本文關(guān)鍵詞： 電動(dòng)車動(dòng)力總成 噪聲品質(zhì) 粒子群優(yōu)化 支持向量機(jī) 敏感頻帶能量比　出處：《西安交通大學(xué)學(xué)報(bào)》2016年01期 　論文類型：期刊論文

【摘要】：為了實(shí)現(xiàn)電動(dòng)車動(dòng)力總成噪聲品質(zhì)的預(yù)測(cè),以某集中驅(qū)動(dòng)式電動(dòng)車為例,在考慮動(dòng)力總成輻射噪聲品質(zhì)頻域特性和已設(shè)立的敏感頻帶能量比這一客觀評(píng)價(jià)參數(shù)的基礎(chǔ)上進(jìn)行了心理聲學(xué)參數(shù),即響度、尖銳度、粗糙度、抖動(dòng)度、語(yǔ)音清晰度等與主觀評(píng)價(jià)的相關(guān)性分析,由此建立了電動(dòng)車動(dòng)力總成噪聲品質(zhì)粒子群支持向量機(jī)預(yù)測(cè)模型,內(nèi)容涉及采用支持向量機(jī)建立噪聲品質(zhì)預(yù)測(cè)模型、利用粒子群優(yōu)化算法對(duì)向量基懲罰因子及核函數(shù)參數(shù)進(jìn)行優(yōu)化,最后驗(yàn)證了敏感頻帶能量比評(píng)價(jià)參數(shù)的有效性。研究結(jié)果表明:敏感頻帶能量比與主觀評(píng)價(jià)相關(guān)度達(dá)到0.946,可以較好地反映主觀感受;基于粒子群支持向量機(jī)的噪聲品質(zhì)預(yù)測(cè)模型的平均相對(duì)誤差和最大相對(duì)誤差分別為2.0%和6.7%,表明以敏感頻帶能量比作為輸入特征的粒子群優(yōu)化支持向量機(jī)模型,在電動(dòng)車動(dòng)力總成噪聲品質(zhì)的預(yù)測(cè)精度上優(yōu)于基于遺傳算法優(yōu)化及網(wǎng)格搜索優(yōu)化的預(yù)測(cè)模型。
[Abstract]:In order to predict the noise quality of electric vehicle powertrain, a centralized drive electric vehicle is taken as an example. On the basis of considering the frequency domain characteristic of radiated noise quality of power assembly and the objective evaluation parameter of sensitive band energy ratio, the psychoacoustic parameters, namely loudness, sharpness, roughness and jitter, are carried out. Based on the correlation analysis between speech articulation and subjective evaluation, a prediction model of noise quality particle swarm optimization support vector machine (PSO) for electric vehicle powertrain is established, which involves the establishment of noise quality prediction model using support vector machine (SVM). The particle swarm optimization algorithm is used to optimize the vector basis penalty factor and kernel function parameters. Finally, the validity of the evaluation parameters of the sensitive band energy ratio is verified. The results show that the correlation degree between the sensitive band energy ratio and the subjective evaluation is 0.946, which can better reflect the subjective feeling. The average relative error and maximum relative error of the noise quality prediction model based on particle swarm optimization support vector machine are 2.0% and 6.7% respectively. It is shown that the particle swarm optimization support vector machine model is based on the sensitive band energy ratio as the input feature. The prediction accuracy of the noise quality of electric vehicle powertrain is better than the prediction model based on genetic algorithm optimization and grid search optimization.
【作者單位】： 同濟(jì)大學(xué)新能源汽車工程中心;同濟(jì)大學(xué)汽車學(xué)院;同濟(jì)大學(xué)中德學(xué)院;
【基金】：國(guó)家“863計(jì)劃”資助項(xiàng)目(20U11AA11A265) 國(guó)家自然科學(xué)基金資助項(xiàng)目(51205290) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目(1700219118)
【分類號(hào)】：U469.72
【正文快照】： 大量的聲學(xué)研究發(fā)現(xiàn),A計(jì)權(quán)聲壓級(jí)不能完全反映人對(duì)噪聲的主觀感受。在這種情況下,噪聲品質(zhì)這個(gè)現(xiàn)代噪聲研究的全新概念應(yīng)運(yùn)而生,它指出人對(duì)噪聲的感覺是受心理和生理因素的共同影響。噪聲品質(zhì)的準(zhǔn)確預(yù)測(cè)是對(duì)產(chǎn)品聲學(xué)優(yōu)化設(shè)計(jì)的重要前提。噪聲品質(zhì)預(yù)測(cè)研究包括車內(nèi)噪聲[1-2]、

【共引文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張冬妍;張春妍;尹文芳;;基于KPCA和PSO-SVM的木材干燥過程在線優(yōu)化建模研究[J];安徽農(nóng)業(yè)科學(xué);2014年07期

2 田建波;程U，

本文編號(hào)：1461994