采用非结构网格上的SIMPLE(Semi-implicit Method for Pressure-linked Equations)算法、k-ε湍流模型求解了三维不可压Navier-stokes方程,对低速不可压粘性流场进行数值模拟,在求解压强方程时采用共轭梯度方法,求解其它变量方程时,采...采用非结构网格上的SIMPLE(Semi-implicit Method for Pressure-linked Equations)算法、k-ε湍流模型求解了三维不可压Navier-stokes方程,对低速不可压粘性流场进行数值模拟,在求解压强方程时采用共轭梯度方法,求解其它变量方程时,采用预处理的BICG(biconjugate gradients)算法。对NACA0012翼型绕流流场和飞艇绕流流场进行数值模拟并对结果进行分析,取得较好的结果。展开更多
A multilayer perceptron neural network system is established to support the diagnosis for five most common heart diseases (coronary heart disease, rheumatic valvular heart disease, hypertension, chronic cor pulmonale ...A multilayer perceptron neural network system is established to support the diagnosis for five most common heart diseases (coronary heart disease, rheumatic valvular heart disease, hypertension, chronic cor pulmonale and congenital heart disease). Momentum term, adaptive learning rate, the forgetting mechanics, and conjugate gradients method are introduced to improve the basic BP algorithm aiming to speed up the convergence of the BP algorithm and enhance the accuracy for diagnosis. A heart disease database consisting of 352 samples is applied to the training and testing courses of the system. The performance of the system is assessed by cross-validation method. It is found that as the basic BP algorithm is improved step by step, the convergence speed and the classification accuracy of the network are enhanced, and the system has great application prospect in supporting heart diseases diagnosis.展开更多
基金Supported by the National Natural Science Foundation of China under Grant(11371075)the research innovation program of Hunan province of China for postgraduate students under Grant(CX2015B374)
基金the Natural Science Foundation of China (No. 30070211).
文摘A multilayer perceptron neural network system is established to support the diagnosis for five most common heart diseases (coronary heart disease, rheumatic valvular heart disease, hypertension, chronic cor pulmonale and congenital heart disease). Momentum term, adaptive learning rate, the forgetting mechanics, and conjugate gradients method are introduced to improve the basic BP algorithm aiming to speed up the convergence of the BP algorithm and enhance the accuracy for diagnosis. A heart disease database consisting of 352 samples is applied to the training and testing courses of the system. The performance of the system is assessed by cross-validation method. It is found that as the basic BP algorithm is improved step by step, the convergence speed and the classification accuracy of the network are enhanced, and the system has great application prospect in supporting heart diseases diagnosis.
