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 genetic 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, genetic algorithm (GA) which is one of the artificial intelligence algorithms is used to approximate high-order transfer functions (TFs) as lower-order TFs. Genetic algorithm is considered as one of the evolutionary techniques which are used in optimization problems. In this approach, genetic algorithm is applied to model order reduction putting in consideration improving accuracy and preserving the properties of the original model which are two important issues for improving the performance of simulation and computation and maintaining the behavior of the original complex models being reduced. The proposed technique could be used in EDA tools.
Towards improving simulation of analog circuits using model order reduction
Large analog circuit models are very expensive to evaluate and verify. New techniques are needed to shorten time-to-market and to reduce the cost of producing a correct analog integrated circuit. Model order reduction is an approach used to reduce the computational complexity of the mathematical model of a dynamical system, while capturing its main features. This technique can be used to reduce an analog circuit model while retaining its realistic behavior. In this paper, we present an approach to model order reduction of nonlinear analog circuits. We model the circuit using fuzzy differential equations and use qualitative simulation and K-means clustering to discretion efficiently its state space. Moreover, we use a conformance checking approach to refine model order reduction steps and guarantee simulation acceleration and accuracy. In order to illustrate the effectiveness of our method, we applied it to a transmission line with nonlinear diodes and a large nonlinear ring oscillator circuit. Experimental results show that our reduced models are more than one order of magnitude faster and accurate when compared to existing methods.