Artificial neural network modeling for predicting final moisture content of individual Sugi (Cryptomeria japonica) samples during air-drying

Watanabe, Ken; Matsushita, Yasuhiro; Kobayashi, Isao; Kuroda, Naohiro

doi:10.1007/s10086-012-1314-2

Cited by 22 publications

(15 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial neural networks are increasingly being used for modeling in the field of wood science. For instance, in the field of wood drying, Avramidis (2006) [13] predicted the drying rate of wood based on neural network construction model; Zhang Dongyan (2008) [14] constructed a neural network model for predicting wood MC during conventional drying;İlhan Ceylan (2008) [15] used neural network models to study wood drying characteristics; Watanabe (2013Watanabe ( , 2014 [16,17] employed artificial neural network model to predict the final moisture content of Sugi (Cryptomeria japonica) during drying and evaluate the drying stress on the wood surface. Ozsahin (2017) [18] utilized artificial neural networks to successfully predict the equilibrium moisture content and specific gravity of heat-treated wood.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Chai

Chen

Cai

et al. 2018

Forests

View full text Add to dashboard Cite

The moisture content (MC) control is vital in the wood drying process. The study was based on BP (Back Propagation) neural network algorithm to predict the change of wood MC during the drying process of a high frequency vacuum. The data of real-time online measurement were used to construct the model, the drying time, position of measuring point, and internal temperature and pressure of wood as inputs of BP neural network model. The model structure was 4-6-1 and the decision coefficient R2 and Mean squared error (Mse) of the training sample were 0.974 and 0.07355, respectively, indicating that the neural network model had superb generalization ability. Compared with the experimental measurements, the predicted values conformed to the variation law and size of experimental values, and the error was about 2% and the MC prediction error of measurement points along thickness direction was within 2%. Hence, the BP neural network model could successfully simulate and predict the change of wood MC during the high frequency drying process.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Chai

Chen

Cai

et al. 2018

Forests

View full text Add to dashboard Cite

show abstract

“…Step 2: The LSSVM model is learned and trained by the training sample set, the F value of each ant individual is calculated by (9), and the pheromone density of each ant is calculated by (10) [37]. If the F value is smaller, the σ value is larger; conversely, if the F value is larger, the σ value is smaller.…”

Section: B Operating Steps Of Modified Ant Colony Searchmentioning

confidence: 99%

“…The experiment results indicated that the model had better generalization ability and higher forecasting accuracy. In recent years, the artificial neural network (ANN) has a widespread application on nonlinear prediction, and some achievements have been made in the prediction of moisture content [9], [10]. Zhang et al [11] used the neural network method to identify the wood drying system and established the neural network model of the wood drying process.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Wood Moisture Content Based on Modified Ant Colony Algorithm to Optimize LSSVM Parameters

Sun

2020

IEEE Access

View full text Add to dashboard Cite

Wood moisture content (WMC) is an important technical index used in the wood drying process, and assessing its change accurately and reliably is the key to improving wood drying quality. In order to improve the accuracy and reliability of WMC forecasting, a modeling method is proposed that uses a modified ant colony algorithm (MACA) to optimize the least square support vector machine (LSSVM). The MACA combines the large-step size global search with the small-step size local fine search to obtain the optimal parameter combination automatically and are tested by five standard functions. Then the MACA-LSSVM model is proposed to predict the WMC and compared with back propagation neural network (BP-NN), LSSVM model, and ant colony optimization LSSVM (ACO-LSSVM). The drying data from a small-sized wood drying kiln independently developed by Northeast Forestry University are taken as the samples for analyzing. The results indicate that the root mean square relative error (RMSRE) obtained by the proposed method (MACA-LSSVM) is only 1.82%, which is 0.77%, 0.50%, and 0.20% less than those of the BP-NN, LSSVM, and ACO-LSSVM models. The forecasting time are 0.0070 s, 0.0030 s, and 0.0010 s shorter, respectively. The relative error (RE) and the mean absolute error (MAE) are also lower than those of the latter three models. The MACA-LSSVM shows the characteristics of low computational complexity, fast convergence speed, high prediction accuracy and strong generalization ability, and the prediction effect is ideal. This model can provide the theoretical support for intelligent control of the wood drying process.INDEX TERMS Ant colony algorithm, least square support vector machine, parameter optimization, wood moisture content.

show abstract

“…The topology of the BP neural network is constructed by connecting the hidden layers. The input layer and hidden layer are activated via a tangent Sigmoid function, and the hidden layer and output layer are connected via a linear function (Watanabe 2013). The neuron number of predictive models have a significant impact, and the neuron nodes are estimated according to the actual need in the input layer and output layer.…”

Section: Bp Neural Network Prediction Modelmentioning

confidence: 99%

Prediction of wood elastic strain development trend in conventional drying process based on GM-BP model

Wan-hui

Zhao

Zhu

et al. 2020

BioRes

View full text Add to dashboard Cite

The elastic strain of wood reflects the nature (stretching or compression) and the magnitude of the drying stress at that time during the conventional drying process. The accurate prediction of strain is important to optimize the drying process and to improve drying speed and quality. In this work, the elastic strain was measured in real time, and moisture content was measured by periodic weighing during the drying process. Using these data, the GM (1,1) grey prediction model was used to predict moisture content in adjacent periods in the future. Based on the moisture content predicted by GM (1,1), a BP neural network was constructed to predict the development trend of elastic strain in the surface layer and core layer. The prediction results of the GM-BP combination model showed that the fitting error range of the prediction of the surface layer elastic strain was [-5×10-3~5×10-3], with a mean square error (MSE) of 2.31×10-7. The elastic strain of the core layer was [-2×10-3~2×10-3], and the MSE was 3.86×10-8. Thus, the GM-BP model achieved high accuracy for predicting the development trend of elastic strain. It can provide a new method and innovative thinking for the optimization and control of wood drying process.

show abstract

Artificial neural network modeling for predicting final moisture content of individual Sugi (Cryptomeria japonica) samples during air-drying

Cited by 22 publications

References 13 publications

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Forecasting of Wood Moisture Content Based on Modified Ant Colony Algorithm to Optimize LSSVM Parameters

Prediction of wood elastic strain development trend in conventional drying process based on GM-BP model

Contact Info

Product

Resources

About