Journal of Intelligent & Fuzzy Systems - Volume 43, issue 5

Authors: Wang, Haolun | Zhang, Faming

Article Type: Research Article

Abstract: Frank operations are more robust and flexible than other algebraic operations, and interaction operational laws consider interrelationship between membership functions in Pythagorean fuzzy number. Combining the strengths of both, we define some Frank interaction operational laws of Pythagorean fuzzy numbers for the first time in this article. Based on this, the Pythagorean fuzzy Frank interaction weighted averaging and geometric operators are developed. Meanwhile, we discuss their basic properties and related special cases. Furthermore, a novel multiple attribute decision-making framework is established based on the modified WASPAS method in Pythagorean fuzzy environment. The proposed method is implemented in a real-case study …of cloud computing product selection to test the proposed methodology’s plausibility. A sensitivity analysis is conducted to verify our method’s reliability, and the effectiveness and superiority are illustrated by comparative study. Show more

Keywords: Frank interaction operational laws, Pythagorean fuzzy Frank interaction aggregation operators, PyF-ITARA, WASPAS, cloud computing product

DOI: 10.3233/JIFS-213152

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5793-5816, 2022

Authors: Yao, Xuan | Wang, Hai | Xu, Zeshui

Article Type: Research Article

Abstract: Preference relations are often used to derive the priority of attributes and/or alternatives. Linguistic term with weakened hedges (LTWH) as a type of complex linguistic expressions can more straightforwardly describe the linguistic information provided by DMs when evaluating under uncertainties. The preference relations represented by LTWHs are an effective tool to model linguistic information. The concept and properties of additive consistency have been proposed before. This paper aims to study the multiplicative consistency of preference relations expressed by LWTHs. This paper constructs the principle of inspection for multiplicative consistency. Especially, the theories and algorithms for consistency checking and improving are …proposed. We develop an automatic approach to improve a LWHPR that is not multiplicatively consistent. Finally, we demonstrate the practicality of the proposed method through a case study of evaluating the attributes in the prevention of haze pollution in China. Show more

Keywords: Preference relations, multiplicative consistency, linguistic term with weakened hedges

DOI: 10.3233/JIFS-213170

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5817-5832, 2022

Authors: Zhang, Qinghui | Tian, Xinxin | Chen, Weidong | Yang, Hongwei | Lv, Pengtao | Wu, Yong

Article Type: Research Article

Abstract:  Unsound wheat kernel recognition is an important part of wheat quality inspection, and it is also a key indicator to measure wheat quality. Research on unsound wheat kernel recognition is of great significance to the correct evaluation of wheat quality. The existing researches on unsound wheat kernel recognition are mainly to directly optimize the classical classification networks, and the recognition effect is often unsatisfactory due to insufficient training data. Aiming at the problem that the recognition rate of unsound wheat kernels is not ideal due to the lack of training data, we propose a Transfer Learning Feature Fusion (TLFF) model. …The model uses transfer learning and feature fusion to identify unsound wheat kernels. First, feature extraction is performed by deep Convolutional Neural Networks (CNNs) VGG-16 and VGG-19 pre-trained on the large public dataset ImageNet. Then, the features extracted by the pre-trained neural networks are fused and classified through the flattening layer, fully connected layer, Dropout layer, and Softmax layer. We conduct experiments on single model, two-model fusion, three-model fusion, and four-model fusion, and select the three-model fusion scheme to perform this task. Finally, we vote on the output results of the three best fusion models to further improve the recognition rate. The pre-trained models we use are trained on a large public dataset ImageNet. Since the scale of the dataset is very large, these pre-trained models also have good generalization performance for images other than ImageNet dataset. Therefore, although our dataset is small, we can still achieve good recognition results. Experimental results show that the recognition performance of the TLFF model is significantly better than the existing unsound wheat kernel recognition models. Show more

Keywords: Transfer learning, feature fusion, unsound wheat kernel recognition, convolutional neural network, voting

DOI: 10.3233/JIFS-213195

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5833-5858, 2022

Authors: Zhao, Zhen-Yu | Ma, Xu

Article Type: Research Article

Abstract: The power industry has significantly contributed to the prosperity of the national economy, and accurate prediction can reflect the development trend of the power system and power market. The short-term electricity consumption of a country exhibits both annual growth certainty and random change uncertainty, which can be suitably considered with the grey forecasting model. Regarding the short-term trends of electricity consumption in China, this study established an optimized multivariate grey forecasting model with variable background values (OGM(1, N) model) to forecast the electricity consumption level in China. The established model could be converted into the GM(1, N) model and different …variant models by adjusting the model parameters. With Beijing, Tianjin and Shanghai as examples, the OGM(1, N) model is compared to the GM(1, N) model and its variant model. The excellent prediction results confirm the feasibility of the proposed model. Then, the proposed model is applied to study China’s electricity consumption. The research results indicated that the OGM(1, N) model attains an extraordinarily high precision in the prediction of electricity consumption and can provide a practical reference for accurate electricity consumption prediction. Show more

Keywords: Electricity consumption, multivariate grey forecasting model, variable background values, China

DOI: 10.3233/JIFS-213210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5859-5875, 2022

Authors: Babypriya, B. | Renoald, A. Johny | Shyamalagowri, M. | Kannan, R.

Article Type: Research Article

Abstract: In the context of this paper a three phase grid connected Photo-Voltaic (PV) system that is used to design with MPPT and developed Grey Wolf optimization (GWO) algorithm for analyzing the power quality issues in the grid system. The proposed Grey Wolf optimization (GWO) algorithm is incorporated in the prototype model and compared with other related optimization algorithms namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The various loading conditions as well as solar irradiances are modeled by using MATLAB simulation and experimentally validated by a DSPIC (DS 1104) based prototype model. A three phase PV grid connected non-linear …load is observed in different operating environmental conditions. The optimization control algorithms was developed and implemented in Super-Lift Inverter (SLI) grid connected system. The findings of this work are, grid reactive power demand is compensated using DSTATCOM, and also from the real power of renewable energy system. But, majority of the active power is provided or absorbed by DSTATCOM component. The objective of this proposed work is that the three optimization control algorithms are examined, and the PV integrated grid tied system maintains a compensation power at Unity Power Factor (UPF). The proposed optimization methods produce load output power factor values such as 0.89 (GWO), 0.88, (PSO) and 0.86 (GA). Show more

Keywords: PV system, particle swarm optimization, genetic algorithm, Grey Wolf optimization, Grid

DOI: 10.3233/JIFS-213259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5877-5896, 2022

Authors: Jiang, Yadan | Qiu, Dong

Article Type: Research Article

Abstract: The difference operation for fuzzy number is an essential concept for the fuzzy set theory. There are several differences proposed: generalized difference, generalized Hukuhara difference and granule difference. Based on these differences, generalized differentiability, generalized Hukuhara differentiability and granule differentiability are also proposed, respectively. In this paper, the relations among these three kinds of differences and that of related three kinds of differentiability are clarified.

Keywords: Generalized differences, generalized differentiability, granule differentiability, fuzzy-number-valued function

DOI: 10.3233/JIFS-213270

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5897-5911, 2022

Authors: Tran, Van Quan | Nguyen, Linh Quy

Article Type: Research Article

Abstract: The use of recycled glass in the concrete mix instead of natural coarse aggregates and supplemental cementitious material has several advantages, including the conservation of natural resources, the reduction of CO2 emissions, and cost savings. However, due to their qualities, the mechanical properties of concrete containing Ground Glass Particles (GGP) differ from those of natural aggregates concrete. As a result, assessing the compressive strength (CS) of concrete with GGP is crucial. Therefore, this paper proposes the hybrid Machine Learning (ML) model including the Gradient Boosting (GB) and Bayesian optimization (BO) algorithms for predicting the compressive strength of concrete containing …GGP. The hybrid ML model is developed and validated based on the training dataset (70% of the data) and the test dataset (30% of the remaining data), respectively. The performance of hybrid ML model is evaluated by three criteria, such as the Pearson correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The K-Fold Cross-Validation technique is also used to verify the reliability of the hybrid ML model). The best performance of the hybrid ML model is determined with the R  = 0.9843, RMSE = 1.7256 (MPa), and MAE = 1.3154 (MPa) for training dataset and R  = 0.9784, RMSE = 2.4338 (MPa) and MAE = 1.9618 (MPa) for testing dataset. Based on the best hybrid ML model, the sensitivity analysis including SHapley Additive exPlanation (SHAP) and Partial Dependence Plots (PDP) 2D are investigated to obtain an in-depth examination of each individual input variable on the predicted compressive strength of concrete contaning GGP. The sensitivity analysis shows that four factors, such as curing age, surface area, TiO2 , and temperature have the most effect on the compressive strength of concrete containing GGP. Show more

Keywords: Gradient boosting, bayesian optimization, compressive strength, concrete, machine learning, ground glass particles

DOI: 10.3233/JIFS-213298

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5913-5927, 2022

Authors: Zhang, Lijun | Duan, Lixiang

Article Type: Research Article

Abstract: To address data distribution discrepancy across scenarios, deep transfer learning is used to help the target scenario complete the recognition task using similar scenario data. However, fault misrecognition or low diagnostic accuracy occurs due to the weak expression of the deep transfer model in cross-scenario application. The Convolutional Block Attention Module (CBAM) can independently learn the importance of each channel and space features, recalibrate the channel and space features, and improve image classification performance. This study introduces the CBAM module using the Residual Network (ResNet), and proposes a transfer learning model that combines the CBAM module with an improved ResNet, …denoted as TL_CBAM_ResNet17. A miniature ResNet17 deep model is constructed based on the ResNet50 model. The location of the CBAM module embedded in the ResNet17 model is determined to strengthen model expression. For effective cross-scenario transfer and reduced data distribution discrepancy between source and target domains, a multi-kernel Maximum Mean Discrepancy (MK–MMD) layer is added in front of the classifier layer in the ResNet17 model to select data with common domain features. Considering a reciprocating compressor as the research object, cross-scenario datasets are produced by the vibration signals from the simulation test bench and simulation signals from the dynamic simulation model. Mutual transfer experiments are conducted using these datasets. The proposed method (TL_CBAM_ResNet17) demonstrates better classification performance than TCA, JDA, the TL_ResNet50 model, the TL_ResNet17 model, and the TL_ResNet17 model integrated with other attention mechanism module, and greatly improves the accuracy of fault diagnosis and generalization of the model in cross-scenario applications. Show more

Keywords: Cross-scenario, transfer learning, reciprocating compressor, ResNet, CBAM, dynamic simulation

DOI: 10.3233/JIFS-213340

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5929-5943, 2022

Authors: Manoharan, G. | Sivakumar, K.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-213374

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5945-5951, 2022

Authors: Liu, Sijia | Guo, Zixue

Article Type: Research Article

Abstract: In order to solve the problem of multi-attribute decision-making with unknown weights under probabilistic hesitant fuzzy information, considering the shortcomings of the existing probabilistic hesitant fuzzy distance measure, such as weak distinguishing ability, a probabilistic hesitant fuzzy multi-attribute decision-making method based on improved distance measures is proposed. Firstly, the hesitancy degree of probabilistic hesitant fuzzy element and the improved difference measure of probabilistic hesitant fuzzy element are defined, and an improved probabilistic hesitant fuzzy distance measure based on hesitancy degree, incompleteness degree and improved difference measure is proposed. Secondly, based on the improved distance measure, a mathematical programming model with …the goal of minimizing the relative approach degree is con-structed to determine the attribute weights of evaluation indexes in multi-attribute decision making problems. Using it as a base, a new probabilistic hesitant fuzzy multi-attribute decision-making method is proposed by combining the improved probabilistic hesitant fuzzy distance measure with the compromise ratio method. Finally, the proposed method is applied to the problem of green supplier selection, and the feasibility and effectiveness of the proposed method are verified by case analysis and comparison with other methods. Show more

Keywords: Probabilistic hesitant fuzzy set, multi-attribute decision-making, distance measure

DOI: 10.3233/JIFS-213427

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5953-5964, 2022