Application of neural networks in microwave circuit modelling

Peik, S.F.; Coutts, G.M.; Mansour, Raafat R.

doi:10.1109/ccece.1998.685651

Cited by 14 publications

(11 citation statements)

References 3 publications

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“…Finally, the outputs of the RBF network are computed from hidden neurons as shown in Equation (8) where w ki is the weight of the link between the ith neuron of the hidden layer and the kth neuron of the output layer.…”

Section: Rbf Networkmentioning

confidence: 99%

“…Recently CAD approach based on neural networks has been introduced in the microwave community for modeling of passive and active microwave component. Number of research papers [4][5][6][7][8][9][10][11][12][13][14] indicates how ANN can be used efficiently to calculate different design and performance parameters of microstrip antennas. However the literature shows that only three layer MLPFFBP has been preferred to prove the utility of ANN in the area of microstrip antenna design.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence in the Estimation of Patch Dimensions of Rectangular Microstrip Antennas

Thakare¹,

Singhal²

2011

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Artificial Neural Network (ANNs) techniques are recently indicating a lot of promises in the application of various micro-engineering fields. Such a use of ANNs for estimating the patch dimensions of a microstrip line feed rectangular microstrip patch antennas has been presented in this paper. An ANN model has been developed and tested for rectangular patch antenna design. The performance of the neural network has been compared with the simulated values obtained from IE3D EM Simulator. It transforms the data containing the dielectric constant (ε r ), thickness of the substrate (h), and antenna's dominant-mode resonant frequency (f r ) to the patch dimensions i.e. length (L) and width (W) of the patch. The different variants of back propagation training algorithm of MLFFBP-ANN (Multilayer feed forward back propagation Artificial Neural Network) and RBF-ANN (Radial basis function Artificial Neural Network) has been used to implement the network model. The results obtained from artificial neural network when compared with simulation results, found satisfactory and also it is concluded that RBF network is more accurate and fast as compared to different variants of back propagation training algorithms of MLPFFBP. The ANNs results are more in agreement with the simulation findings. Neural network based estimation has the usual advantage of very fast and simultaneous response of all the outputs.

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Section: Rbf Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in the Estimation of Patch Dimensions of Rectangular Microstrip Antennas

Thakare¹,

Singhal²

2011

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“…Further, in [10], the switching characteristics of CMOS inverter have also been modelled while Microwave transistors and circuit modelling have been elucidated in [11]- [12]. In [13] the speed control the speed of a Double Star Induction Motor has been modelled Induction Motor.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Design of Inverter Threshold Quantization based Current Comparator using Artificial Neural Networks

Bhatia¹,

Pandey²,

Bhattacharyya³

2016

IJECE

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Performance of a MOS based circuit is highly influenced by the transistor dimensions chosen for that circuit. Thus, proper dimensioning of the transistors plays a key role in determining its overall performance. While choosing the dimension is critical, it is equally difficult, primarily due to complex mathematical formulations that come into play when moving into the submicron level. The drain current is the most affected parameter which in turn affects all other parameters. Thus, there is a constant quest to come up with techniques and procedure to simplify the dimensioning process while still keeping the parameters under check. This study presents one such novel technique to estimate the transistor dimensions for a current comparator structure, using the artificial neural networks approach. The approach uses Multilayer perceptrons as the artificial neural network architectures. The technique involves a two step process. In the first step, training and test data are obtained by doing SPICE simulations of modelled circuit using 0.18μm TSMC CMOS technology parameters. In the second step, this training and test data is applied to the developed neural network architecture using MATLAB R2007b.

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“…A design method is introduced in Ref. 6, a method in which ANN has been used efficiently and effectively for the calculation of resonating frequency of an inset fed microstrip antenna. The network is built with three layers MLP structure [7] with a relatively large number of hidden neurons to achieve the required degree of freedom and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Microstrip antenna design using artificial neural networks

Thakare

Singhal²

2009

Int J RF and Microwave Comp Aid Eng

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Neural-network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behavior of passive/active components/circuits. This work describes the fundamental concepts in this emerging area aimed at teaching RF/microwave engineers what neural networks are, why they are useful, when they can be used, and how to use them to model microstrip patch antenna. This work studies in-depth different designs and analysis methods of microstrip patch antenna using artificial neural-network and different network structure are also described from the RF/microwave designer's perspective. This article also illustrates two examples of microstrip antenna design and validating the utility of ANN in the area of microstrip antenna design.

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Application of neural networks in microwave circuit modelling

Cited by 14 publications

References 3 publications

Artificial Intelligence in the Estimation of Patch Dimensions of Rectangular Microstrip Antennas

Artificial Intelligence in the Estimation of Patch Dimensions of Rectangular Microstrip Antennas

Modelling and Design of Inverter Threshold Quantization based Current Comparator using Artificial Neural Networks

Microstrip antenna design using artificial neural networks

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