Abstract
In this paper, 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). The INN based in the efficient optimization method called teaching–learning-based optimization (TLBO). For validate effectiveness of this proposed strategy, we compared the results of convergence and modeling obtained with a population based algorithm which is widely used in training NN namely Particle Swarm Optimization (PSO-NN). The results prove that the proposed INN has given better results.
Highlights
The full wave EM solvers [1] have been used to design the microwave filter for a long time
Training of neural networks (NN) is an important step; it is based on optimization of weights of NN to minimize the mean square error (MSE) between the NN output and the desired output
Many populations based algorithms have been proposed for training a neural network such as Particle Swarm Optimization (PSO) [6], Genetic Algorithms [7] and other optimization algorithms [8]
Summary
The full wave EM solvers [1] have been used to design the microwave filter for a long time. Artificial neural network (ANN) has been proven to be a fast and effective means of modeling complex electromagnetic devices. It has been recognized as a powerful tool for predicting device behavior for which no mathematical model is available or the device has not been analyzed properly yet. The trained model delivers the output parameters very quickly. For these attractive qualities, ANN has been applied to different areas of engineering’s [2] - [4]. We tried to improve the NN by training them by a recent and effective optimization algorithm called Teaching-Learning Based Optimization (TLBO) [9].
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