微波加热是一种与被加热物直接相互作用的选择性加热方式，具有清洁、节能、减排等特点。针对工业物料作为微波加热负载时，其温度非线性变化的特点，以微波工业加热过程中的多维、海量参数为研究对象，基于泛函接神经网络模型提取样本数据的深度特征，提出了一种基于布谷鸟搜索算法，优化BP神经网络的网络参数，建立了以"数据驱动"为手段微波加热工业物料温度模型。仿真实验结果证明了所提出模型的准确性、实时性。

Microwave heating, an alternative heating method, can directly interact with objects to be heated. This method will dramatically improve energy utilization rate, which is clean, energy-saving and emission reduction. According to the nonlinear change of temperature when industrial material is used as microwave heating load, regarding the dimensional and mass parameters in microwave industrial heating processes as research objects, and also based on the functional-linked neural network to extract the deep features of sample data, a cuckoo search algorithm is proposed to optimize the parameters of BP neural network, thus establishing the industrial microwave heating temperature prediction model based on the "data driven" method. Simulation results show the accuracy and instantaneity of the temperature prediction model proposed in this paper.

国家重点基础研究973课题资助项目（2013CB328903）。