Journal of Intelligent & Fuzzy Systems - Volume 30, issue 6

Authors: Li, Chuan | Valente de Oliveira, José | Sanchez, René-Vinicio | Cerrada, Mariela | Zurita, Grover | Cabrera, Diego

Article Type: Research Article

Abstract: Detecting early faults in rolling element bearings is a crucial measure for the health maintenance of rotating machinery. As faulty features of bearings are usually demodulated into a high-frequency band, determining the informative frequency band (IFB) from the vibratory signal is a challenging task for weak fault detection. Existing approaches for IFB determination often divide the frequency spectrum of the signal into even partitions, one of which is regarded as the IFB by an individual selector. This work proposes a fuzzy technique to select the IFB with improvements in two aspects. On the one hand, an IFB-specific fuzzy clustering method …is developed to segment the frequency spectrum into meaningful sub-bands. Considering the shortcomings of the individual selectors, on the other hand, three commonly-used selectors are combined using a fuzzy comprehensive evaluation method to guide the clustering. Among all the meaningful sub-bands, the one with the minimum comprehensive cost is determined as the IFB. The bearing faults, if any, can be detected from the demodulated envelope spectrum of the IFB. The proposed fuzzy technique was evaluated using both simulated and experimental data, and then compared with the state-of-the-art peer method. The results indicate that the proposed fuzzy technique is capable of generating a better IFB, and is suitable for detecting bearing faults. Show more

Keywords: Rolling element bearing, fuzzy clustering, fuzzy comprehensive evaluation, fault detection, envelope demodulation

DOI: 10.3233/IFS-162097

Citation: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 6, pp. 3513-3525, 2016

Authors: Bhuiya, Sushil Kumar | Chakraborty, Debjani

Article Type: Research Article

Abstract: In this paper, we consider an economic production quantity (EPQ) model for imperfect production process under fuzzy random variable demand considering inspection errors. Due to the first stage inspection errors, some proportion of defective items are returned because of dissatisfaction of the customers. In the previous traditional models, the defective rates and the inspection errors follow some probability distributions. However, in real life situation, it is almost impossible to obtain the statistical information precisely. Thus, this study proposes the fuzzy defective rates and the fuzzy inspection errors. In addition, this model interpolates two more stages of inspections, one is after …the production run time, and another is after the beginning of the rework process. The purpose of this study is to establish a fuzzy random EPQ model with the fuzzy defective rates and inspection errors. The expected profit per unit time is calculated by using fuzzy random renewal reward theorem. This model maximizes the expected profit per unit time in fuzzy sense. We develop a methodology for finding the global optimal solutions. A numerical example is also provided to illustrate our proposed model. Furthermore, sensitivity analysis is also carried out in order to present some managerial inferences. Show more

Keywords: Inventory, imperfect item, inspection error, fuzzy type-I and type-II error, fuzzy random variable

DOI: 10.3233/IFS-162098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 6, pp. 3527-3541, 2016

Authors: Kalita, Mrinal C. | Saikia, Helen K.

Article Type: Research Article

Abstract: Notion of singular fuzzy ideals of commutative rings is introduced in this paper. These singular fuzzy ideals are defined via essential fuzzy ideals. Various characteristic features of such ideals to establish relationship between fuzzy non singularity and fuzzy semiprime character of commutative rings are presented.

Keywords: Fuzzy ideal, essential fuzzy ideal, singular ideal, semiprime

DOI: 10.3233/IFS-162099

Citation: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 6, pp. 3543-3549, 2016

Authors: Xu, Xuanhua | Wang, Bing | Zhou, Yanju

Article Type: Research Article

Abstract: A new method of solving large group decision-making problems with incomplete preference information is proposed in this paper. First, we introduce the trust viewpoint of access control, which belongs to the information technology field. Second, we establish subjective trust degree (i.e., direct trust degree) and objective trust degree (i.e., indirect trust degree) to better evaluate the level of trust and propose a compensation method based on a trust model to transfer the incomplete preference matrix into a complete preference matrix. Third, by adopting the method based on clustering to solve decision makers’ weights, a ranking of alternatives is obtained by …synthesizing the weights and the complete preference matrix. To obtain a more reasonable clustering result, we redefine the distance similarity formula based on analysis of the previous problems, then combine it with cosine similarity to propose a double clustering model. Finally, a numerical example is used to demonstrate the effectiveness of the proposed method in this paper and comparison and analysis are conducted. Show more

Keywords: Trust model, incomplete information, large group, group decision-making

DOI: 10.3233/IFS-162100

Citation: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 6, pp. 3551-3565, 2016

Authors: Chen, Zhen-Song | Chin, Kwai-Sang | Ding, Heng | Li, Yan-Lai

Article Type: Research Article

Abstract: This study investigates and improves the operational laws of triangular intuitionistic fuzzy numbers. The triangular intuitionistic fuzzy random variable (TIFRV) is introduced on the basis of the concepts of the triangular intuitionistic fuzzy number and triangular fuzzy random variable. Related properties of a TIFRV are also proposed and verified. To solve the problem of multi-criteria decision making on aspiration levels—a situation in which criterion weights are unknown and criterion values are given in terms of TIFRVs—this study proposes a triangular intuitionistic fuzzy random decision-making method based on a combination of parametric estimation, score functions, and prospect theory. In this method, …the decision maker evaluates alternatives with triangular intuitionistic fuzzy numbers in different periods of decision making and thus enables the estimation of the parameters of the triangular intuitionistic fuzzy population and the creation of an intuitionistic triangular fuzzy random matrix. An expectation–variance intuitionistic fuzzy matrix is constructed on the basis of mean–variance analysis, and a fuzzy random score function is then defined to transform a normalized expectation–variance intuitionistic fuzzy matrix into a score function matrix. Prospect theory is used to calculate the values of prospect score functions, and the information entropy method is used to determine criterion weights. This procedure generates comprehensive prospect score function values that determine the final ranking of alternatives. A practical example is presented to show the feasibility and effectiveness of the proposed approach. Show more

Keywords: Triangular intuitionistic fuzzy random variable, score function, multiple-criteria decision making

DOI: 10.3233/IFS-162101

Citation: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 6, pp. 3567-3581, 2016

Authors: Yoon, Ji Won

Article Type: Research Article

Abstract: The Adaptive Mean Shift (AMS) algorithm is a popular and simple non-parametric clustering approach based on Kernel Density Estimation. In this paper the AMS is reformulated in a Bayesian framework, which permits a natural generalization in several directions and is shown to improve performance. The Bayesian framework considers the AMS to be a method of obtaining a posterior mode. This allows the algorithm to be generalized with three components which are not considered in the conventional approach: node weights, a prior for a particular location, and a posterior distribution for the bandwidth. Practical methods of building the three different components …are considered. Show more

Keywords: Adaptive mean shift algorithm, kernel density estimation

DOI: 10.3233/IFS-162103

Citation: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 6, pp. 3583-3592, 2016

Authors: Su, Kai | Ma, Liangli | Xiao, Bin | Zhang, Huaiqiang

Article Type: Research Article

Abstract: Strongly promoted by the development of Service-Oriented Computing and Cloud Computing technologies, a large number of functionally equivalent web services emerge on the Internet. Quality of Service (QoS) is becoming a key factor to distinguish different web services. Although many Collaborative Filtering (CF) based approaches are recently proposed to predict the QoS of web services, the prediction accuracy are not satisfactory, since they rarely take full use of the neighbor information and latent feature information contained in the historical QoS data. Especially when the real-world QoS data is highly sparse, the previous works fail to detect the actual relationships between …services. In this paper, we present a novel hybrid web service QoS prediction approach that systematically combines the memory-based CF and model-based CF. Firstly a non-negative matrix factorization model for web service QoS prediction is presented. Then an Expectation-Maximization (EM) based approach is designed to learn the model for making further prediction. The service neighbor information, which integrates the direct similarity and transitive indirect similarity of services to handle the data sparsity problem, is introduced into EM based learning process to make the prediction results more accurate. Large-scale real-world experiments are conducted using the WSDREAM QoS dataset. The experimental results demonstrate that our approach can achieve better prediction accuracy than other state-of-the-art approaches. Show more

Keywords: Web service, QoS prediction, collaborative filtering, non-negative matrix factorization, EM, Data sparsity, similarity transition

DOI: 10.3233/IFS-162104

Citation: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 6, pp. 3593-3604, 2016

Authors: Yun, Unil | Kim, Donggyu | Ryang, Heungmo | Lee, Gangin | Lee, Kyung-Min

Article Type: Research Article

Abstract: Utility pattern mining is a technique that finds valuable patterns from large-sized databases with each item’s importance and quantity information associated with it. The representative utility pattern mining technique, high utility pattern mining (HUPM), calculates the utilities of patterns by summating all of the item utilities in the patterns. However, such utility measures for patterns in HUPM have a drawback in whichpatterns with long lengths tend to have utilities sufficient to become high utility patterns. For these reasons, high average utility pattern mining (HAUPM) employing different utility measures has been studied in order to consider such pattern length factors. Recently, …techniques for handling stream data are necessary because many data sources, e.g. sensors and POS devices, produce data in real time. However, all the existing HAUPM algorithms are unable to find up-to-date, meaningful patterns over data streams. We thus propose the first sliding window based HAUPM algorithm discovering recent high average utility patterns over data streams. Based on the sliding window model, our algorithm divides stream data into numerous batches, and keeps only recent batches in its window. Thereby, the algorithm can mine recent, important patterns over data streams. We also introduce a new strategy that enhances the performance of our algorithm by minimizing the overestimated average utilities stored in the proposed data structure. The experimental results show that our algorithm outperforms the competitors. Show more

Keywords: Association rule mining, utility pattern mining, stream pattern mining, sliding window model

DOI: 10.3233/IFS-162106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 6, pp. 3605-3617, 2016

Authors: Akram, Muhammad | Nawaz, Saira

Article Type: Research Article

Abstract: A fuzzy soft set is a mapping from parameter set to the fuzzy subsets of universe. Fuzzy soft set theory provides a parameterized point of view for uncertainty modeling and soft computing model. In this research article, we apply the concept of fuzzy soft sets to graphs. We present the concept of fuzzy soft graphs, various methods of their construction, and investigate some of their related properties. We discuss certain types of irregular fuzzy soft graphs. We also describe applications of fuzzy soft graphs in social network and road network.

Keywords: Fuzzy soft sets, fuzzy soft graphs, operations on fuzzy soft graphs, irregular fuzzy soft graphs, decision making

DOI: 10.3233/IFS-162107

Citation: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 6, pp. 3619-3632, 2016

Authors: Abdi, Hamdi | Beigvand, Soheil Derafshi

Article Type: Research Article

Abstract: This paper presents a novel Long-Term Load Forecasting (LTLF) technique based on the new heuristic method, namely Gravitational Search Algorithm (GSA). The objective of the suggested approach is establishing a more accurate LTLF model to minimize the average error of modeling. In order to estimate different fitting functions based on the proposed algorithm, two different case studies include Egyptian and Kuwaiti grids are selected. Also, the results are compared with a conventional approach, namely Least Squares (LS) method, and Particle Swarm Optimization (PSO) as a heuristic algorithm, to select the best LF model. Finally, based on the average and maximum …errors arise from the estimations as a decision condition; the best function is selected for the LTLF problem. Show more

Keywords: Energy forecasting, demand forecasting, long-term forecasting, electricity, regression, gravitational search algorithm

DOI: 10.3233/IFS-162108

Citation: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 6, pp. 3633-3643, 2016