| 唐朝国.基于改进Levy飞行的PSO湿度传感器补偿算法[J].电子测量与仪器学报,2020,34(3):119-125 |
| 基于改进Levy飞行的PSO湿度传感器补偿算法 |
| PSO humidity sensor compensation algorithm based on improved Levy flight |
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| DOI： |
| 中文关键词: BP神经网络 温度补偿 PSO算法 Levy飞行 |
| 英文关键词:BP neural network temperature compensation particle swarm optimization algorithm Levy flight algorithm |
| 基金项目:中铁二院科研项目（KYY2019117（19-20））资助 |
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| 摘要点击次数: 364 |
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| 中文摘要: |
| 针对综合管廊中温度变化导致湿度传感器数据失真的问题,提出一种改进Levy飞行的粒子群优化（PSO）算法（ILPSO),用于补偿数据误差。首先,建立一个预测误差的神经网络,通过PSO寻找网络初始参数;然后,在PSO寻找过程中加入改进的Levy飞行,粒子飞行的概率与到最优粒子的距离成反比,靠近最优粒子时以较大概率反向逃离最优粒子,克服粒子早熟问题;最后,网络以PSO的输出作为初始参数重新训练。在算法寻优性能实验中,相比于其他测试算法,ILPSO算法的寻优能力更强,在传感器误差测试实验以及稳定性实验中,ILPSO算法的补偿效果最好,补偿后的湿度值误差在5%以内,均方误差（MSE）最低,稳定性最好。实验结果表明,与传统的Levy飞行相比,ILPSO算法对误差预测网络的适应度更强,收敛更快,提高了湿度传感器温度补偿的准确性以及稳定性。 |
| 英文摘要: |
| Aiming at the problem of humidity sensor data distortion caused by temperature changes in utility tunnel, an improved Levy flight particle swarm optimization algorithm (ILPSO) is proposed to compensate data errors. Firstly, a neural network with prediction error is established, and the initial parameters of the network are found by PSO. Then, an improved levy flight is added in the PSO search process, the probability of particle flight is inversely proportional to the distance to the optimal particle, and when approaching the optimal particle, it reversely escapes from the optimal particle with a larger probability, thus overcoming the problem of particle premature. Finally, the network retrains with the output of PSO as the initial parameter. In the sensor error test experiment and stability experiment, the compensation effect of ILPSO algorithm is the best, the humidity value error after compensation is less than 5%, MSE is the lowest, and the stability is the best. In summary, compared with the traditional Levy flight, ILPSO algorithm has stronger adaptability to error prediction network, faster convergence, and improves the accuracy and stability of humidity sensor temperature compensation. |
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