贝叶斯神经网络在蒸气管网预测中的应用

蒸气管网是具有典型大时滞特点的非线性网络系统结构，提高管网运行预测能力，对管网的安全高效运行有很好的指导意义。贝叶斯神经网络具有良好的泛化能力和准确计算能力，在网络目标函数中引入表示网络结构复杂性的惩罚项，以便能够在训练优化过程中降低网络结构的复杂性，达到避免网络过拟合的目的。实例验证表明，模型计算结果和泛化能力均有良好表现，优于传统BP算法计算性能，可提高企业蒸气管网运行管理水平，对流程工业节能减排建设有一定的帮助。

Application of Bayesian Neural Networks in Steam Pipe Network Prediction

Steam pipe network is typical non-linear network structure.It is instructive to increase predictive capacity to steam pipe network highly effective.Bayesian neural networks is well generalization and better calculate capabili-ty.A penalty term which could be interpreted as an indication of the complexity of the network was introduced into the obj ective function to present the occurrence of “overfitting”.Compared with the conventional BP neural net-work,it has the advantages of faster convergence rate,higher stability and ability for generalization.The result had certain guided signification to accelerate the construction of hybrid process energy-saving and emission-reduction.