摘要
提出了一种应用人工神经网络进行流型判别的新方法。采用了自适应梯度下降法和改进模拟退火法等措施 ,加快了BP网络的收敛速度 ,增强了其跳出局部极小值的能力 ,提高了神经网络模拟非线性系统的能力 ,并分析了可作为流型判别用的神经网络的输入参数、输出参数。为了证实该方法的可行性 ,应用前人的实验数据进行验证。
A new method for the discrimination of flow patterns is proposed in this paper, which is based on the use of an artificial neural network. With the help of a self adaptive gradient reduction method and an improved simulation annealing approach, etc the convergence rate of a BP network can be accelerated. There emerged, as a result, an enhancement in the network's ability to avoid a local minimum magnitude, which contributes to an increased capability of the network to simulate a nonlinear system. In addition, an analysis was made of the network input and output parameters, which can be used for pattern discrimination. To demonstrate the feasibility of the recommended method, the authors have employed the experimental data of scholars forerunners to check and verify their work procedures. It has been proved that the above method can be assessed as a very effective one for the discrimination of two phase and multi phase flow patterns.
出处
《热能动力工程》
CAS
CSCD
北大核心
2001年第1期63-65,90,共4页
Journal of Engineering for Thermal Energy and Power
基金
国家自然科学基金资助项目! (5 9995 46 0 )
关键词
BP神经网络
两相流
多相流
流型判别
传热
neural network, two phase flow, multi phase flow, flow pattern discriminatL
