基于PSO的多级反馈放大器的设计与仿真
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摘要
采用粒子群优化算法,在高电压增益、低输出电阻的要求下,选择电压增益与输出电阻的比作为适应度函数,对两级直接耦合负反馈放大电路电阻的参数进行优化设计.对结果的电阻值经EWB软件仿真,闭环电压增益与优化理论计算的平均误差为1.65%,说明理论的正确性.优化结果显示,输出电阻总是趋于设定值的底限,以使适应度函数为最大,符合算法的要求.同时根据对放大器指标的不同需求,可以改变适应度函数,找到的最好效果.
The parameters of resistances in two levels direct coupled negative feedback amplifying circuit are optimized by particle swarm optimization with the requirements of highvoltage gain and low output resistance,the ratio of voltage gain and output resistance is chosen as fitness function of algorithm.The optimal results of resistance are simulated by EWB,the average relative error between close loop voltage gain and theory value is 1.65%,the result indicate that the theory is correct.The optimal results also indicated that the output resistance always tends to the set value and make the fitness function is the maximum,this state consistent with the requirements of the algorithm.In addition,the best result can be found with changing of fitness function according to the different needs of the amplifier index.
The parameters of resistances in two levels direct coupled negative feedback amplifying circuit are optimized by particle swarm optimization with the requirements of highvoltage gain and low output resistance,the ratio of voltage gain and output resistance is chosen as fitness function of algorithm.The optimal results of resistance are simulated by EWB,the average relative error between close loop voltage gain and theory value is 1.65%,the result indicate that the theory is correct.The optimal results also indicated that the output resistance always tends to the set value and make the fitness function is the maximum,this state consistent with the requirements of the algorithm.In addition,the best result can be found with changing of fitness function according to the different needs of the amplifier index.
引文
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[3]Pedersen M E H,Chipperfield A J.Simplifying Par-ticle Swarm Optimization[J].Applied Soft Compu-ting,2010,10(2):618.
[4]Sari A,Espanet C,Hissel D.Particle swarm optimi-zation applied to the co-design of a fuel cell air circuit[J].Journal of Power Sources,2008,179(1):121.
[5]Mousa A A,El-Shorbagy M A,Abd-El-Wahed WF.Local search based hybrid particle swarm optimi-zation algorithm for multiobjective optimization[J].Swarm and Evolutionary Computation,2012,3:1.
[6]聂茹,岳建华.基于自适应混沌变异粒子群算法的地震参数反演[J].四川大学学报:自然科学版,2009,46(1):89.
[7]何怡刚,祝文姬,周炎涛,等.基于粒子群算法的模拟电路故障诊断方法[J].电工技术学报,2010,25(6):163.
[8]童诗白,华成英.模拟电子线路基础[M].北京:高等教育出版社,2006.
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