PSO-LM-BP算法预测控制空调系统TRNSYS建筑节能针对夏季建筑空调系统能耗高,碳排放量大的问题,提出一种改进后的LM-BP神经网络,并结合粒子群优化算法,建立基于建筑历史数据的空调系统冷负荷神经网络预测模型,通过粒子群算法寻优得到不同负荷率下冷源系统各设备的最优运行参数.以青岛某一公建项目为例,采用TRNSYS仿真模拟空...
利用保存好的PSO‑LM‑BP神经网络带钢冷轧横向厚差预报模型,根据不同带钢热轧来料断面轮廓参数,快速批量预测其冷轧后横向厚差,给出热轧来料断面轮廓参数与冷轧横向厚差之间的变化规律;S42:利用保存好的PSO‑LM‑BP神经网络带钢冷轧横向厚差预报模型,针对生产现场中常见带钢热轧来料断面轮廓情况,预测其冷轧后横向厚...
By collecting the load current of the controller in real time, the model between the current value and the corresponding screw sleeve rotation angle is established offline by using the improved BP neural network. The relative value of the tightness is o...
针对非线性系统,提出了一种基于BP神经网络的预测控制方法.以BP神经网络建立多步预测模型并预测系统输出值,用LM (Levenberg-Marquardt)算法和PSO (Particle Swarm Optimization)算法组合的混合算法对目标性能指标函数进行滚动优化求解,得到非线性系统的最优控制量;利用误差修正参考输入法实现反馈矫正.通过将粒子群算法引入LM...
Khan K, Sahai A (2012) A comparison of BA,GA, PSO, BP and LM for training feed forward neural networks in e-learning context. Int J Intel Sys Appl 4(7): 23-29Khan K, Sahai A (2012) A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-...