Particle swarm optimization neural network model-based method for detecting moisture content of wood

Abstract

The invention discloses a particle swarm optimization neural network model-based method for detecting the moisture content of wood. A particle swarm is combined with a back propagation (BP) algorithm to finish neural network training, so that the training accuracy of a network model is enhanced; and the model is applied to the detection of the moisture content of wood, so that high detection accuracy is achieved. The method has the advantages that: 1) by the properties of randomized global optimization search and high convergence rate of a particle swarm optimization algorithm, overall optimization is performed on the weight of a network, so that the defects of low convergence rate and easy local minimum existing in the BP algorithm are overcome; 2) in the BP algorithm, an approximately optimal weight provided by the particle swarm optimization algorithm is taken as an initial value and further optimization is performed by using the characteristics of nonlinear mapping capability and high local optimization capability of the BP algorithm, so that an optimal value of a network weight is obtained; and 3) the moisture content of wood and an environmental temperature parameter are detected based on an electrical measuring method, a particle swarm optimization neural network model is established and is applied to the detection of the moisture content of wood, and the effectiveness of the method is verified.

Description

technical field [0001] The invention relates to a method for detecting moisture content of wood, in particular to a method for detecting moisture content of wood based on a particle swarm optimization neural network model, which is mainly used for detecting moisture content of wood during the drying process of wood in the field of wood processing. Background technique [0002] Wood is one of the most widely used and sustainable engineering materials. How to make effective use of limited wood resources and reduce energy consumption has attracted widespread attention from governments around the world. my country is a Shaolin country. How to better improve the utilization rate of wood and improve the performance of wood has become one of the problems that need to be solved urgently in front of wood scientists. To use wood, it must first be dried. The moisture content of wood is a key parameter to control the drying process, and its measurement accuracy will directly affect th...

AI Technical Summary

Problems solved by technology

The BP algorithm is an algorithm based on gradient descent. In theory, it cannot guarantee that the convergence result is the global optimal [10, 11], and it is easy to fall into the local optimal value.

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