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Soft Computing Techniques Implementation and Challenges in Antenna Engineering

机译:天线工程中的软计算技术实施与挑战

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This paper shows the usefulness of soft computing techniques in antenna parameters calculation, design and optimization. Since long time, different soft computing techniques have been drawn attention of researchers, such as Artificial neural network (ANNs), Fuzzy Logic, Radial basis function neural network (RBFNNs) and evolutionary algorithms (EAs). Some popular EAs are Genetic algorithm and its variants, Particle swarm optimization (PSO), Ant Colony, Differential Evolution, Bacterial Foraging Optimization (BFO) and Biogeography Based Optimization (BBO). The main focus in this paper is to review implementation and optimization of different antenna structures through different optimization techniques.
机译:本文显示了天线参数计算,设计和优化的软计算技术的有用性。自长时间,已经引起了不同的软计算技术,研究人员,例如人工神经网络(ANNS),模糊逻辑,径向基函数神经网络(RBFNNS)和进化算法(EAS)。一些流行的EA是遗传算法及其变体,粒子群优化(PSO),蚁群,差异演化,细菌觅食优化(BFO)和基于生物地理的优化(BBO)。本文的主要重点是通过不同的优化技术审查不同天线结构的实施和优化。

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