Digest of Papers. 2005 Topical Meeting on Silicon Monolithic Integrated Circuits in RF Systems, 2005.
DOI: 10.1109/smic.2005.1587918
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PSO-based evolutionary optimization for black-box modeling of arbitrary shaped on-chip RF inductors

Abstract: In the present work a particle swarm optimization (PSO) based off-line system identification algorithm has been proposed for modeling of RF on-chip inductors. The proposed scheme has a distinctive feature that the determination of the system structure and the identification of parameters can be simultaneously obtained. The system identification algorithm used here is based upon a black-box modeling approach. Unlike the conventional equivalent circuit models, in the proposed modeling a priori information of the… Show more

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Cited by 4 publications
(6 citation statements)
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“…For such an array, the SLL minimization problem can be solved by minimizing the following cost function (9) where AF is defined in (7). In order to introduce constraints over FNBW and null locations, we use the penalty method [19] and modify (9) as (10) where and are large numbers required to impose penalty and are arbitrarily taken as and respectively. is the calculated FNBW and is the desired FNBW.…”
Section: Synthesis Of Uniformly Excited Unequally Spaced Linear Amentioning
confidence: 99%
See 3 more Smart Citations
“…For such an array, the SLL minimization problem can be solved by minimizing the following cost function (9) where AF is defined in (7). In order to introduce constraints over FNBW and null locations, we use the penalty method [19] and modify (9) as (10) where and are large numbers required to impose penalty and are arbitrarily taken as and respectively. is the calculated FNBW and is the desired FNBW.…”
Section: Synthesis Of Uniformly Excited Unequally Spaced Linear Amentioning
confidence: 99%
“…BW is the allowed tolerance of FNBW. In (10), is the null depth associated with the th null located at and is the desired null depth [19]. It is evident from (10) that CF is equal to for all those array geometries that satisfy the constraints on FNBW and null locations.…”
Section: Synthesis Of Uniformly Excited Unequally Spaced Linear Amentioning
confidence: 99%
See 2 more Smart Citations
“…In the particle swarm algorithm, the trajectory of each particle (i,e,, candidate solution to the optimization problem) in the search space is adjusted according to its own experience and the experience of the other particles in the swarm. It has been successfully applied in many different areas such as tieural network training [14], system modeling [15], and engineering design [16]. In this paper, it is applied to estimate LMD model parameters based on measured height and temperature profiles.…”
Section: System Identification Based On Psomentioning
confidence: 99%