A Consensual Modeling of the Expert Systems Applied to Microwave Devices
In this work, a consensual approach is developed for modeling RF/microwave devices. In the proposed method, multiple individual models generated by an expert system ensemble are combined by a consensus rule that results in a consistent and improved generalization outputting with the highest possible reliability and accuracy. Here, the expert system ensemble is basically constructed by the competitor and diverse regressors which in our case are back-propagation artificial neural network (ANN), support vector (SV) regression machine, k-nearest neighbor and least squares algorithms that perform generalization independently from each other. In the case of excessive data, to reduce the amount of the data, the expert system ensemble of regressors can be shown to be trained by a subset consisting of the SVs. Main feature of the consensual modeling can be put forward as due to diversity in generalization process of each member of the ensemble, the resulted consensus model will effectively identify and encode more aspects of the nonlinear relationship between the independent and the dependent variables than will a single model. Thus, in the consensual modeling, an enhanced single model is built by combining the most successful sides of the competitor and the diverse contributors. Finally, consensual modeling is demonstrated typically for the two devices: the first is a passive device modeling which is synthesis of the conductor-backed coplanar waveguide with upper shielding and the second is an active device modeling which is the noise modeling of a microwave transistor. (C) 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE 20: 430-440, 2010.
consensual modeling artificial neural networks least squares support vector regression k-nearest neighbor algorithm