In this paper, a recognition model based on the improved hybrid particle swarm optimisation (HPSO) optimised backpropagation network (BP) is proposed to improve the efficiency of radar working state recognition. First, the model improves the HPSO algorithm through the nonlinear decreasing inertia weight by adding the deceleration factor and asynchronous learning factor. Then, the BP neural network’s initial weights and thresholds are optimised to overcome the shortcomings of slow convergence rate and falling into local optima. In the simulation experiment, improved HPSO-BP recognition models were established based on the datasets for three radar types, and these models were subsequently compared to other recognition models. The results reveal that the improved HPSO-BP recognition model has better prediction accuracy and convergence rate. The recognition accuracy of different radar types exceeded 97%, which demonstrates the feasibility and generalisation of the model applied to radar working state recognition.

中文翻译：

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基于改进HPSO-BP的雷达工作状态识别

为了提高雷达工作状态识别效率，提出了一种基于改进的混合粒子群优化（HPSO）优化反向传播网络（BP）的识别模型。首先，该模型通过添加减速因子和异步学习因子，通过非线性减小惯性权重来改进HPSO算法。然后，对BP神经网络的初始权重和阈值进行优化，以克服收敛速度慢和陷入局部最优的缺点。在仿真实验中，基于三种雷达类型的数据集建立了改进的HPSO-BP识别模型，随后将这些模型与其他识别模型进行了比较。结果表明，改进的HPSO-BP识别模型具有较好的预测精度和收敛速度。