CAJ | 학술논문

针对汽油机进气流量的多维非线性特性，提出了一种混沌径向基（RBF）神经网络的汽油机进气流量预测模型。证明了汽油机进气流量时间序列具有混沌特性，对采集的原始数据进行相空间重构，利用RBF神经网络对重构后的数据进行训练和预测，并利用混沌算法确定输出层连接权值和隐含层高斯函数径向基中心，使其达到全局最优，加快了RBF神经网络的收敛速度。仿真结果表明，与空气进气流量平均值模型、RBF神经网络预测模型比较，该模型具有更高的预测精度，为精确及时测试汽油机进气流量提供了一种全新的软件测量方法。
침대기유궤진기류량적다유비선성특성，제출료일충혼돈경향기（RBF）신경망락적기유궤진기류량예측모형。증명료기유궤진기류량시간서렬구유혼돈특성，대채집적원시수거진행상공간중구，이용RBF신경망락대중구후적수거진행훈련화예측，병이용혼돈산법학정수출층련접권치화은함층고사함수경향기중심，사기체도전국최우，가쾌료RBF신경망락적수렴속도。방진결과표명，여공기진기류량평균치모형、RBF신경망락예측모형비교，해모형구유경고적예측정도，위정학급시측시기유궤진기류량제공료일충전신적연건측량방법。
A soft predictive model based on Chaos-RBF neural network is proposed for the intake air flow of gasoline engine as its multidimensional nonlinear characteristics. The engine air intake flow time series with chaotic characteristics have been proved;the phase space of the original data has also been reconstructed before using RBF neural network to train and predict. And then, the result has been compared with the air inlet flow average model and RBF neural network forecasting model. Chaos algorithm is used to determine and optimal the implied Gaussian radial basis function center and the out put layer connection weights, in order to accelerate the convergence rate of RBF neural network. The simulation results show that this model is a new method to measure the intake air flow of the engine with more accuracy and timeless, which is superior to the intake air flow average model and RBF neural network prediction model.