Neuro-fuzzy modeling techniquesfor microwave components

Rahouyi, E.B.; Hinojosa, Juan; Garrigós, Javier

doi:10.1109/lmwc.2005.863245

Cited by 28 publications

(18 citation statements)

References 9 publications

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“…Therefore, if a solution contains one or more mapping structures, two optimizations are performed. First the circuit is optimized over without the mapping structure in order to find , as in (16). For the fixed value of , an optimization is then performed over to find the optimal set of mapping parameters (19) Let be the netlist whose parameters are set to these optimal values.…”

Section: ) Mapping a Circuit To A Fitness Valuementioning

confidence: 99%

“…Techniques combining ANNs and space mapping have been developed for electromagnetic (EM) modeling [2], nonlinear device modeling [13], and statistical device modeling [14]. In addition to the combination of space mapping with ANNs, implementations using linear regression models [15], neuro-fuzzy models [16], locally weighted models [17], and hybrid models [18] have also been described. This paper explores further advances in the application of ANN and space mapping for modeling of nonlinear microwave devices.…”

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confidence: 99%

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Evolutionary Neuro-Space Mapping Technique for Modeling of Nonlinear Microwave Devices

Gorissen

Zhang

et al. 2011

IEEE Trans. Microwave Theory Techn.

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Abstract-This paper presents a new advance in Neuro-space mapping (Neuro-SM) techniques for modeling nonlinear microwave devices. Suppose that existing device models (namely, coarse models) cannot match the behavior of a new device (referred to as the fine model). By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. However, the efficiency of mapping depends on both the mapping structure and the coarse model. In this paper, a structural optimization technique is presented to achieve optimal combinations of mapping structure and coarse model. An aggressive optimization formulation exploring detailed structural variations in both the mapping and the coarse model is proposed, where the internal branches of coarse models and separate mappings for the voltage and current at gate and drain are used as basic topology variables. The formulation of such a structural optimization by an evolutionary optimization algorithm is proposed. Numerical examples of metal-semiconductor field-effect transistor and high electron-mobility transistor modeling demonstrate that, by using the proposed algorithm, optimal combinations of space mapping and coarse model structures can be achieved leading to the best modeling accuracy with the simplest mapping function.Index Terms-Genetic algorithms, knowledge-based modeling, nonlinear device modeling, space mapping.

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Section: ) Mapping a Circuit To A Fitness Valuementioning

confidence: 99%

mentioning

confidence: 99%

Evolutionary Neuro-Space Mapping Technique for Modeling of Nonlinear Microwave Devices

Gorissen

Zhang

et al. 2011

IEEE Trans. Microwave Theory Techn.

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“…The FNN has 102 rules. Such number could be certainly reduced using appropriate fuzzy algorithms described in several references such as [15,20,[47][48][49][50][51]. Then, by specifying a user-defined testing error of 2%, our fuzzy-neural method showed that FET topology no.…”

Section: B Example 2: Phemt With Measured Datamentioning

confidence: 99%

Fuzzy neural-based approaches for efficient RF/microwave transistor modeling

Gaoua

Cheng

et al. 2009

Int J RF and Microwave Comp Aid Eng

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In today's RF and microwave circuits, there is an ever-increasing demand for higher level of system integration that leads to massive computational tasks during simulation, optimization, and statistical analyses, requiring efficient modeling methods so that the whole process can be achieved reliably. Since active devices such as transistors are the core of modern RF/microwave systems, the way they are modeled in terms of accuracy and flexibility will critically influence the system design, and thus, the overall system performance. In this article, the authors present neural-and fuzzy neural-based computer-aided design techniques that can efficiently characterize and model RF/microwave transistors such as field-effect transistors and heterojunction bipolar transistors. The proposed techniques based on multilayer perceptrons neural networks and c-means clustering algorithms are demonstrated through examples.

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“…It combines the benefits of artificial neural networks (ANNs) and FISs in a single model. Fast and accurate learning, excellent explanation facilities in the form of semantically meaningful fuzzy rules, the ability to accommodate both data and existing expert knowledge about the problem, and good generalization capability features have made neurofuzzy systems popular in the last few years [21,22]. In [21], ANFIS was successfully introduced by Guney et al to compute the input resistance of rectangular microstrip antennas (MSAs).…”

Section: Introductionmentioning

confidence: 99%

“…In [21], ANFIS was successfully introduced by Guney et al to compute the input resistance of rectangular microstrip antennas (MSAs). In [22], Rahouyi et al have been successfully applied this technique to a microwave tunable phase shifter. This modeling technique is relatively new to the microwave engineering.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Models for Conventional Coplanar Waveguides

Türkmen¹,

Kaya²,

Yildiz³

et al. 2008

PIER B

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Abstract-In this work a new method based on the adaptive neuro-fuzzy inference system (ANFIS) was successfully introduced to determine the characteristic parameters, effective permittivities and characteristic impedances, of conventional coplanar waveguides. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid-learning algorithm, which combines least-square method and backpropagation algorithm, is used to identify the parameters of ANFIS. There are very good agreement between the results of ANFIS models, experimental works, conformal mapping technique, spectral domain approach and a commercial electromagnetic simulator, MMICTL.

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Neuro-fuzzy modeling techniquesfor microwave components

Cited by 28 publications

References 9 publications

Evolutionary Neuro-Space Mapping Technique for Modeling of Nonlinear Microwave Devices

Evolutionary Neuro-Space Mapping Technique for Modeling of Nonlinear Microwave Devices

Fuzzy neural-based approaches for efficient RF/microwave transistor modeling

Adaptive Neuro-Fuzzy Models for Conventional Coplanar Waveguides

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