International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 3, No. 3, 8 May 2011
Cover page and Table of Contents: PDF (size: 748KB)
The Application of Sparse Antenna Array Synthesis Based on Improved Mind Evolutionary Algorithm
Full Text (PDF, 748KB), PP.40-46
Views: 0 Downloads: 0
Author(s)
Nan Li
1,*
Index Terms
MEA, DEA, sparse antenna array, circular antenna array, side lobe level
Abstract
Mind Evolutionary Algorithm (MEA) imitates the human mind evolution by using similartaxis and dissimilation operations, which overcomes the prematurity and improves searching efficiency. But the generation of the initial population is blind and the addition of naturally washed out temporary subpopulations is random. This paper improved MEA by introducing chaos and difference into it, which brought adequate diversity to the initial population and saved the excellent genes in the evolution. Then the improved MEA is used in the synthesis of sparse antenna arrays. The excellent results of computer simulation show the advantage of array antenna patterns synthesis using the improved MEA.
Cite This Paper
Nan Li, "The Application of Sparse Antenna Array Synthesis Based on Improved Mind Evolutionary Algorithm", International Journal of Intelligent Systems and Applications(IJISA), vol.3, no.3, pp.40-46, 2011. DOI:10.5815/ijisa.2011.03.06
Reference
[1]K. M. Xie, Y. G. Du and C. Y. Sun, “Application of the Mind-Evolution-Based Machine Learning in Mixture-Ratio Calculation of Raw Materials Cement”, Proceedings of the 3rd World Congress on Intelligent Control and Automation, pp. 132-134, 2000.(in Chinese)
[2]K. Keiji, “Stability extended delayed-feedback control for discrete time chaotic systems”, IEEE Trans. On Circuits and Systems, vol. 46, pp.1285-1288, Oct 1999.
[3]L. Chen, G. R. Chen, “Fuzzy modeling, prediction and control of uncertain chaotic systems based on time series”, IEEE Trans. Circuits and Systems-I: Fundamental Theory and Application, vol. 47, pp.1527-1531, Oct 2000.
[4]R. L. Devaney, “An Introduction to Chaotic Dynamical Systems”, Addison-Wesley, 2nd ed, 1989, New York.
[5]R. Storn and K. Price, “Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces”, Technical Report, International Computer Science Institute, vol. 8, pp. 22-25, Aug 1995.
[6]K. S. Chen, C. L. Han and Z. S. He, “A Synthesis Technique for Linear Sparse Arrays with Optimization Constraint of Minimum Element Spacing”, Chinese Journal of Radio Science, vol. 22, pp. 27-32, Feb 2007. (in Chinese)
[7]V. Murino, A. Trucco and C. S. Regazzoni, “Synthesis of Unequally Spaced Arrays by Simulated Annealing”, IEEE Trans. Antennas Signal Processing, vol. 44. pp. 119-123, 1996.
[8]B. P. Kumar and G. R. Branner, “Design of Unequally Spaced Arrays for Performance Improvement”, IEEE Trans. on Antenna and Propagation., vol. 3, pp. 511-523, 1999.
[9]K. S. Chen, Z. S. He and C. L. Han, “Design of Unequally Spaced Arrays for Performance Improvement”, Sensor Array and Multichannel Signal Processing, IEEE Workshop 2006, pp. 166-170, 2006. (in Chinese)
[10]E. M. Thomas and M. P. Krishma, “Pattern Synthesis of Conformal Arrays for Airborne Vehicles”, IEEE Aerospace Conference Proceeding, vol. 2, pp. 1030-1038, 2004.
[11]G. Caille, E. Vourch and M. J.Martin, “Conformal Array Antenna for Observation platforms in low Earth Orbit”, IEEE Antenna and Propagation Magazine, vol. 44, pp. 103-104,2002.