一种改进BP算法在制冷系统仿真中的应用

采用了改进的BP算法，用网络输出关于学习率的线性展开法来自适应优化学习率，BFGS为尺度法来调整权重，对BFGS法作了局部改进以处理可能发生的BFGS公式中分母项为零的情况，以实际制冷系统的热力性能参数实验数据为样本，对比研究了标准BP算法，含学习率优化的最速下降法和含学习率优化的BFGS法的学习过程，结果表明，文中选用的含学习率优化的BFGS法学习效率与学习精度高，且自适应性能好。

Application of an Improved BP Algorithm in the Simulatio n of Refrigeration System

As a common method to train multilayer perceptron (MLP) networks in the simulation of refri geration systems, the standard BP algorithm, is lack of efficiency and adaptive capacity. So, it should be substituted by an improved BP algorithm. After analysis and comparison, the method of linear expansion of MLP's outputs with respect to learning rate was selected to optimize learning rate adaptively, and BFGS variable metric method was selected to tune the weights. BFGS method is improved partly to deal with a possible case in which a denominator in BFGS formula becomes zero. Taking the experimental data of thermodynamic performance of refrigeration systems as training samples, the training courses were compared, which use the standard BP algorithm, steepest descent algorithm with learning rate optimization, and BFGS algorithm with learning rate optimization, respectively. The results show that the BFGS algorithm with the learning rate optimization has the advantages of high learning efficiency, high approximation precision and good adaptive capacity.