الگوریتم کلونی زنبور عسل مصنوعی برای مدل سیگنال کوچک استخراج پارامتر اثر میدانی برفلز نیمه هادی ترانزیستور
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|7367||2010||6 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 4075 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
- تولید محتوا با مقالات ISI برای سایت یا وبلاگ شما
- تولید محتوا با مقالات ISI برای کتاب شما
- تولید محتوا با مقالات ISI برای نشریه یا رسانه شما
پیشنهاد می کنیم کیفیت محتوای سایت خود را با استفاده از منابع علمی، افزایش دهید.
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Engineering Applications of Artificial Intelligence, Volume 23, Issue 5, August 2010, Pages 689–694
This paper presents an application of swarm intelligence technique namely artificial bee colony (ABC) to extract the small signal equivalent circuit model parameters of GaAs metal extended semiconductor field effect transistor (MESFET) device and compares its performance with particle swarm optimization (PSO) algorithm. Parameter extraction in MESFET process involves minimizing the error, which is measured as the difference between modeled and measured S parameter over a broad frequency range. This error surface is viewed as a multi-modal error surface and robust optimization algorithms are required to solve this kind of problem. This paper proposes an ABC algorithm that simulates the foraging behavior of honey bee swarm for model parameter extraction. The performance comparison of both the algorithms (ABC and PSO) are compared with respect to computational time and the quality of solutions (QoS). The simulation results illustrate that these techniques extract accurately the 16—element small signal model parameters of MESFET. The efficiency of this approach is demonstrated by a good fit between the measured and modeled S-parameter data over a frequency range of 0.5–25 GHz.
Small signal model parameter extraction of MESFET involves extraction of extrinsic and intrinsic model element values (Lin and Kompa, 1994) by minimizing the difference between modeled and measured S-parameter over a broad range of frequencies. In the recent past, different techniques ( Yaser, 2000 and Van Niekerk et al., 2000) have been reported in the literature for extracting the model parameters of MESFET. These techniques are normally based on either analytical or numerical optimization techniques. Although analytical methods provide faster solution, the quality of solution (QoS) is normally poor. To improve QoS, methods based on numerical optimization are being increasingly used for parameter extraction. Numerical optimization techniques are either gradient-based or gradient-free. Possibility of having a multimodal error surface is an important extraction challenge in parameter extraction problem. In order to find a quality solution, an extraction algorithm that can achieve the global minima in multimodal error surface is required. However, the conventional gradient based approach that are used in past, can easily be trapped in local minima. Many researchers have proposed global optimization techniques like genetic algorithm (GA) ( Gao, 2001, Watts et al., 1999 and Menozzi et al., 1996) to extract the small signal model parameters of the MESFET. Since a typical small signal parameter extraction problem has a multimodal error surface and it involves a large set (i.e. 16 number of elements), conventional gradient based techniques fails to provide QoS. In some cases, GA cannot guarantee global solution due to the diversity of population (Leung et al., 1997). Swarm intelligence has become a research interest to different domain of researchers in recent years. These algorithms simulate the food foraging behavior of a flock of birds or swarm of bees. Particle swarm optimization and its variants have been introduced for solving numerical optimization problems and successfully applied to solve many real world problems (Eberhart and Kenedy, 1995b; Sabat et al., 2009 and Sabat et al., 2010). PSO algorithm is a population based stochastic optimization technique and suitable for optimizing nonlinear multimodal error function. Motivated by the foraging behavior of honeybees, researchers have (Riley et al., 2005 and Karaboga, 2009) initially proposed artificial bee colony (ABC) algorithm for solving various optimization problems. ABC is a relatively new population-based meta-heuristic approach and is further improved by Karaboga and Basturk (2008). This algorithm is easy to implement and found to be robust. Some recent results illustrate that artificial bee colony (ABC) algorithms outperforms basic PSO algorithm in terms of QoS (Karaboga and Basturk, 2008). The PSO and ABC algorithms are population based evolutional meta-heuristic optimization algorithms that avoids trapping of solution in local minima. The objective of this paper is to use ABC algorithm for extracting the small signal parameters and to compare relative performances in terms of computational cost and QoS with that of basic PSO algorithm. Fig. 1 shows a 16 element small signal equivalent circuit adopted for parameter extraction. It has eight extrinsic and eight intrinsic parameters. Extrinsic parameters are bias independent whereas intrinsic parameters are bias dependent. The methodology for extracting these parameters essentially involves minimization of the difference between measured and modeled S-parameter values under dc bias conditions.
نتیجه گیری انگلیسی
In this paper, the artificial bee colony (ABC) algorithm, which is a new, simple and robust optimization algorithm, is used to extract small signal model parameters of a fabricated GaAs MESFET from measured S-parameter data. The performance of the proposed algorithm is compared with the particle swarm optimization (PSO) algorithm. These algorithms are able to successively extract the small signal model parameters of MESFET. The results reveal that ABC is more robust and has less relative error between the measured and modeled S-parameters compared to PSO algorithm. ABC algorithms also converges faster compared to PSO algorithm for this problem. Moreover, in these swarm intelligence strategy no user intervention is required for model parameter extraction unlike gradient descent approach. In future, we will study the possible variations of ABC and PSO to further improve the performance.