Investigation and neural network prediction of wood bonding quality based on pressing conditions

Bardak, Selahattin; Tiryaki, Sebahattin; Nemli, Gökay; Aydın, Aytaç

doi:10.1016/j.ijadhadh.2016.02.010

Cited by 29 publications

(16 citation statements)

References 25 publications

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“…Gu et al [26] used an improved BP neural network based on GA algorithm to develop the yield-irrigation prediction model for subsurface drip irrigation system. Bardak et al [27] presented an application of ANN to predict the wood bonding quality based on pressed conditions. Heidari et al [28] optimized the multilayer perceptron NN using the GRO algorithm, which was applied to many popular datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Quality Prediction Model Based on Novel Elman Neural Network Ensemble

Zhang

2019

Complexity

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In this paper, we propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality prediction, thus achieving the quality control of designed products at the product design stage. First, the Elman NN parameters are optimized using the grasshopper optimization (GRO) method, and then the weighted average method is improved to combine the outputs of the individual NNs, where the weights are determined by the training errors. Simulations were conducted to compare the proposed method with other NN methods and evaluate its performance. The results demonstrated that the proposed algorithm for quality prediction obtained better accuracy than other NN methods. In this paper, we propose a novel Elman NN ensemble model for quality prediction during product design. Elman NN is combined with GRO to yield an optimized Elman network ensemble model with high generalization ability and prediction accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Quality Prediction Model Based on Novel Elman Neural Network Ensemble

Zhang

2019

Complexity

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“…[15][16][17]. In this paper, we aimed to determine the wood density based on intensity of RGB color on wood surface, develop the calibration equation using color values and evaluate the calibration efficiency by prediction the wood density using Fuzzy logic.…”

Section: Figure 1 Fuzzy Inference Systemmentioning

confidence: 99%

Prediction of Wood Density by Using Red-Green-Blue (RGB) Color and Fuzzy Logic Techniques

Bardak¹,

Bardak²

2017

Journal of Polytechnic

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Prediction of wood density by using redgreen-blue (rgb) color and fuzzy logic techniques Kırmızı-yeşil-mavi (kym) renk ve bulanık mantık teknikleri kullanılarak odun yoğunluğu tahmini Yazar(lar) (Author(s)): Timuçin BARDAK, Selahattin BARDAKBu makaleye şu şekilde atıfta bulunabilirsiniz(To cite to this article): Bardak T., and Bardak S., "Prediction of wood density by using red-green-blue (rgb) color and fuzzy logic techniques", Politeknik Dergisi, 20(4): 979-984, (2017). Politeknik Dergisi, 2017; 20 (4) : 979-984 Journal of Polytechnic, 2017; 20 (4) : 979-984 ABSTRACTDensity is an important wood property since it correlates to mechanical properties of wood. Fuzzy logic, among the various available Artificial Intelligence techniques, emerges as a good technique in predicting. Digital image analysis is an powerful tool to obtain meaningful data out of an image. In this study, digital image processing based on a red-green-blue (RGB) color examination was practiced to measure the intensity of wood color. Densities of the test samples were measured. Then, a new fuzzy logic model was developed based on these measured values and RGB color intensity of wood. Afterwards, the experimental and modeling data results were compared. 98.17% accuracy was observed between the measurement and the fuzzy logic model. Consequently, Fuzzy logic is visable method for the prediction of the wood density. Keywords

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“…Compared with other artificial intelligence techniques such as Gaussian process(GP), 32,33 Bayesian regression, 34 neural networks (NNs), 35 Gaussian process mixed model (GPMM), 36 fuzzy least squares support vector machines (FLS-SVM) is of unique advantages such as requiring less modelling data, simple calculation and quick identification capacity in resolving the nonlinear and high dimension problems with small sample sets. Moreover, an adaptive variable chaos immune algorithm (AVCIA) is useful for global optimization of nonlinear and high dimension problems.…”

Section: Introductionmentioning

confidence: 99%

Parameter-identification investigations on the hysteretic Preisach model improved by the fuzzy least square support vector machine based on adaptive variable chaos immune algorithm

Jiaqiang

Qian

Zhu

et al. 2017

Journal of Low Frequency Noise, Vibration and Active Control

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In order to solve the hysteretic character of the piezoelectric material for application, the initial weight factors of the hysteretic units are calculated by the Preisach theory and the first-order reversal curves test data, a hysteretic Preisach model based on the improved fuzzy least square support vector machine (improved FLS-SVM) is established. In the established model, the fuzzy least square support vector machine is introduced to calculate more weight factors of the hysteretic units and the adaptive variable chaos immune algorithm is introduced to optimize the penalty factor and the kernel parameter of the FLS-SVM (the penalty factor c ¼ 35 and the kernel parameter ¼ 1.35 are obtained). Moreover, the quadratic polynomial interpolation method is used to eliminate the sawtooth phenomenon. The validity of established model reveals that fuzzy least square support vector machine method based on adaptive variable chaos immune algorithm (FLS-SVMAVCIA) is more accurate than FLS-SVM method according to application results of the real actuators (the absolute mean error of the FLS-SVMAVCIA model is less than 1 mm and its maximum error is less than 2 mm). As a result, the hysteretic phenomenon can be effectively eliminated by the hysteretic Preisach model based on the FLS-SVMAVCIA method.

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Investigation and neural network prediction of wood bonding quality based on pressing conditions

Cited by 29 publications

References 25 publications

Quality Prediction Model Based on Novel Elman Neural Network Ensemble

Quality Prediction Model Based on Novel Elman Neural Network Ensemble

Prediction of Wood Density by Using Red-Green-Blue (RGB) Color and Fuzzy Logic Techniques

Parameter-identification investigations on the hysteretic Preisach model improved by the fuzzy least square support vector machine based on adaptive variable chaos immune algorithm

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