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The Application of Improved Mind Evolutionary Algorithm in Antenna Array Synthesis

Author: LiZuo
Tutor: LiuJianXia
School: Taiyuan University of Technology
Course: Circuits and Systems
Keywords: Mind Evolutionary Algorithm Chaos Differential evolution algorithm Array antenna synthesis The dilute cloth array antenna
CLC: TN820
Type: Master's thesis
Year: 2010
Downloads: 79
Quote: 1
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Abstract


With the rapid development of wireless communication technology, the number of mobile communication users increased dramatically, the data rates continue to increase, the increasingly high demand communication system resource utilization. After decades of excavations, time, frequency and coding technology potential, however, has to play to the limit, it is difficult to further improve. In this case, the smart antenna and multi-antenna technology came into being, become the key technologies of the various types of communication systems, array antenna pattern synthesis technology is the smart antenna a core technology. In the the array antenna comprehensive study, has emerged in many classical methods and theories. Array antenna comprehensive study has laid a solid theoretical foundation, but also has its own difficult to overcome the shortcomings, for example, the calculation of complex issues such as Scope. Evolutionary algorithm in handling such nonlinear multi-parameter non-differentiable and discontinuous problems, has its unique advantages. Thus, more and more evolutionary algorithm is applied to the array antenna synthesis. In this paper, chaos optimization algorithm and differential evolution algorithm to improve the mind evolutionary algorithm, proposed a new chaotic differential Mind Evolutionary Algorithm (Chaotic Differential Mind Evolutionary Algorithm referred to as CDMEA) and applied to the array antenna synthesis. Specific content includes: (1) Logistic, Tent, and the improved Tent chaotic map traversal characteristics. In mind evolutionary algorithm to generate the initial population, using the improved Tent mapping them guidance, spread evenly over the initial population in the solution space. (2) the differential evolution algorithm analysis, research, and its mutation operation introduced into the mind evolutionary algorithm alienation operation, while preserving the good genes in the evolution, but also to increase the diversity of the population and avoid optimization into a local optimum superior. (3) thinking chaotic differential evolution algorithm is applied to the array antenna integrated into three categories, and compared with the genetic algorithm optimization results. The optimization results show that the improved mind evolutionary algorithm can achieve better experimental results in a shorter time. (4) to study the advantages and disadvantages of the scrim the antenna array, it can be less picket antenna to obtain the narrower the width of the main beam, which greatly reduces the cost, but relatively sidelobe level higher. In this paper, the improved mind evolutionary algorithm to optimize its sidelobe level. Experimental results show that the improved mind evolutionary algorithms to optimize the results significantly superior to the genetic algorithm, and proved the effectiveness and practicality of the algorithm in the thinned array antenna problem.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Radio equipment,telecommunications equipment > Antenna > General issues
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