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In order to accurately obtain the mechanical properties of wood fiber, paper, wood fiber-reinforced materials between ring density and moisture content as a model input, neural network modeling by particle swarm optimization PSO optimal control model to predict the longitudinal elasticity of wood fiber modulus, and thus a correct understanding of the true understanding of the mechanical properties...
Particle swarm optimization algorithm based neural network construction has been presented to calibrate the complex nonlinear relationship between modulus of elasticity (MOE) and wood physical property parameters. Consider that the traditional BP algorithm has shortcomings of converging slowly and easily trapping a local minimum value, a hybrid algorithm using particle swarm optimization (PSO) and...
As an organic whole, there are unknown nonlinear relationships existing in the different parameters of wood. This paper is aimed to solve the complex nonlinear relationship of wood parameters. Maoershan larch is selected for the test material. A neural network model is adopted with the density of wood ring and moisture content as the model inputs, wood vertical elastic modulus as the output. Particle...
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