Abstract:

In order to get the accurate thermal conductivity of Phyllostachys pubescens to a certain extent and to improve existing research methods of bamboo’s thermal conductivity, the author adopted laser flash method to accurately measure the thermal conductivity of Phyllostachys pubescens, and based on it, a prediction model of neural networks was formed, which made the Phyllostachys pubescens’ thermal conductivity vary with different temperature and density. Because of the slow convergence rate of origin BP algorithm, the author made use of Trianlm function to train the prediction model of neural networks, thus obtained the ideal hidden layer neuron numbers, and analyzed the output prediction value of the model by linear analysis and tolerance analysis at the same time. The experimental results were as follows: the prediction model of neural networks was of higher accuracy which could predict Phyllostachys pubescens’ thermal conductivity to a certain extent, which could save lots of time and resources compared with regular experimental methods. The article tried to reveal the relationship between Phyllostachys pubescens’ thermal conductivity, temperature and density, providing theoretical evidences for the further study of Phyllostachys pubescens’ thermal physical properties.