Artificial intelligence in the design of microstrip antenna

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This work presents a Neural Network model for the design of Microstrip Antenna for a desired frequency between 3.5 GHz to 5.5 GHz. The results obtained from the proposed method are compared with the results of IE3D and are found to be in good agreement. The advantage of the proposed method lies with the fact that the various parameters required for the design of specific Microstrip antenna at a particular frequency of interest can be easily extracted without going into the rigorous time consuming, iterative design procedures using a costly software package. In this work, a general design procedure is suggested for the Microstrip antennas using artificial neural networks and this is demonstrated using the rectangular patch geometry.

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Artificial intelligence in the design of microstrip antenna

  1. 1. Artificial Intelligence in the Design of Microstrip Antenna By: Raj Kumar Thenua Vandana V. ThakareDepartment of Electronics & Instrumentation Engineering AEC, Agra, UP
  2. 2. Outline  Introduction  Methodology  Design of a microstrip line feed rectangular Microstrip Antenna using IE3D EM Simulator  Analysis of a microstrip line feed rectangular Microstrip Antenna using ANN  Application  Conclusion  Future scope of the work  Results  References2 8/9/2012
  3. 3. Introduction  Accurate RF/Microwave design is crucial for the current upsurge in VLSI, telecommunication and wireless technologies  Design at microwave frequencies is significantly different from low-frequency and digital designs  Substantial development in RF/microwave CAD techniques have been made during the last decade  Further advances in CAD are needed to address new design challenges  Fast and accurate models are key to efficient CAD  Neural network based modeling and design could significantly impact high-frequency CAD3 8/9/2012
  4. 4. A Illustration Example MICRO STRIP PATCH ANTENNA  radiating metallic patch on a ground substrate  patch can take different configurations but rectangular and circular patches are the most popular one because of ease of analysis and fabrication and their attractive radiation characteristics4 8/9/2012
  5. 5. Example Antenna Figure 1.05 8/9/2012
  6. 6. Justification for Present Work  Antenna design is a very complex problem .  Spacecrafts,aircrafts,missiles and satellite applications require antenna in small ,size ,weight,cost and easy to install.  Mobile ,radio and other wireless communications also demands such specific antennas.  To fulfill such requirements Microstrip patch antennas are used.6 8/9/2012
  7. 7. Methodology  Development of an ANN model in Mat Lab Neural Network Tool Box for the calculation of patch dimensions for Microstrip Antenna .  The data for training the network is generated using IE3D a Electro Magnetic Simulator.  As an example a microstrip line feed rectangular Microstrip patch Antenna is being considered and designed using simulator for a particular resonating frequency i.e. 4.9 GHz.  Validation of ANN model7 8/9/2012
  8. 8. IE3D Electro Magnetic Simulator • Computer Aided Simulation  Integrated Electromagnetic three Dimensional (IE3D) Software  Developed by Zeland Inc., United States  Design Dimensions can be milli, micro and so on.  Simulation Time – Few Minutes  Output Result can be obtained in the form of patch dimensions , VSWR, Return loss, Gain Directivity ,Radiation efficiency,etc.8 8/9/2012
  9. 9. Design parameters designed in IE3D Simulator With dielectric constant Єr = 4.7 Substrate thickness h = 1.588mm Length L= 6.6 mm Width W = 8.8 mm Length of the feed l = 2 mm Width of the feed w = 0.5mm Resonating frequency fr = 4.9 GHz9 8/9/2012
  10. 10. Inset feed Microstrip Antenna Figure 2.010 8/9/2012
  11. 11. Relations to calculate different parameters ofrectangular patch antenna  The effective dielectric constant of the dielectric material is given by11 8/9/2012
  12. 12. Contd…  For an efficient radiator, a practical width that leads to good radiation efficiencies is given by:  where vo is the free-space velocity of light.12 8/9/2012
  13. 13. Contd.. The actual length of the patch: where ∆L is the extension of the length due to the fringing effects and is given by:13 8/9/2012
  14. 14. Contd… ∆L is given by14 8/9/2012
  15. 15. Microstrip Antenna Designed at4.9GHz Figure 3.015 8/9/2012
  16. 16. IE3D Electromagnetic Simulatorto Generate Simulated Data efficiency h gain W IE3D Input impedance L SOFT Feed WARE VSWR dimensions Єr Return loss frequency band Figure 4.016 8/9/2012
  17. 17. Analysis ANN ModelA h W Єr ANN F1 L F2 Figure 5.017 8/9/2012
  18. 18. Neural Network Model  The ANN model is a system with input vector s’ representing the circuit design parameters: height of substrate= h dielectric constant = Єr cut off frequencies F1 and F2  And the output vector r’ representing the Patch dimensions. Length of the Patch = L Width of the Patch = W18 8/9/2012
  19. 19. Neural Network Architecture  Three layer network structure has been considered  Input layer will have four neurons to accept input parameters h ,Єr, F1 and F2.  Output layer will have two neurons to output patch dimensions.  The hidden layer will have number of neurons depending upon design accuracy.  The radial basis function network is considered for the network architecture.  The network will be trained using radial basis function19 8/9/2012
  20. 20. RBF Network Feed forward neural networks with a single hidden layer that use radial basis activation functions for hidden neurons are called radial basis function networks. RBF networks are applied for various microwave modeling purposes. RBF can approximate any regular function. Trains faster than any multi-layer perceptron. It has just two layers of weights. Input is non-linear and output is linear. No saturation while generating outputs20 8/9/2012
  21. 21. Architecture of RBF Network x1 y1 x2 y2 x3 output layer input layer (linear weighted sum) (fan-out) hidden layer (weights correspond to cluster centre, output function usually Gaussian)21 8/9/2012
  22. 22. RBF Functions  Gaussian Activation Function 1 j x exp X j j X j j 1...L  Output Layer: is a weighted sum of hidden inputs L k (x) jk . j (x) j 1 X is a multi dimensional input vector with elements xi and j is the vector determining the center of basis function j and has elements ji.22 8/9/2012
  23. 23. Contd.. The distance measured from the cluster centre is usually the Euclidean distance. n rj ( xi wij ) 2 i 123 8/9/2012
  24. 24. MAT Lab Tool Box In order to develop the ANN model MAT LAB neural network tool box has been used.24 8/9/2012
  25. 25. Network Training Two kinds of training algorithms- Supervised and Unsupervised- RBF networks are used mainly in supervised applications- In this case, both dataset and its output is known.- The model is trained with the set of 200 samples  Clustering algorithms (k-mean)  The centers of radial basis functions are initialized randomly.  For a given data sample Xi the algorithm adapts its closest center25 8/9/2012
  26. 26. Network Testing  The performance of the network is tested by a second set of a sample vectors pairs which are not included in training data set but must be in the specified given range.  If the unknown sample pairs are modeled correctly the network is likely to represent a valid model.  The model is tested for around 26 values and found satisfactorily.26 8/9/2012
  27. 27. Application  After training and testing the model is ready to be used as a simulator for the calculation of patch dimensions for the Microstrip antenna.  The model can be reused in the design process many times without the cost of EM Simulations.  The network is capable of predicting the output for any given input in the trained region inexpensively.27 8/9/2012
  28. 28. Results S. No. F1 GHz F2 GHz W mm L mm W mm L mm (IE3D) (IE3D) (RBF) (RBF) 1 4.93 5.03 17.8 13.35 17.84 13.33 2 4.86 4.95 17.8 13.55 17.83 13.53 3 4.82 4.91 17.8 13.65 17.84 13.66 4 4.8 4.89 17.8 13.85 17.83 13.84 5 4.77 4.85 17.8 14.05 17.84 14.02 6 4.73 4.81 17.8 14.15 17.83 14.17 7 4.71 4.79 17.8 14.25 17.82 14.26 8 4.69 4.76 17.8 14.35 17.83 14.3628 8/9/2012
  29. 29. W mm L mm W mm L mm S. No. F1 GHz F2 GHz (IE3D) (IE3D) (RBF) (RBF) 9 4.66 4.73 17.8 14.45 17.84 14.46 10 4.78 4.87 18.3 13.85 18.29 13.86 11 4.65 4.73 18.3 14.35 18.31 14.37 12 4.61 4.7 18.8 14.35 18.82 14.36 13 4.49 4.55 18.8 14.85 18.83 14.84 14 4.47 4.55 19.3 14.85 19.32 14.83 15 4.37 4.41 19.3 15.35 19.31 15.33 16 4.35 4.41 19.8 15.35 19.82 15.3729 8/9/2012
  30. 30. W mm L mm W mm L mm S. No. F1 GHz F2 GHz (IE3D) (IE3D) (RBF) (RBF) 17 4.31 4.37 20.3 15.85 20.29 15.84 18 4.29 4.35 20.3 16.35 20.31 16.36 19 4.27 4.33 20.8 16.35 20.84 16.36 20 4.26 4.33 20.8 16.85 20.82 16.86 21 4.21 4.27 21.3 16.85 21.33 16.84 22 4.16 4.21 21.3 17.35 21.29 17.36 23 4.14 4.19 21.8 17.35 21.83 17.37 24 4.02 4.04 21.8 17.85 21.83 17.86 25 3.99 4.01 22.3 17.85 22.33 17.83 26 3.92 3.93 22.3 18.35 22.31 18.3630 8/9/2012
  31. 31. Conclusion  The neural network developed in this work models the patch dimensions calculator for microstrip line feed rectangular Microstrip patch antenna.  The radial basis function network is giving the best approximation to the target values  The values obtained from ANN are very close to simulation readings .  The error between output of ANN and IE3D is very very small.  The developed model for resonant structure Microstrip Antenna validate the modeling approach.31 8/9/2012
  32. 32. Future Scope  Working with the same concept and design analysis ,different microwave and RF devices could be designed .  Different analysis and synthesis ANN model could be developed for other performance parameters of the microwave circuits like input impedance ,directivity ,gain, VSWR, return loss etc.32 8/9/2012
  33. 33. References [1] Q. J. Zhang, K. C. Gupta, Neural Networks for RF and Microwave Design, Artech House Publishers, 2000. [2] R. K. Mishra, Member, IEEE, and A. Patnaik , ANN Techniques in Microwave technology . [3] A. H. Zaabab, Q.J. Zhang, M. Nakhla, ”Analysis and Optimization of Microwave Circuits & Devices Using Neural Network Models”’ IEEE MTT- S Digest 1994, pp 393- 396 [4] C.A. Balanis, Antenna Theory, John Wiley & Sons, Inc., 1997. [5] D.M. Pozar, Microstrip Antenna , Proc. IEEE, Vol. 80, pp.79-81, [6] F. Wang, V.K. DevabhaktunI, and Q.J. Zhang,” A hierarchical neural network approach to the development of library of neural models for microwave design”, IEEE Intl. Microwave Symp. Digest, pp. 1767-1770, Baltimore, MD, 1998.33 8/9/2012
  34. 34. Contd..  [7] F. Peik, G. Coutts, R.R. Mansour ,COM DEV, Cambridge, ON, Canada, “Application of neural networks in microwave circuit modelling” , Electrical and computer Engineering,1998,IEEE Canadian Conference , vol-2 ,24-28 May 1998,pages:928-931  [8] S. Devi , D.C. Panda, S.S. Pattnaik, “A novel method of using Artificial neural networks to calculate input impedance of circular microstrip antenna”, Antennas and Propagation Society International Symposium, Vol. 3, pp. 462 – 465, 16-21 June 2002.  [9] R.K. Mishra, A. Patnaik, “Neural network-based CAD model for the design of square-patch antennas”, Antennas and Propagation, IEEE Transactions, Vol. 46, No. 12, pp. 1890 – 1891, December 1998.  [10] A. Patnaik, R.K. Mishra, G.K. Patra, S.K. Dash, ”An artificial Neural network model for effective dielectric constant of microstrip line,” IEEE Trans. On Antennas Propagat., vol. 45, no. 11, p. 1697, Nov. 1997.  [11] Simon Haykin, Neural Networks second edition pHI34 8/9/2012
  35. 35. Thank you35 8/9/2012

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