An innovative approach of model order reduction using ant colony optimization
In this paper, a generic solution in the area of model order reduction is presented. The proposed method is based on artificial intelligence, which depends on a probabilistic optimization algorithm called Ant Colony. This approach aims to obtain a reduced-order model out of a relatively complex model represented as a transfer function. This reduced order model is obtained in a reasonable run time. It has acceptable error in time domain represented by the unit step time response, and the least error in frequency domain represented by Bode plot compared to other conventional methods in model order reduction regardless the targeted transfer function.
Model order reduction using fuzzy logic algorithm
Model order reduction has been one of the most challenging topics in the past years. Conventional mathematical methods have been used to obtain a reduced order model of high order complex models. In this paper, fuzzy logic algorithm, which is one of the artificial intelligence algorithms, is used in model order reduction. Fuzzy logic algorithm is considered as one of the evolutionary techniques, which are used in optimization problems. In our proposed approach, fuzzy algorithm is used in model order reduction taking in consideration improving the accuracy and preserving the basic properties of the original model. The obtained reduced order model has an acceptable error compared to the original model. Also the algorithm does not take large amount of time when evaluating the reduced model.
Comparing fast convolution and model order reduction methods for S-parameter simulation
This paper presents a comparison of the model-order reduction and fast convolution techniques for the macromodel simulation. Model-order reduction techniques using the vector fitting method have been extensively employed. In parallel, convolution techniques using scattering parameters have demonstrated special properties that can be exploited by acceleration techniques to achieve superior performance. The comparison is performed using simulations for various blackbox data.