Journal of Intelligent & Fuzzy Systems - Volume 32, issue 3

Authors: Guan, Xianjun | Qian, Li | Li, Mengxin | Chen, Huayou | Zhou, Ligang

Article Type: Research Article

Abstract: The aim of this paper is to propose an earthquake relief emergency logistics capacity (ERELC) evaluation model on the basis of cloud model and fuzzy multi-attribute decision making (MADM) method with cloud generalized information aggregation operators, in which two phase procedures are proposed. The first stage utilizes cloud analytic hierarchy process (CAHP) based on the cloud model to obtain the weights of influencing factors; in the second stage, fuzzy comprehensive evaluation (FCE) is applied to determine the overall capacity belongs to. Several new aggregation operators are proposed as well, including the cloud generalized weighted averaging (CGWA) operator and cloud generalized …ordered weighted averaging (CGOWA) operator. The main contribution of this paper possesses two points: (1) Using the cloud model can synthetically describe the randomness and fuzziness of qualitative concepts. (2) Establishing a hierarchical model in evaluation of ERELC realizes the transformation from qualitative analysis to quantitative analysis. In addition, a case study is presented to make this application more understandable. We also make comparisons between the fuzzy comprehensive evaluation methods proposed in this paper and some existing ones to confirm its feasibility and rationality. Show more

Keywords: Fuzzy comprehensive evaluation, earthquake emergence, logistics capacity, cloud model, multi-attribute decision making

DOI: 10.3233/JIFS-16252

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2281-2294, 2017

Authors: Wang, Meng-Xian | Wang, Jian-Qiang

Article Type: Research Article

Abstract: Anomaly detection is an important task for applications involving Big Data. Comparing with traditional method, anomaly detection in Big Data confronts growing amounts of data with high dimensionality and complex structures, which require more real-time analysis. This paper presents a fuzzy input-output system for anomalous data using electronic consumer records (ECR), a trapezium-cloud-map-filtration (TCMF) framework and a value mining model. ECRs are used to add or remove criteria based on consumers’ consumption. In addition, MapReduce framework and trapezium clouds generated from each subsample are aggregated by using the aggregated trapezium cloud as a filter for each subsample. Then, a fuzzy …logic-based value mining model is proposed based on Takagi-Sugeno model (T-S model) and trapezium clouds. This paper establishes a system that can improve decision-making accuracy by filtering large-scale data, and an illustrative example using a hotel booking situation is presented to verify the validity and feasibility of the proposed model. Finally, a comparative analysis is conducted between the proposed approach and existing methods. Show more

Keywords: T-S model, Big Data, trapezoidal cloud, cloud droplet, consumers’ classification

DOI: 10.3233/JIFS-16254

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2295-2308, 2017

Authors: Khalil, Ahmed Mostafa | Hassan, Nasruddin

Article Type: Research Article

Abstract: The aim of this paper is to correct the assertions (3) and (4) of Theorem 3.23 proposed by Zhang and Shu [Journal of Intelligent and Fuzzy Systems 27 (2014) 2115-2125]. A counterexample illustrates the flaw of the assertions. We introduce the notion of generalized possibility multi-fuzzy soft sets and use it to correct the flaw in those assertions. Finally we introduce two new definitions and five theorems to improve the work of Zhang and Shu, along with two examples.

Keywords: Generalized distributive law, multi-fuzzy set, multi-fuzzy soft set, possibility multi-fuzzy soft set

DOI: 10.3233/JIFS-16264

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2309-2314, 2017

Authors: Yan, Dandan | Yang, Youlong | Li, Benchong

Article Type: Research Article

Abstract: Selecting model between recognition rate of “large” class and recognition rate of “small” class in imbalanced data is often a serious trade-off. Most approaches emphasize the accuracy of “large” class. The drawback is that potentially informative “small” class may be overlooked and even make an overfitting model. In this paper, we propose an alternative approach based on fuzzy system for classification problems with imbalanced data, called receive feedback model (RFM). It works by starting with a maximal attribution ratio probability that includes all observations for each class, and then gradually reclassify “unlabeled” samples if they succeed in minimal risk evaluation …of a certain class. To exploit the RFM of classification problems, we further introduce probably approximately correct of the model and the convergence of our procedure. Extensive experiments using public data sets and the results of statistical tests have shown that the proposed RFM significantly outperforms other approaches in term of the appropriate trade-off both recognition rates of “large” class and “small” class. Show more

Keywords: Imbalanced data, classification, fuzzy number, probably approximately correct, fuzzy rule

DOI: 10.3233/JIFS-16270

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2315-2325, 2017

Authors: Paripour, Mahmoud | Komak Yari, Mohammad

Article Type: Research Article

Abstract: The fractional quadratic integral equations have wide applications in various fields of science and engineering. We present an existence theorem for at least one continuous solution for fuzzy fractional quadratic integral equations (FFQIE). In this paper, we introduce fuzzy quadratic integral equation of fractional order and existence and uniqueness of the solutions for this class of fractional equations.

Keywords: Fuzzy number, fuzzy integral equations, quadratic fuzzy integral equations, Banach fixed point theorem

DOI: 10.3233/JIFS-16316

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2327-2338, 2017

Authors: Zhao, Xiaopeng | Yang, Guotian

Article Type: Research Article

Abstract: Based on the minimum entropy and fuzzy subtractive clustering method, a new specialized algorithm for online multi-model identification is proposed in this paper. Different from the traditional identification model, the structure and parameters of the established model can be recursively updated when new data coming to the system, which makes it a wise choice for online modeling and complex processes control. The entropy-based online fuzzy subtractive clustering method is used to determine the number of the local models and their corresponding memberships. A controlled auto-regressive integrated moving average expression is adopted as the form of linear subsystems, for it not …only match the identification process, but also can be used to design the control system easily. The parameters of local models are calculated by weighted recursive least square method, and the nondimensional error index is used to evaluate the performance of the identified model. By applying generalized predictive control strategy to the established model, a fuzzy generalized predictive control system is constructed, and the control law is given in the paper. Finally, a case of the method to “Mackey-Glass difference time delay equation” is studied. The simulation results illustrate the viability and the robustness of the strategy. Show more

Keywords: Multi-model, online identification, entropy, subtractive clustering, predictive control

DOI: 10.3233/JIFS-16317

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2339-2349, 2017

Authors: Zhao, Jing | Wong, Pak Kin | Xie, Zhengchao | Ma, Xinbo

Article Type: Research Article

Abstract: Study on rotary valve system (RVS) for automotive engines has been carried out over many years. Recent researches have already successfully implemented the idea, but none of design can provide variable valve timing and flow area control simultaneously. Therefore, a RVS with variable valve control system is proposed in this paper. The system design and dynamic analysis of the proposed RVS are first presented. Moreover, the selection of the proportional, integral and derivative (PID) controller parameters for the valve control based on an emerging artificial intelligent technique, cuckoo search (CS), is also discussed. Experimental and simulation results show that the …proposed tuning method is better than the traditional Ziegler and Nichols method, and the proposed variable rotary valve control system is feasible. Show more

Keywords: Intelligent control, flow area control, cuckoo search, rotary valve system, variable valve timing

DOI: 10.3233/JIFS-16327

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2351-2363, 2017

Authors: Zhang, Yong | Liu, Bo | Yu, Jiaxin

Article Type: Research Article

Abstract: This paper proposes an evolutionary-based selective ensemble learning framework for solving classification problem. In the proposed ensemble learning framework, extreme learning machine (ELM) is selected as base learner and evolutionary algorithms are employed to optimize the weights of base learners in the ensemble. Then, some base learners, that their weights are larger than the threshold, are selected for making decision. The proposed ensemble learning framework is evaluated on 20 benchmark data sets from KEEL repository through four different evolutionary algorithms. Results show that the proposed evolutionary-based ensemble learning framework outperforms the simple voting based ensemble method in terms of classification …performance. In four evolutionary optimization algorithms, PSOGA-based and DE-based weight optimization algorithms can effectively improve the classification accuracy and generalization ability. Show more

Keywords: Extreme learning machine, evolutionary algorithm, ensemble learning, classification

DOI: 10.3233/JIFS-16332

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2365-2373, 2017

Authors: Liu, Peide | Teng, Fei

Article Type: Research Article

Abstract: Similar to the extension from intuitionistic fuzzy numbers (IFNs) to neutrosophic numbers (NNs), we extend the normal intuitionistic fuzzy numbers (NIFNs) to normal neutrosophic numbers (NNNs) to handle the incompleteness, indeterminacy and inconsistency of the evaluation information. In addition, because Heronian mean (HM) operators can capture the correlations of the aggregated arguments, we further extend the HM operator to deal with the NNNs, and propose some new HM operators and apply them to solve the multiple attribute group decision making (MAGDM) problems. Firstly, we briefly introduce the definition, the operational laws, the properties, the score function, and the accuracy function …of the NNNs. Secondly, some new HM operators are introduced, such as generalized Heronian mean (GHM) operator, generalized weighted Heronian mean (GWHM) operator, improved generalized weighted Heronian mean (IGWHM) operator, generalized geometric Heronian mean (GGHM) operator, improved generalized geometric Heronian mean (IGGHM) operator, and improved generalized geometric weighted Heronian mean (IGGWHM) operator. Moreover, we propose the normal neutrosophic number improved generalized weighted Heronian mean (NNNIGWHM) operator and normal neutrosophic number improved generalized geometric weighted Heronian mean (NNNIGGWHM) operator, and discuss their properties and some special cases. Furthermore, we propose two MAGDM methods respectively based on the NNNIGWHM and NNNIGGWHM operators. Finally, we give an illustrative example to demonstrate the practicality and effectiveness of the two methods. Show more

Keywords: Multiple attribute group decision making, heronian mean, normal fuzzy number, normal neutrosophic numbers, normal neutrosophic number Heronian mean operators

DOI: 10.3233/JIFS-16345

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2375-2391, 2017

Authors: Javadian, Mohammad | Shouraki, Saeed Bagheri

Article Type: Research Article

Abstract: In this paper the Unsupervised Active Learning Method (UALM), a novel clustering method based on the Active Learning Method (ALM) is introduced. ALM is an adaptive recursive fuzzy learning algorithm inspired by some behavioral features of human brain functionality. UALM is a density-based clustering algorithm that relies on discovering densely connected components of data, where it can find clusters of arbitrary shapes. This approach is a noise-robust clustering method. The algorithm first blurs the data points as ink drop patterns, then summarizes the effects of all data points, and finally puts a threshold on the resulting pattern. It uses the …connected-component algorithm for finding clusters. Then determines cluster centers by intersecting the narrow-paths. Experimental results confirmed the superiority of our proposed method compared to the two most well-known density-based clustering algorithms, DBSCAN and DENCLUE. Show more

Keywords: Active Learning Method, clustering, density-based clustering, Unsupervised Active Learning Method, fuzzy data

DOI: 10.3233/JIFS-16360

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2393-2411, 2017