基于鸟群算法优化BP神经网络的热舒适度预测

热舒适度是室内环境舒适性的评价指标，由于热舒适度的计算是一个复杂的非线性迭代过程，不便应用于空调实时控制系统中，为解决这一问题，可利用BP神经网络算法对热舒适度进行预测.但为了改善传统BP神经网络收敛速度慢的问题，将采用鸟群算法（BSA）来优化BP神经网络初始的权值与阈值.最后，将BSA算法与相近的粒子群算法（PSO）进行对比分析，并利用MATLAB软件进行仿真，使BSA-BP预测模型的仿真结果与基本的BP神经网络预测模型、PSO-BP预测模型的仿真结果进行对比分析.结果表明，BSA-BP预测模型具有较快的收敛速度和较高的预测精度.

Thermal Comfort Prediction Analysis Based on BP Neural Network Optimized by Bird Swarm Algorithm

Thermal comfort is an evaluation index of indoor environment comfort. Since the calculation of thermal comfort is a complex nonlinear iterative process, it is inconvenient to apply to air conditioning real-time control system. In order to solve this problem, use the BP neural network algorithm to predict thermal comfort. However, in order to improve the slow convergence rate of traditional BP neural network, the bird swarm algorithm (BSA) is used to optimize the initial weights and thresholds of BP neural network. Finally, the BSA algorithm is compared with the similar particle swarm optimization (PSO) algorithm. MATLAB software is used to simulate, and the simulation results of BSA-BP prediction model are compared with the simulation results of the basic BP neural network prediction model and the PSO-BP prediction model. The results show that the BSA-BP algorithm has faster convergence speed and higher prediction accuracy.