Prediction of the equilibrium moisture content and specific gravity of thermally modified wood via an Aquila optimization algorithm back-propagation neural network model

Chen, Yao; Wang, Wei; Li, Ning

doi:10.15376/biores.17.3.4816-4836

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…To further assess the IBWO-BP model's effectiveness in predicting the physical and mechanical properties of heat-treated wood, we conducted a comparison with previous studies. The comparative analysis model will be developed in four different models: the Aquila optimizer-BP model (AO-BP) [6], the nonlinear and adaptive grouping gray wolf optimisation-BP model (NAGGWO-BP) [29], a decision tree regression (DT) [49], and multiple linear regression (MLR) [50].…”

Section: Results Of Comparison Analysis With Earlier Prediction Modelsmentioning

confidence: 99%

“…Among practical applications, BP neural networks can be used for tasks such as classification, regression, and clustering. It has also been widely used in wood science, for instance, to predict the EMC and SG [6] and the surface roughness [38] of wood.…”

Section: Bp Neural Networkmentioning

confidence: 99%

“…He heated air-dried wood in superheated steam at 150 • C and discovered that the hygrometric properties of heat-treated wood were reduced. Moreover, heat treatment is typically implemented to change various properties of wood, such as colour [3], bonding strength [4], gloss [5], equilibrium moisture content (EMC) [6], and durability [7]. Previous research has also extensively studied heat treatment's effects on dimensional stability and mechanical properties [8].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the traditional BP neural network for prediction has the disadvantages of poor data generalization and low fitting accuracy [27]. To overcome these limitations, meta-heuristic algorithms (MHAs) have been used by researchers to optimise the BP neural networks' threshold and weights [6]. Lei et al [28] optimised the BP neural network to predict oak absolute dry density employing nonlinear weighted particle swarm optimisation (IPSO).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Physical and Mechanical Properties of Heat-Treated Wood Based on the Improved Beluga Whale Optimisation Back Propagation (IBWO-BP) Neural Network

Wang,

et al. 2024

Forests

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The physical and mechanical properties of heat-treated wood are essential factors in assessing its appropriateness for different applications. While back-propagation (BP) neural networks are widely used for predicting wood properties, their accuracy often falls short of expectations. This paper introduces an improved Beluga Whale Optimisation (IBWO)-BP model as a solution to this challenge. We improved the standard Beluga Whale Optimisation (BWO) algorithm in three ways: (1) use Bernoulli chaos mapping to explore the entire search space during population initialization; (2) incorporate the position update formula of the Firefly Algorithm (FA) to improve the position update strategy and convergence speed; (3) apply the opposition-based learning based on the lens imaging (lensOBL) mechanism to the optimal individual, which prevents the algorithm from getting stuck in local optima during each iteration. Subsequently, we adjusted the weights and thresholds of the BP model, deploying the IBWO approach. Ultimately, we employ the IBWO-BP model to predict the swelling and shrinkage ratio of air-dry volume, as well as the modulus of elasticity (MOE) and bending strength (MOR) of heat-treated wood. The benefit of IBWO is demonstrated through comparison with other meta-heuristic algorithms (MHAs). When compared to earlier prediction models, the results revealed that the mean square error (MSE) decreased by 39.7%, the root mean square error (RMSE) by 22.4%, the mean absolute percentage error (MAPE) by 9.8%, the mean absolute error (MAE) by 31.5%, and the standard deviation (STD) by 18.9%. Therefore, this model has excellent generalisation ability and relatively good prediction accuracy.

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Section: Results Of Comparison Analysis With Earlier Prediction Modelsmentioning

confidence: 99%

Section: Bp Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Physical and Mechanical Properties of Heat-Treated Wood Based on the Improved Beluga Whale Optimisation Back Propagation (IBWO-BP) Neural Network

Wang,

et al. 2024

Forests

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“…Chen et al [ 35 ] suggested two prediction models based on an AO algorithm and a back-propagation neural network model for estimating the equilibrium moisture level and precise gravity of thermally modified wood. The efficacy and accuracy of the suggested model were proved by comparison with two other models, as well as an ANN.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

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Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

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Research on non-destructive and rapid detection technology of foxtail millet moisture content based on capacitance method and Logistic-SSA-ELM modelling

Qiu,

Li,

Huang

et al. 2024

Front. Plant Sci.

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Moisture content testing of agricultural products is critical for quality control, processing efficiency and storage management. Testing foxtail millet moisture content ensures stable foxtail millet quality and helps farmers determine the best time to harvest. A differential capacitance moisture content detection device was designed based on STM32 and PCAP01 capacitance digital converter chip. The capacitance method combined with the back-propagation(BP) algorithm and the extreme learning machine(ELM) algorithm was chosen to construct an analytical model for foxtail millet moisture content, temperature, and volume duty cycle. This work performs capacitance measurements on foxtail millet with different moisture contents, temperatures, and proportions of the measured substance occupying the detection area (that is, the volumetric duty cycle). On this foundation, the sparrow search algorithm (SSA) is used to optimize the BP and ELM models. However, SSA may encounter problems such as falling into local optimization solutions due to the reduction of population diversity in the late iterations. As a consequence, Logistic algorithm is introduced to optimize SSA, making it more appropriate for solving specific problems. Upon comparative analysis, the model predicted using the Logistic-SSA-ELM algorithm was more accurate. The results indicate that the predicted values of prediction set coefficient of determination (RP), prediction set root mean square error (RMSEP) and prediction set ratio performance deviation (RPDP) were 0.7016, 3.7150 and 1.4035, respectively. This algorithm has excellent prediction performance and can be used as a model for detection of foxtail millet moisture content. In view of the important role of foxtail millet moisture content detection in acquisition and storage, it is particularly important to study a nondestructive and fast online real-time detection method. The designed capacitive sensor with differential structure has well stabilization and high accuracy, which can be further studied in depth and gradually move towards the general trend of agricultural development of smart agriculture and precision agriculture.

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Prediction of the equilibrium moisture content and specific gravity of thermally modified wood via an Aquila optimization algorithm back-propagation neural network model

Cited by 8 publications

References 18 publications

Prediction of Physical and Mechanical Properties of Heat-Treated Wood Based on the Improved Beluga Whale Optimisation Back Propagation (IBWO-BP) Neural Network

Prediction of Physical and Mechanical Properties of Heat-Treated Wood Based on the Improved Beluga Whale Optimisation Back Propagation (IBWO-BP) Neural Network

A Comprehensive Survey on Aquila Optimizer

Research on non-destructive and rapid detection technology of foxtail millet moisture content based on capacitance method and Logistic-SSA-ELM modelling

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