Use adaptation sequence modified inertia weight particle swarm optimization 适应度排序改进惯性权重的粒子群算法
Two kinds of lumped parameter dynamical models most used for boiler uniphase heated tubes are modified by inertia compensation, and so two kinds of high precision modified lumped parameter dynamical models are developed in this paper. 通过惯性补偿的方法,对锅炉单相受热管的两类常用的集总参数模型进行动态修正,得到两类高精度的集总参数动态修正模型;
The off-line identification of motor parameters before running is necessary for those high-precision equipments, then the corresponding controller can be properly initialized; and the parameters of the controller should be modified in time, when moment of inertia is changed. 高精度设备有必要在系统运行前对电机参数进行离线辨识,然后正确初始化相应的控制器;而且,当设备的转动惯量变化时要能及时修正控制器参数。
Owing to the problem of traditional particle swarm algorithm using to seek the aim easily fall in the local best, A modified Particle Swarm Optimization based on dynamic inertia weight is proposed. 针对传统粒子群算法在移动机器人寻找目标过程中容易陷入局部最优化而不能找到全局最优点的问题,提出一种基于动态惯性权重的改进粒子群算法。
For the situation that fuzzy controller quantifiable factors are difficult to be modified, the adaptive tuning laws of PSO inertia coefficient are adopted to obtain the preferable fuzzy controller quantifiable factors. 针对模糊量化因子调节的困难,采用粒子群优化惯性系数的自适应调整机制,以寻优模糊控制器量化因子。