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A Neural Network based Model for Temperature Prediction in High Power Microwave Heating System

机译:基于神经网络基于高功率微波加热系统的温度预测模型

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The applications of neutral network based temperature prediction for microwave heating system has been comprehensively investigated. Temperature prediction is essential due to the characteristics of high power microwave heating system, a.k.a. big inertia and pure time-delay. This work proposes a BP neutral network based model associated with a Genetic Algorithm (GA), which aims at improving the temperature prediction accuracy. Our numerical experiments show that the BP neutral network with tansig transfer function and trainlm training function is the most appropriate one in our case based on minimum measures of error. Experiments on the pure water and coal obtained in our simulated experimental facilities were used for training and testing the proposed optimal neutral network. Based on our experimental results, the GA based BP neural network model can significantly improve the prediction accuracy as well as the convergence speed. As a result, the proposed system is suitable for the real-world applications.
机译:全面研究了中性网络基于微波加热系统的温度预测的应用。由于高功率微波加热系统,A.K.A.大惯性和纯时滞的特点,温度预测至关重要。该工作提出了一种与遗传算法(GA)相关的基于BP中性网络的模型,其旨在提高温度预测精度。我们的数值实验表明,具有TANSIG传递函数和TRASTLM训练功能的BP中性网络是我们基于最低误差措施的情况下最合适的网络。我们模拟实验设施中获得的纯水和煤的实验用于培训和测试所提出的最佳中性网络。基于我们的实验结果,基于GA的BP神经网络模型可以显着提高预测精度以及收敛速度。因此,所提出的系统适用于现实世界应用。

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