基于改进的粒子群算法优化反向传播神经网络的热舒适度预测模型

Thermal comfort prediction model based on improved particle swarm optimization-back propagation neural network

针对热舒适度预测是一个复杂的非线性过程,不便于空调的实时控制应用的问题,提出一种基于改进的粒子群优化(PSO)算法优化反向传播(BP)神经网络的热舒适度预测模型。这一预测模型通过采用PSO算法优化BP神经网络的初始权值和阈值,改善了传统BP算法收敛速度慢及对网络初始值敏感的问题。同时,针对标准PSO算法易出现早熟收敛、局部寻优能力弱等缺点,提出了相应改进策略,进一步提高了PSO优化BP神经网络的能力。实验结果表明:与传统BP模型和标准PSO-BP模型相比,基于改进的PSO-BP算法的热舒适度预测模型具有更高的预测精度和更快的收敛速度。

Aiming at the problem that thermal comfort prediction, which is a complicated nonlinear process, can not be applied to real-time control of air conditioning directly, this paper proposed a thermal comfort prediction model based on the improved Particle Swarm Optimization-Back Propagation （PSO-BP） neural network algorithm. By using PSO algorithm to optimize initial weights and thresholds of BP neural network, the problem that traditional BP algorithm converges slowly and is sensitive to the initial value of the network was improved in this prediction model. Meanwhile, for the standard PSO algorithm prone to premature convergence, weak local search capabilities and other shortcomings, this paper put forward some improvement strategies to further enhance the PSO-BP neural network capabilities. The experimental results show that, the thermal comfort prediction model based on the improved PSO-BP neural network algorithm has faster algorithm converges and higher prediction accuracy than the traditional BP model and standard PSO-BP model.