【摘要】 文中提出了一种新的基于混沌算法优化的粒子群(CPSO)算法,该算法在种群初始化时应用混沌算法优化粒子的初始位置,扩大粒子的有效搜索范围,在陷入局部最优时应用混沌算法遍历整个搜索空间,跳出局部最优。仿真实验证明该算法寻优性能优于当前其他PSO算法。利用CPSO对LSSVM的参数进行优化选择,建立多传感器数据融合模型。将该模型应用于压力的检测,实验证明了该方法优于当前其他主要方法。更多还原

【Abstract】 This paper put forward a new chaos particle swarm optimization (CPSO) algorithm, when the particle swarm are ini-tialized,the algorithm uses the chaos algorithm to optimize the position of the particle which can extend particle effective searchrange. Also when trapped, the particle swarm break away from local optimum by chaos algorithm searches the entire space. The sim-ulations prove the CPSO has better optimization performance than the other PSOs. CPSO searches parameters for LSSVM,and thedata fusion model of muhi-sensor establish. This method can improve accuracy of measurement effectively than the other main meth-od through pressure measurement experiment.更多还原