基于径向基神经网络改进算法优化锅炉燃烧效率  

Boiler combustion efficiency optimization based on improved radial basis neural network

作　　者：靳玉萍[1] 党婕[1] 

机构地区：[1]西安科技大学计算机科学与技术学院,西安710054

出　　处：《计算机应用》2013年第6期1771-1773,1779,共4页journal of Computer Applications

摘　　要：为了提高径向基神经网络训练精度,提出一种混合优化算法。该算法利用粒子群优化算法全局搜索能力强的特点,避免了K均值算法受初始点选择的不利影响,提高了网络中心的搜索速度;同时采用动态权值算法避免径向基神经网络可能出现的病态问题,进一步提高网络的逼近能力。锅炉燃烧实例表明了改进算法的有效性和实用性。In order to improve the training accuracy of radial basis neural network, thin paper proposed a hybrid optimization algorithm. The algorithm used the strong global search ability of Particle Swarm Optimization （PSO） algorithm to avoid the adverse effect by choosing initial point in the K-means algorithm, thus improving the network center search speed. Meanwhile, the dynamic weight algorithm was used to avoid the ill-posed problem, and to further improve the network approximation ability. The boiler combustion instance indicates that the improved algorithm is efficient and practical.

关 键 词：锅炉燃烧 粒子群优化算法 K均值算法 变梯度算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]