Abstract
In this paper, an improved neural networks (INN) strategy is proposed to design two waveguide filters (Pseudo-elliptic waveguide filter and Broad-band e-plane filters with improved stop-band). INN is trained by an efficient optimization algorithm called teaching–learning-based optimization (TLBO). To validate the effective of this proposed strategy, we compared the results of convergence and modeling obtained with a population based algorithm that is widely used in training NN namely Particle Swarm Optimization (PSO-NN). The results show that the proposed INN has given better results.
Index Terms
Improved neural networks; modeling; teaching–learning-based optimization; waveguide filters
