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

A neural network model and particle swarm optimization technology, applied in the field of wood processing, can solve the problems of falling into the local optimum value, unable to guarantee the global optimum of the convergence result, etc.

Inactive Publication Date: 2011-05-25
NORTHEAST FORESTRY UNIVERSITY +2
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The BP algorithm is an algorithm based on gradient descent. In theory, it cannot guarantee that the c

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

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[0051] The present invention proposes a wood moisture content detection method of particle swarm optimization neural network model. In order to obtain the globally optimal neural network weight, the neural network can be trained by combining PSO and BP algorithm. The training process is divided into two steps . First, the connection weights of the network are trained by using the global optimization and fast convergence characteristics of the PSO algorithm. However, the PSO algorithm has low convergence accuracy and poor fine-tuning, and can quickly converge to the optimal solution, but it is difficult to obtain the optimal solution. In order to solve this problem, in the second step, the BP algorithm has the characteristics of infinite approximation ability and strong local optimization ability. In a space B(W * ), use the BP algorithm to further optimize, and get the optimal value W of the network weight * . Using such two-step training, give full play to the respective a...

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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...

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Application Information

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IPC IPC(8): G01N27/00G06N3/02G06N3/08
Inventor 张佳薇曹军李明宝
Owner NORTHEAST FORESTRY UNIVERSITY
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