Journal of Intelligent & Fuzzy Systems - Volume 36, issue 2

Authors: Abbasizadeh, N. | Davvaz, B.

Article Type: Research Article

Abstract: This paper provides a new connection between algebraic hyperstructures and fuzzy sets. We present the concept of I -fuzzy topological polygroups and prove some properties. The concept of I -fuzzy topological polygroups is a generalization of the concept of I -fuzzy topological groups. By considering the relative fuzzy topology on fuzzy subpolygroups we prove some related properties.

Keywords: Polygroup, I-fuzzy topology, I-fuzzy quasi-coincident neighborhood system, I-fuzzy topological polygroups

DOI: 10.3233/JIFS-18833

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1671-1682, 2019

Authors: Türkmen, Muhammed Recai | Dündar, Erdinç

Article Type: Research Article

Abstract: In this study, we have investigated the concepts of lacunary summable, lacunary statistical convergence and lacunary statistically Cauchy sequence for double sequences in fuzzy normed spaces. Also, we have investigated some properties and relationships between these concepts.

Keywords: Double sequences, statistical convergence, fuzzy numbers, fuzzy normed spaces, lacunary sequences

DOI: 10.3233/JIFS-18841

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1683-1690, 2019

Authors: Duan, Huiming | Liu, Yongzhi | Wang, Di | He, Leiyuhang | Xiao, Xinping

Article Type: Research Article

Abstract: The modeling and prediction of short-term traffic flow can reflect the prediction results of the traffic state and traffic flow data. In this paper, first, we use a high-dimensional tensor to represent the multi-mode characteristics of traffic flow data, and we make use of the basic operations properties of tensors, such as Tucker decomposition, to study the methods for filling in data, such as ITRM. Additionally, we preprocess the lost traffic flow and abnormal data. At the same time, we study the short-term traffic flow based on the “week-day-time” multi-mode of the traffic flow data. Using the grey model (GM …(1, 1)) to predict the same period of the weekly mode, the scrolling grey model (SGM) of the same time period is predicted. For the time mode, a neural network time series of wavelet analysis is used to predict the traffic flow forecast during the same period. Then, the prediction results of the three different models are weighted by the grey correlation analysis method, and then, the coupling prediction model of the three models is obtained. In the end, according to the traffic flow data of the main road of Shaoshan road in Changsha, Hunan, China, we first preprocess the lost data by using the filling algorithm for the tensor data, and then, we make the traffic flow data complete, use the three tensor data modes of traffic flow, and analyze the results. The experimental results show that the coupling prediction model with the tensor model is much better than the single GM (1, 1) model, the SGM and the neural network prediction model. Show more

Keywords: High-dimensional tensor, multi-mode traffic flow data, short-term traffic flow forecasting, grey model

DOI: 10.3233/JIFS-18804

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1691-1703, 2019

Authors: Han, Yuan-Liang | Shi, Fu-Gui

Article Type: Research Article

Abstract: In this paper, the degree of which an L -subset is an L -convex fuzzy ideal is proposed and its some characterizations are given. Besides, an L -fuzzy convexity on a lattice is naturally proposed and its some properties are studied.

Keywords: L-convex fuzzy ideal, Implication operator, L-fuzzy ideal degree, L-fuzzy convexity

DOI: 10.3233/JIFS-18764

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1705-1714, 2019

Authors: Yu, Wangwang | Zhang, Hui | Li, Boquan

Article Type: Research Article

Abstract: In this paper, we present a priority degree method of hesitant fuzzy linguistic term sets (HFLTSs) based on a type of G function. First, we obtain several invalid examples by using Rodríguez et al. and Lee et al.’s method of HFLTSs. Considering these problems, second, after transforming HFLTS into the interval number and proposing a priority degree method of the interval number using the G function, we present a priority degree method of HFLTEs. Finally, we compare both Rodríguez et al.’s decision-making method and lee et al.’s decision-making method with our priority degree method. The result shows that …our priority degree method is more rational. Show more

Keywords: Hesitant fuzzy linguistic term set, Hesitant fuzzy set, Interval number, G function, Priority degree

DOI: 10.3233/JIFS-18773

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1715-1725, 2019

Authors: Thao, Nguyen-Trang

Article Type: Research Article

Abstract: Fuzzy time series modeling has recently become an interesting topic to study. Among fuzzy time series models, the Abbasov-Mamedova (AM) model has advantages over the others because it can forecast the value that is outside the min-max range of the original data. However, the performance of the AM model strongly depends on three parameters that are user-defined. In previous studies, the optimal parameters of the fuzzy time series models have been identified with a global optimization method. Surprisingly, optimizing the parameters of the Abbasov and Mamedova model has not been solved in spite of its advantages over the others. This …paper presents a new approach to improve the performance of AM model based on the evolutionary algorithm. Particularly, the objective function is calculated as the Mean absolute percentage error which will be minimized using the differential evolution (DE) algorithm. The experiments on Azerbaijan’s population, Vietnam’s GDP and rice production demonstrate the feasibility and applicability of the proposed methods. Show more

Keywords: fuzzy time series, differential evolution, optimization, forecast, MAPE

DOI: 10.3233/JIFS-18636

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1727-1741, 2019

Authors: Ma, Hongzhan | Chu, Xuening | Li, Yupeng

Article Type: Research Article

Abstract: Product redesign strategy can effectively shorten design lead time and reduce production cost of new variants development. Identification of function components is the basis of product redesign. In the existing methods to identify the function components, customer requirements are primarily considered while the failure knowledge, a critical information to improve product reliability, is often ignored. The objective of this research is to identify the to-be-improved components considering both customer requirements and product reliability. First, a two-stage fuzzy quality function deployment (QFD) is used to calculate the importance weight of each component considering customer requirements. Second, the fuzzy failure mode effects …and analysis (FMEA) is adopted to measure the failure risk of each component. Different from traditional FMEA, the failure causality relationships are analyzed in this work to provide a means of making use of failure information more effectively for constructing a directed failure causality relationship diagram. Fuzzy permanent function is developed to quantify the failure risk of each component. Then, a modification necessity index is introduced to model the degree of modification necessity for each component considering customer requirements and failure risk. Finally, the optimal set of function components that need to be modified is identified by 0-1 objective programming and constrained optimization. A case study for identification of the function components for the operation device of a crawler crane is implemented to demonstrate the effectiveness of the developed approach. Show more

Keywords: Identification of function component, fuzzy QFD, FMEA, fuzzy permanent function, 0-1 objective programming

DOI: 10.3233/JIFS-18614

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1743-1757, 2019

Authors: Ren, Liwei | Ban, Xiaojun | Ying, Hao

Article Type: Research Article

Abstract: Controlling a dynamic system to make its output identical to a user-desired reference trajectory given to the system’s input without any delay (i.e., perfect output tracking control) is an important and frequently encountered control requirement in industries (e.g., robotic control). When disturbances exist in a system, perfect output tracking control may be impossible to attain and alternatively asymptotical output tracking is sought. This paper presents an asymptotical output tracking control design method for a general class of discrete-time TS fuzzy systems with quadratic rule consequents, which offer better modeling capabilities than the linear rule consequents. The control problem is dealt …with by utilizing the feedback linearization method. To guarantee asymptotical output tracking performance in the presence of square disturbance signal, an auxiliary PI controller is added to attenuate the disturbance. The feedback linearization method is known in the literature to fail to work for certain systems because it can make the tracking controller’s output unbounded. To address this issue, we put forward a full block S-procedure condition to check whether such failure will occur for any given quadratic TS fuzzy system. Applying feedback linearization to the quadratic TS fuzzy systems is innovative relative to the literature that has exclusively dealt with the TS fuzzy systems with linear rule consequents only. Two numerical examples are provided to illustrate the effectiveness and utility of our theoretical results. Show more

Keywords: Discrete-time quadratic TS fuzzy systems, asymptotical output tracking, disturbance rejection, feedback linearization, full block S-procedure

DOI: 10.3233/JIFS-18454

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1759-1772, 2019

Authors: Zareapoor, Masoumeh | Shamsolmoali, Pourya | Yang, Jie

Article Type: Research Article

Abstract: Imaging techniques are the most rapidly growing area of computer vision, and the resolution has reached a new level. Super-resolution is a technique that enhances the resolution of images from the low-resolution input and help to accurately analyze and derive the data. Recently convolutional neural network are becoming mainstream in computer vision. Most existing CNN models based super-resolution either directly reconstruct the low-resolution input and then improve the resolution at the last layer, or another way is, to firstly enlarge the low-resolution input to high resolution (HR), then reconstruct the HR to obtain the desired output. These models encounter some …major flows; large computational resources and losing information. In this paper, we adopt gradual process for training the CNN, to propose an efficient super-resolution model. The gradual strategy helps network to progressively magnify and reconstruct the LR image in each step, and thereby possibly avoid of losing information (second problem). In addition, we optimize the number of layers, add the residual network and skip connection to the proposed network to ease the difficulty of training (first problem). The proposed model not only achieves a compatible performance with the existing prominent methods but also, efficiently reduce the computational expenses. Show more

Keywords: Super-resolution, deep network, skip connections, image processing

DOI: 10.3233/JIFS-18136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1773-1783, 2019

Authors: Barzegar Kelishami, Hasan | Fariborzi Araghi, Mohammad Ali | Allahviranloo, Tofigh

Article Type: Research Article

Abstract: In this paper, by using the fuzzy CESTAC method and the CADNA library a procedure is proposed to control the step size for solving the fuzzy differential equation with fuzzy boundary conditions based on the finite differences method under generalized H-differentiability (gH-differentiability). An algorithm is presented to implement the discrete stochastic arithmetic for solving the given fuzzy boundary value problem on the C++ code via the CADNA library. Also, a theorem is proved to show the accuracy of results based on the concept of the common proximity of two fuzzy numbers. Finally, some examples are solved by using the proposed …algorithm to illustrate the effectiveness of applying the stochastic arithmetic (SA) in place of the floating-point arithmetic (FPA) to validate the results and find the optimal solution. Show more

Keywords: Finite differences method, Fuzzy boundary value problem (FBVP), Stochastic arithmetic, CESTAC method, CADNA Library

DOI: 10.3233/JIFS-181055

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1785-1796, 2019