多速粒子群优化算法及其在软测量中的应用  

Multi-velocity particle swarm optimization and its application in soft sensor

作　　者：须文波[1] 杜润龙[1] 

机构地区：[1]江南大学信息工程学院,江苏无锡214122

出　　处：《计算机应用》2007年第3期730-732,736,共4页journal of Computer Applications

摘　　要：多速粒子群优化算法(MVPSO)是一种改进的粒子群优化算法,具有概念清晰、操作简单、易实现等优点,同时克服了标准PSO算法易陷入局部极值的不足。用MVPSO和PSO对几种典型多峰值函数优化问题进行测试,结果表明MVPSO优化算法更容易找到全局最优解,优化效率和优化性能明显提高。将MVPSO优化算法应用于青霉素发酵过程产物(青霉素)浓度软测量,建立基于MVPSO算法的青霉素发酵过程产物浓度软测量模型。实验表明,基于MVPSONN的软测量模型比基于BPNN的软测量模型具有更好的性能。Multi-velocity particle swarm optimization algorithm （MVPSO） is an improved PSO algorithm, which can overcome the shortcomings of easily getting in the local extremum. Besides, this improved algorithm is characterized by clear definition, simple operation and easy realization. Both MVPSO and PSO were used to resolve several typical test function optimization problems. The results indicate that MVPSO has higher efficiency, better performance and more advantages in many aspects than PSO. MVPSO was applied to train artificial neural network to construct a practical soft sensor of product concentration of penicillin fermentation process. The experimental results indicate that this method is feasible and effective in soft sensor application.

关 键 词：粒子群优化算法 多速 软测量 发酵过程 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP273[自动化与计算机技术—控制科学与工程]