摘要
神经网络与遗传算法相结合在锅炉燃烧优化问题上的应用非常广泛,但是传统的反向传播(BP,Back Propagation)神经网络泛化能力较弱,而贝叶斯正则化方法能有效提高神经网络的泛化能力。应用贝叶斯正则化BP神经网络与遗传算法相结合的方法,对锅炉燃烧多目标优化问题进行研究。通过利用锅炉热态实验数据进行仿真,结果表明:贝叶斯神经网络模型可以很好地预测锅炉的热效率和NOx浓度,结合遗传算法可以对锅炉燃烧实现有效的多目标寻优,为电站的经济环保运行提供理论指导。
Neural network and genetic algorithm have been extensively used in boiler combustion optimization problems. But the traditional Back Propagation neural network's generalization ability is poor. The Bayesian regularization can improve the neural network's generalization ability. A boiler combustion multi-objective optimization method combining Bayesian regularization BP neural network and genetic algorithm(Bayes NN-GA)was researched. A number of field test data from a boiler was used to simulate the Bayesian neural network model. The results show that the thermal efficiency and NOx emissions predicted by the Bayesian neural network model show good agreement with the measured, and the optimal results show that hybrid algorithm by combining Bayesian regularization BP neural network and genetic algorithm can be a good tool to solve the problem of multi-objective optimization of a coal-fired combustion, which provide a theory guidance for the boiler operation in an economic and environmental way.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2015年第8期1790-1795,共6页
Journal of System Simulation
关键词
锅炉
燃烧优化
贝叶斯正则化
神经网络
遗传算法
多目标优化
boiler
combustion optimization
Bayesian regularization
neural network
genetic algorithm
multi-objective optimization
