| 南敬昌,黄 菊,张慧妹.改进蝴蝶算法的神经网络天线建模[J].电子测量与仪器学报,2023,37(12):166-175 |
| 改进蝴蝶算法的神经网络天线建模 |
| Improved neural network antenna modeling for butterfly algorithms |
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| DOI： |
| 中文关键词: 多层前馈神经网络 双陷波蜂窝结构分形超宽带天线 蝴蝶算法 天牛须算法 权值优化 |
| 英文关键词:multilayer feedforward neural networks dual notch cellular fractal ultra-wideband antenna butterfly algorithm beetle
antennae search algorithm weight optimization |
| 基金项目:国家自然科学基金(61971210) 、辽宁省应用基础研究计划项目(2022JH2 / 101300275)资助 |
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| 中文摘要: |
| 为提高天线建模效率,改变传统建模方法速度慢、效率低的问题,提出了一种用改进的蝴蝶算法(BOA)优化多层前馈
神经网络(back propagation neural network, BPNN)的天线建模方法。 首先,以多层前馈神经网络为基础网络,建立蝴蝶算法优
化的 BP 神经网络,解决 BP 神经网络预测精度低的问题。 其次,在蝴蝶算法中融入天牛须算法(BAS),用天牛须算法替代蝴蝶
算法的局部寻优过程,减小蝴蝶算法的空间复杂度、解决蝴蝶算法易陷入局部最小值的问题,创建改进的 BOA-BP 神经网络对
天线进行精准建模。 设计实例表明,该网络的预测精度达到了 99. 60%,相比于传统的 BPNN 和未改进蝴蝶算法优化的 BPNN,
预测 S11 的误差分别减少了 47%和 40. 9%。 此外,改进的 BOA 算法的运行时间相对于粒子群算法和遗传算法也分别减小了
80. 86%和 82. 79%,大大降低了网络运行的时间成本。 综上,改进的 BOA 优化后的 BPNN 的建模精度和速度均得到了提高,验
证了改进的蝴蝶算法作为一种新型神经网络优化策略的可行性和有效性。 |
| 英文摘要: |
| To improve the efficiency of antenna modeling and change the problem of slow speed and low efficiency of traditional modeling
methods, an antenna modeling method using improved butterfly algorithm ( BOA) to optimize multilayer feedforward neural network
(back propagation neural network ( BPNN)) is proposed. Firstly, the BP neural network optimized by the butterfly algorithm is
established with the multilayer feedforward neural network as the base network to solve the problem of low prediction accuracy of the BP
neural network. Secondly, the beetle antennae search (BAS) algorithm is integrated into BOA, replacing the local optimization process
of the butterfly algorithm with the beetle antennae search algorithm to reduce the spatial complexity of the BOA, solve the problem that
the BOA is prone to fall into local minima, and create an improved BOA-BP neural network for accurate antenna modeling. The design
example shows that the prediction accuracy of the network reaches 99. 60%, and the prediction error is reduced by 47% and 40. 9%
compared with the traditional BPNN and the BPNN optimized by the unimproved butterfly algorithm, respectively. In addition, the
running time of the improved BOA algorithm is reduced by 80. 86% and 82. 79% compared with the particle swarm algorithm and the
genetic algorithm, which greatly reduces the running time cost of the network. In summary, the modeling accuracy and speed of the
improved BOA-optimized BPNN are improved, which verifies the feasibility and effectiveness of the improved butterfly algorithm as a
novel neural network optimization strategy. |
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