Journal of Intelligent & Fuzzy Systems - Volume 40, issue 6

Authors: Shabir, Muhammad | Mubarak, Asad | Naz, Munazza

Article Type: Research Article

Abstract: The rough set theory is an effective method for analyzing data vagueness, while bipolar soft sets can handle data ambiguity and bipolarity in many cases. In this article, we apply Pawlak’s concept of rough sets to the bipolar soft sets and introduce the rough bipolar soft sets by defining a rough approximation of a bipolar soft set in a generalized soft approximation space. We study their structural properties and discuss how the soft binary relation affects the rough approximations of a bipolar soft set. Two sorts of bipolar soft topologies induced by soft binary relation are examined. We additionally discuss …some similarity relations between the bipolar soft sets, depending on their roughness. Such bipolar soft sets are very useful in the problems related to decision-making such as supplier selection problem, purchase problem, portfolio selection, site selection problem etc. A methodology has been introduced for this purpose and two algorithms are presented based upon the ongoing notions of foresets and aftersets respectively. These algorithms determine the best/worst choices by considering rough approximations over two universes i.e. the universe of objects and universe of parameters under a single framework of rough bipolar soft sets. Show more

Keywords: Rough sets, bipolar soft sets, rough bipolar soft sets, bipolar soft topology, decision making

DOI: 10.3233/JIFS-202958

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11845-11860, 2021

Authors: Zhu, Jia-Nian | Liu, Xu-Chong | Liu, Chong

Article Type: Research Article

Abstract: Non-equidistant non-homogenous grey model (abbreviated as NENGM (1,1, k ) model) is a grey prediction model suitable for predicting time series with non-equal intervals. It is widely used in various fields of society due to its high prediction accuracy and strong adaptability. In order to further improve the prediction accuracy of the NENGM (1,1, k ) model, the NENGM (1,1, k ) model is optimized in terms of the cumulative order and background value of the NENGM (1,1, k ) model, and a NENGM (1,1, k ) model based on double optimization is established (abbreviated as FBNENGM (1,1, k ) …model), and the whale optimization algorithm is used to solve the best parameters of the model. In order to verify the feasibility and validity of the FBNENGM (1,1, k ) model, the FBNENGM (1,1, k ) model and other four prediction models are applied to three cases respectively, and three indexes commonly used to evaluate the performance of prediction models are used to distinguish. The results show that the prediction accuracy of the FBNENGM (1,1, k ) model based on double optimization is better than other prediction models. Show more

Keywords: Grey system theory, fractional-order accumulation, grey prediction model, FBNENGM (1, 1, k) model, ·whale optimization algorithm

DOI: 10.3233/JIFS-210023

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11861-11874, 2021

Authors: Li, Haiyan | Yang, Xiangfeng

Article Type: Research Article

Abstract: Uncertain time series is chronological sequence overtime where each period is described by an uncertain variable. In this paper, we investigate the smoothly clipped absolute deviation (SCAD) penalized estimation method to determine the unknown parameters in the uncertain autoregressive model, and the autoregressive model order can be simultaneously obtained for a pre-given thresholding parameter λ . Besides, an iterative algorithm based on local quadratic approximations for finding the penalized estimators is provided. Based on the fitted autoregressive model, the forecast value and the future value’s confidence interval are given. Besides, the sum of the squared error approach to select the …optimal λ is discussed. Finally, some examples are used to validate the effectiveness of the proposed method by the comparative analysis. Show more

Keywords: Uncertain variable, uncertain autoregressive model, SCAD penalty

DOI: 10.3233/JIFS-210031

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11875-11885, 2021

Authors: Bashir, Humera | Zahid, Zohaib | Kashif, Agha | Zafar, Sohail | Liu, Jia-Bao

Article Type: Research Article

Abstract: The 2-metric resolvability is an extension of metric resolvability in graphs having several applications in intelligent systems for example network optimization, robot navigation and sensor networking. Rotationally symmetric graphs are important in intelligent networks due to uniform rate of data transformation to all nodes. In this article, 2-metric dimension of rotationally symmetric plane graphs R n , S n and T n is computed and found to be independent of the number of vertices.

Keywords: 2-metric dimension, rotationally symmetric, plane graphs

DOI: 10.3233/JIFS-210040

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11887-11895, 2021

Authors: Chen, Lei | Han, Jun | Tian, Feng

Article Type: Research Article

Abstract: Infrared (IR) images can distinguish targets from their backgrounds based on difference in thermal radiation, whereas visible images can provide texture details with high spatial resolution. The fusion of the IR and visible images has many advantages and can be applied to applications such as target detection and recognition. This paper proposes a two-layer generative adversarial network (GAN) to fuse these two types of images. In the first layer, the network generate fused images using two GANs: one uses the IR image as input and the visible image as ground truth, and the other with the visible as input and …the IR as ground truth. In the second layer, the network transfer one of the two fused images generated in the first layer as input and the other as ground truth to GAN to generate the final fused image. We adopt TNO and INO data sets to verify our method, and by comparing with eight objective evaluation parameters obtained by other ten methods. It is demonstrated that our method is able to achieve better performance than state-of-arts on preserving both texture details and thermal information. Show more

Keywords: IR and visible images, image fusion, generative adversarial network, deep learning

DOI: 10.3233/JIFS-210041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11897-11913, 2021

Authors: Zhang, Shao-Yu

Article Type: Research Article

Abstract: This paper introduces a special Galois connection combined with the wedge-below relation. Furthermore, by using this tool, it is shown that the category of M -fuzzifying betweenness spaces and the category of M -fuzzifying convex spaces are isomorphic and the category of arity-2 M -fuzzifying convex spaces can be embedded in the category of M -fuzzifying interval spaces as a reflective subcategory.

Keywords: Fuzzy convex structure, fuzzy betweenness space, fuzzy interval space, arity-2 fuzzy convexity

DOI: 10.3233/JIFS-210060

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11915-11925, 2021

Authors: Oh, Ju-Mok | Kim, Yong Chan

Article Type: Research Article

Abstract: In this paper, we introduce the notions of join preserving maps using distance spaces instead of fuzzy partially ordered sets on complete co-residuated lattices. We investigate the properties of Alexandrov fuzzy topologies, distance functions, join preserving maps and upper approximation operators. Furthermore, we study their relations and examples. We prove that there exist isomorphic categories and Galois correspondences between their categories.

Keywords: Complete co-residuated lattices, distance functions, join preserving maps, upper approximation operators, Alexandrov fuzzy topologies

DOI: 10.3233/JIFS-210061

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11927-11939, 2021

Authors: Chen, Xiaojun | Jia, Shengbin | Ding, Ling | Xiang, Yang

Article Type: Research Article

Abstract: Knowledge graph reasoning or completion aims at inferring missing facts by reasoning about the information already present in the knowledge graph. In this work, we explore the problem of temporal knowledge graph reasoning that performs inference on the graph over time. Most existing reasoning models ignore the time information when learning entities and relations representations. For example, the fact (Scarlett Johansson , spouse Of , Ryan Reynolds ) was true only during 2008 - 2011. To facilitate temporal reasoning, we present TA-TransRILP , which involves temporal information by utilizing RNNs and takes advantage of Integer Linear Programming. Specifically, we …utilize a character-level long short-term memory network to encode relations with sequences of temporal tokens, and combine it with common reasoning model. To achieve more accurate reasoning, we further deploy temporal consistency constraints to basic model, which can help in assessing the validity of a fact better. We conduct entity prediction and relation prediction on YAGO11k and Wikidata12k datasets. Experimental results demonstrate that TA-TransRILP can make more accurate predictions by taking time information and temporal consistency constraints into account, and outperforms existing methods with a significant improvement about 6-8% on Hits@10. Show more

Keywords: Knowledge graph reasoning, temporal information, temporal consistency constraints, integer linear programming

DOI: 10.3233/JIFS-210064

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11941-11950, 2021

Authors: Yu, Junqi | Zhang, Tianlun | Zhao, Anjun | Xie, Yunfei

Article Type: Research Article

Abstract: Energy consumption prediction can provide reliable data support for energy scheduling and optimization of office buildings. It is difficult for traditional prediction model to achieve stable accuracy and robustness when energy consumption mode is complex and data sources are diverse. Based on such situation, this paper raised an approach containing the method of comprehensive similar day and ensemble learning. Firstly, the historical data was analyzed and calculated to obtain the similarity degree of meteorological features, time factor and precursor. Next, the entropy weight method was used to calculate comprehensive similar day and applied to the model training. Then the improved …sine cosine optimization algorithm (SCA) was applied to the optimization and parameter selection of a single model. Finally, an approach of model selection and integration based on dominance was proposed, which was compared with Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), with a large office building in Xi ‘an taken as an example to analysis showing that compared with the prediction accuracy, root mean square percentage error (RMSPE) in the ensemble learning model after using comprehensive similar day was reduced by about 0.15 compared with the BP model, and was reduced by about 0.05, 0.06 compared with the SVR and LSTM model. Respectively, the mean absolute percentage error (MAPE) was reduced by 12.02%, 6.51% and 5.28%. Compared with several other integration methods, integration model based on dominance reduced absolute error at all times. Accordingly, the proposed approach can effectively solve problems of low accuracy and poor robustness in traditional model and predict the building energy consumption efficaciously. Show more

Keywords: Similar day, ensemble learning, sine cosine optimization algorithm, energy consumption prediction

DOI: 10.3233/JIFS-210069

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11951-11965, 2021

Authors: Wu, Meiqin | Li, Zhuoyu | Fan, Jianping

Article Type: Research Article

Abstract: With resource shortage and environmental pollution becoming more and more serious, the development of new energy vehicles (NEVs) plays an important role. In this paper, the hybrid method of best-worst method (BWM), Multi-Objective Optimization by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), and Evaluation based on Distance from Average Solution (EDAS) is used to evaluate new energy vehicles (NEVs) and select the best new energy vehicle. BWM method is used to obtain the subjective preference weight, MULTIMOORA method is used to integrate the objective data with the subjective weight to evaluate new energy vehicles, and the final ranking of alternatives …is obtained by the EDAS method. The paper collect the data of 22 representative new energy vehicle types in China, the validity and feasibility of the method is verified. Show more

Keywords: Best-worst method, multi-objective optimization by ratio analysis plus full multiplicative form, evaluation based on distance from average solution, new energy vehicles

DOI: 10.3233/JIFS-210074

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11967-11980, 2021