Efficient neural network modeling of reconfigurable microstrip patch antenna through knowledge-based three-step strategy

Summary Artificial neural network (ANN) provides an efficient modeling technique based on input–output data obtained from an engineering problem. Highly nonlinear and complex relationships can be formed by ANN because of its nonlinear nature and representing knowledge at interconnection weights. If an ANN model is not sufficient in respect to the accuracy and time consumption, the knowledge based on design experience can be embedded into the modeling process. This knowledge reduces the complexity of the nonlinear input–output relationships; therefore, the knowledge embedded ANN can be formed easily compared with the conventional ANN model. Three-step modeling strategy generates the initial knowledge via the conventional ANN modeling and consists of three sequential and also discrete training processes exploiting the knowledge-based methods such as prior knowledge input and prior knowledge input with difference. The latter step improves the accuracy of the former step, and three-step modeling provides more accuracy than conventional ANN modeling. The efficiency of three-step modeling strategy is demonstrated on two data sets, which are obtained by the reconfigurable microstrip patch antenna design problem. The different number of neurons and ANN structures is handled for the comparison as well. In addition, ANN modeling is formed by MATLAB m-file through neural network toolbox to reveal the efficiency of knowledge-based three-step modeling strategy. Copyright © 2016 John Wiley & Sons, Ltd.