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.
A novel model order reduction technique based on artificial intelligence
Computer aided design (CAD) plays a vital role in modern VLSI design. Electronic simulation is widely used to verify the design and test the behavior of the circuit before fabrication. One of the major research areas in CAD is circuit simulation. Circuit simulation is to use mathematical models to predict the behavior of an electronic circuit. A circuit is usually represented by a set of partial differential equations (PDEs) or ordinary differential equations (ODEs). So, the circuit simulation actually involves solving large-scale ODEs which sometimes takes several days or even weeks. Therefore, fast and accurate circuit simulation algorithms are needed to accelerate the simulation cycle. One way to speed up the simulation is to approximate the original system with an appropriately simplified system which captures the main properties of the original one. This method is called model order reduction (MOR), which reduces the complexity of the original large system and generates a reduced-order model (ROM) to represent the original one. There are many existing MOR methods, but there is no method that gives the best results for all of the systems. So, each system uses the best method according to its application. So, there is still a need for novel MOR techniques. This paper presents a novel MOR technique based on artificial intelligence.
Rotating dialysis cell as in vitro release method for oily parenteral depot solutions.
The purpose of the study was to investigate an in vitro release method based on a rotating dialysis cell for parenteral oil depot formulations using different model conditions and test formulations. The total amount of drug released from the rotating dialysis cell was in accordance with the theoretical values calculated from the partition coefficients. The release rates were shown to depend on the total amount of drug available for the release process and to follow first order kinetics. The rotating dialysis cell has a potential as in vitro release method for characterization of oily depot formulations for parenteral administration.