摘要
粒子群优化(PSO)算法是一种新颖的演化算法,该算法通过粒子间的相互作用在复杂搜索空间中发现最优区域,其优势在于简单而功能强大。提出一种T-S型模糊神经网络控制器,采用PSO算法对模糊神经网络的前件参数和后件参数进行寻优,从而实现了模糊规则的自动调整、修改和完善。通过对非线性和时变被控对象的仿真研究,结果表明采用粒子群优化算法可以实现参数的全局快速寻优,而且优化后的T-S型模糊神经网络控制器能获得良好的控制性能。
Particle Swarm Optimization （PSO） is a new evolutionary algorithm, which can find the optimal region in a complex search space through the interaction between particles. Its advantage is simple and powerful. A T-S type fuzzy neural network controller is presented, which adopt PSO algorithm for optimizing fuzzy neural network parameters in order to realize the fuzzy rules automatically adjusted, modified and improved. From the simulation research for non-linear and time-varying controlled objects, it is shown that the parameters can quickly achieve global optimization by using the PSO algorithm, and the optimized T-S fuzzy neural network controller can obtain a good control performance.
出处
《黑龙江大学自然科学学报》
CAS
北大核心
2010年第2期272-276,共5页
Journal of Natural Science of Heilongjiang University
基金
黑龙江省普通高等学校电子工程重点实验室基金资助项目(DZZD2006-21)
黑龙江大学青年科学基金资助项目(QL200510)
关键词
粒子群优化算法
T—s模糊模型
神经网络
功能组合
particle swarm optimization
T-S fuzzy model
neural network
functional combination
