摘要
针对IIR数字滤波器的设计问题,对降阶模型的设计是典型的多峰优化问题,基本的教与学优化算法在解决该问题易陷入局部最优。针对上述缺陷提出了一种改进的教学优化(MTLBO)算法。改进算法引入反向学习技术,增加解的多样性;同时为避免迭代初期的无效搜索,针对性的设计了分段式的学习策略,用以均衡算法的全局搜索和局部搜索能力。将MTLBO算法应用于IIR数字滤波器同阶和降阶模型的设计,通过两个典型案例的仿真,与PSO算法、DE算法相比,验证了改进算法的优越性和有效性。
In IIR digital design, the design of the reduced order model is a typical multi-peak optimization problem. Owing to the basic teaching and learning optimization algorithm is easily trapped into local optima in solving the problem, an improved optimization teaching and learning （MTLBO） algorithm is proposed. In the proposed algorithm, the opposition-based learning technology is introduced, which can increase the diversity of solutions, improve the global search ability of the algorithm and avoid the possibility of algorithm to fall into local optimum effectively. The teaching factor is modified, which can effectively balance the global search and local search ability of the algorithm, avoid the invalid iteration of the algorithm at the beginning of the search. Compared with PSO and DE algorithms, the simulation results obtained for two well known benchmark examples with both same order and reduced order models of the IIR filters justify the efficacy of the proposed MTLBO algorithm.
出处
《计算机仿真》
CSCD
北大核心
2015年第11期259-263,共5页
Computer Simulation
基金
国家自然科学基金项目(61463047)
"十二五"新疆制造业信息化科技示范工程(201130110)
新疆大学"电子技术基础"精品课程建设项目(XJU201305)
关键词
教与学优化算法
反向学习技术
教学因子
Teaching-learning-based optimization （TLBO）
Opposition-based learning
Teaching factor
