基于RBF神经网络的三维温度场重建算法

摘要

声学法测量温度场是目前很具发展前景的一种温度场测量方法,而重建算法是实现声学法温度场重建的关键.提出了一种基于径向基函数(Radical Basis Function,RBF)神经网络的三维温度场重建算法.通过对被测温度场进行三维离散余弦变换(Discrete Cosine Transform,DCT),再利用RBF神经网络良好的函数逼近能力,实现DCT低阶次项系数向量与声波路径平均温度向量间的映射关系,最后通过逆离散余弦变换实现被测温度场的重建.进行了对模拟温度场的重建仿真,结果表明,该算法具有温度场重建精度高、速度快等特点.%Temperature field acoustic measurement is a promising temperature field measurement method at present,and reconstruction algorithm is essential to temperature field image.This paper presented a new algorithm based on RBF neural network to reconstruct the three-dimensional temperature field.The algorithm used three-dimension discrete cosine transform (DCT) on temperature field and established a mapping relation between low order term coefficient vector and sound wave path average temperature vector then implemented the mapping relation using radical basis function (RBF) neural network that has strong function fitting ability.The three-dimensional temperature field was reconstructed by using inverse three-dimension discrete cosine transform.Simulation results show that the algorithm features high precision and high-speed.