摘要
随着计算机技术的发展,通过系统建模在锅炉温度测量系统中的应用已十分广泛。人工神经网络具有良好的并行性、鲁棒性、非线性逼近能力、自适应性和容错能力,应用在锅炉温度测量系统中使锅炉温度测量的结果的精确性得以极大的提高。论文主要介绍了众多模型中的两种模型:BP神经网络模型及RBF神经网络模型,并分析了这两种模型的原理,建立了数学模型,在锅炉温度测量方面具有较好的应用前景。
With the development of Computer technology, Modeling has been widely used to measure technique for furnace temperature. Artificial neural network has excelent parallelism, robutness, nonlinear approximation ability, adaptability and fault-tolearance ability, which improve the accuracy of the result of measuring furnace temperature. This thesis mainly introduces two kinds of models, one is BP neural network model and the other is RBF neural network model. This paper analyzes the principle of these two kinds of models and establishes mathematical model,which provides a promising prospect.
出处
《网络空间安全》
2016年第5期72-74,77,共4页
Cyberspace Security
关键词
锅炉温度测量
计算机
BP神经网络模型
RBF神经网络模型
measuring technique for furnace temperature
computer
bp neural network model
rbf neural network model
