present system the relevant factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In fuel consumption forecasting, to verify the effect of the proposed RBF neural network predictive system, an artificial neural network with a back-propagation (BP) neural network is compared with an RBF neural network for car fuel consumption prediction. The prediction results demonstrated the proposed system using the neural network is effective and the performance is satisfactory in terms of fuel consumption prediction.
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