改进蝴蝶算法的神经网络天线建模

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。

Improved neural network antenna modeling for butterfly algorithms

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.