Journal of Intelligent & Fuzzy Systems - Volume 39, issue 1

Authors: Kadian, Ratika | Kumar, Satish

Article Type: Research Article

Abstract: In this communication, we have characterized the sum of two general measures associated with two distributions with discrete random variables as well as fuzzy sets. One of these measures is logarithmic, while other contains the power of variables, named as joint representation of Renyi’s-Tsallis divergence measure which implies that the proposed measure is equal to the constant time the sum of Renyi’s and Tsallis divergence measure. Besides the validation of the proposed measures, some of its major properties are also discussed for probability distributions and fuzzy sets. The performance of the proposed measure is contrasted with other existing measures in …the literature. Some illustrative examples are solved in the context of pattern recognition and fault detection problem which demonstrate the practicality and adequacy of measure between fuzzy sets. Show more

Keywords: Renyi’s-Tsallis divergence measure, convex function, fuzzy set, fuzzy divergence measure, pattern recognition, fault detection, 94A15, 94A24, 26D15

DOI: 10.3233/JIFS-191689

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 731-752, 2020

Authors: Shahbazi, Zeinab | Byun, Yung-Cheol

Article Type: Research Article

Abstract: Topic modeling for short texts is a challenging and interesting problem in the machine learning and knowledge discovery domains. Nowadays, millions of documents published on the internet from various sources. Internet websites are full of various topics and information, but there is a lot of similarity between topics, contents, and total quality of sources, which causes data repetition and gives the user the same information. Another issue is data sparsity and ambiguity because the length of the short text is limited, which causes unsatisfactory results and give irrelevant results to end-users. All these mentioned issues in short texts made an …interesting topic for researchers to use machine learning and knowledge discovery techniques to discover underlying topics from a massive amount of data. In this paper, we propose a combination of deep reinforcement learning (RL) and semantics-assisted non-negative matrix factorization model to extract meaningful and underlying topics from short document contents. The main objective of this work is to reduce the problem of repetitive information and data sparsity in short texts to help the users to get meaningful and relevant contents. Furthermore, our propose model reviews an issue of the Seq2Seq approach based on the reinforcement learning perspective and provides a combination of reinforcement learning and SeaNMF formulation using the block coordinate descent algorithm. Moreover, we compare different real-world datasets by using numerical calculation and present a couple of state-of-art models to get better performance on short text document topic modeling. Based on experimental results and comparative analysis, our propose model outperforms the state of art techniques in terms of short document topic modeling. Show more

Keywords: Topic modeling, knowledge discovery, short text, non-negative matrix factorization, machine learning

DOI: 10.3233/JIFS-191690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 753-770, 2020

Authors: Aslam, Muhammad Shamrooz

Article Type: Research Article

Abstract: This paper deals with the problem of quantized state feedback H ∞ control for T-S fuzzy systems with appearing the communication delay under stochastic nonlinearity. To accomplish the objective a uniform framework for effective bandwidth utilization is employed to design co-design method. First of all, the co-design method is proposed such that the data can be communicated according to some logic function. Then, we implemented the measurement size-reduction scheme, using the logarithmic quantization. Additionally, we provided the impact of co-design method and quantization, on the original model of networked control systems (NCSs) is redeveloped as a new structure of …hybrid-triggered NCSs with network induced delay. Moreover, Lyapunov-Krasovskii functional is considered to grantee the closed-loop for stochastic stability analysis of the T-S fuzzy system. The solvability of Lyapunov-Krasovskii functional results in the formation of Linear matrix inequalities. The solution of Linear matrix inequalities leads to the controller gains to perform simulations to validate the proposed scheme. Show more

Keywords: Communication-delay, lyapunov-krasovskii functionals, quantizer, co-design method

DOI: 10.3233/JIFS-191708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 771-788, 2020

Authors: Asadzadeh, Mohammad Sina | Rezaei, Gholam Reza | Jamalzadeh, Javad

Article Type: Research Article

Abstract: In the recent years, many authors have used a single method for equipping algebraic structures with uniformities which are induced by families of algebraic objects. This paper is devoted to a description of this well-known method in general, and provides insight into those results which are obtained using the method. In fact, we prove that the uniform topology induced by this method coincides with a partition topology generated by an equivalence relation, and illustrate the logic behind the continuity of algebraic operations in these kinds of uniform topologies. Furthermore, the main topological properties of the partition topology induced by a …congruence relation are presented. As an application, we explain why many results obtained from this method are trivial. These results have been collected from the works of several mathematicians on more than twenty different algebraic systems over the course of two decades. Show more

Keywords: Algebraic structure, Bl-algebra, uniform structure, partition space, partition uniformity

DOI: 10.3233/JIFS-191709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 789-793, 2020

Authors: Antony Rosewelt, L. | Arokia Renjit, J.

Article Type: Research Article

Abstract: This paper proposes a new content recommendation system which combines the newly proposed embedded feature selection method and the new Fuzzy Temporal Logic based Decision Tree incorporated Convolutional Neural Network classifier. The newly proposed embedded feature selection called Fuzzy Decision Tree and Weighted Gini-Index based Feature Selection Algorithm (FDTWGI-FSA) that contains the existing incorporated the Fuzzy Decision Tree (FDT) and the Weighted Gini-index based Feature Selection Algorithm (WGIFSA) for getting optimized feature subset. Moreover, an enhanced CNN and Fuzzy Temporal Decision Tree for performing the deep learning process which is able to identify the exact e-content from the huge volume …of data with the help of the recommended features by the proposed embedded feature selection method. The exact e-content can be identified after performing the five-layer network structure for extracting the relevant features and it also can be classified by applying the Fuzzy Temporal Decision Tree for the e-learners. Finally, the proposed content recommendation system provides exact content to the e-learners according to their level of understanding and it also satisfies them by providing the exact high level contents. The experiments have been conducted for evaluating the proposed content recommendation system and compared with the existing classifier including the standard CNN. Show more

Keywords: Classification, deep learning, feature selection (FS), fuzzy logic, weighted genetic algorithm (WGA)

DOI: 10.3233/JIFS-191721

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 795-808, 2020

Authors: Zhang, Qiang | Hu, Junhua | Feng, Jinfu | Liu, An

Article Type: Research Article

Abstract: As an extension of the intuitionistic fuzzy set, the Pythagorean fuzzy set can depict uncertain information more effectively, so it has been well applied in multiple criteria decision making problems. At present, the multiple criteria decision making methods using the Pythagorean fuzzy set are generally ranked based on the aggregation operator or the distance measure, ignoring the important tool of the similarity measure. Therefore, this paper proposes several new similarity measures of the Pythagorean fuzzy set and applies them to multiple criteria decision making problems. Firstly, several new similarity measures of the Pythagorean fuzzy set are proposed, and their properties …are discussed. Then, based on the weighted similarity measures, the multiple criteria decision making method is proposed. Finally, the accuracy and reliability of the new similarity measures and the proposed multiple criteria decision making method are verified by the simulation cases. Show more

Keywords: Intuitionistic fuzzy set, Pythagorean fuzzy set, similarity measure, multiple criteria decision making

DOI: 10.3233/JIFS-191723

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 809-820, 2020

Authors: Chen, Zi-yu | Peng, Juan-juan | Wang, Xiao-kang | Zhang, Hong-Yu | Wang, Jian-qiang

Article Type: Research Article

Abstract: Solar energy, as a major and least-cost renewable resource, has attracted extensive attention of experts and scholars. However, the establishment of the power station is time-consuming and costly. And once selected, it is difficult to change. So it is crucial to choose the appropriate site of power station. This paper combines data analysis with multi-criteria group decision-making to solve this problem. First of all, K-means clustering method is selected to process the data according to the characteristics of the data. Secondly, the results obtained by K-means method are represented by probabilistic linguistic term sets. Thirdly, Bonferroni Mean operator is used …to adjust the weight of the criterion, which considers the consensus among experts. Fourthly, Technique for Order Preference by Similarity to Ideal Solution method is employed to rank the alternatives and select the best one. Finally, sensitivity analysis, comparison analysis and simulation are carried out to further confirm the robustness and advantage of the model. This model can help decision makers to better understand the basic situation of power station sites, make the right decisions, and improve some candidate sites according to the results. Show more

Keywords: Multi-criteria group decision-making, site selection, probabilistic linguistic term sets, K-means, bonferroni mean operator, sensitivity analysis

DOI: 10.3233/JIFS-191739

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 821-840, 2020

Authors: Liu, Zhe | Jia, Lifen

Article Type: Research Article

Abstract: As a type of differential equations driven by Liu process, uncertain delay differential equations (UDDEs) model dynamic systems with after-effects or memories in uncertain environment by incorporating time delay terms. Because it is natural for UDDEs to incorporate some unknown parameters, how to estimate them is a crucial problem in practice. This paper undertakes this issue by applying the method of moments based on discrete observations of solutions. With the Euler difference form of UDDEs, a function with respect to unknown parameters is proved to follow a standard normal uncertainty distribution. The moment estimations for unknown parameters are obtained by …solving a system of equations which uses sample moments to approximate population moments. Analytic solutions for some types of UDDEs are derived. Numerical examples show that estimations give small biases and standard deviations as long as time steps are not too large. Applications to population growth models further illustrate the practicability of our method. Show more

Keywords: Uncertain differential equation, parameter estimation, moments method, uncertainty theory

DOI: 10.3233/JIFS-191751

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 841-849, 2020

Authors: Aydemir, Salih Berkan | Yilmaz Gunduz, Sevcan

Article Type: Research Article

Abstract: Algebraic operations are used effectively in decision-making problems. Especially, Dombi, Hamacher and Einstein algebraic operators are used frequently in the decision-making field. On the other hand, it is known that aggregation operators affect the decision-making process in decision-making problems. In this paper, we used Dombi operations to develop some Fermatean fuzzy aggregation operators. Arithmetic and geometric analysis of each aggregation method were performed. We defined the following operators: Fermatean fuzzy Dombi weighted average operator, Fermatean fuzzy Dombi weighted geometric operator, Fermatean fuzzy Dombi ordered weighted average operator, Fermatean fuzzy Dombi ordered weighted geometric operator, Fermatean fuzzy Dombi hybrid weighted average …operator, Fermatean fuzzy Dombi hybrid weighted geometric operator. Also, an analysis was performed for the beta value of the Dombi parameter. Properties of proposed operators were presented, and operators were defined on Fermatean fuzzy sets. Finally, proposed operators were compared with the existing aggregation operators. To understand the impact of the proposed operators on the decision-making process, Fermatean fuzzy TOPSIS was established. Show more

Keywords: Fermatean fuzzy sets, multi-criteria decision making, TOPSIS, Dombi operations, Fermatean fuzzy aggregation operators

DOI: 10.3233/JIFS-191763

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 851-869, 2020

Authors: Qiu, Chenye

Article Type: Research Article

Abstract: Feature selection is a crucial data pre-processing step in classification problems. The wrapper approach is widely used due to their good classification performance. However, it is very computational expensive due to the cross validation scheme in the evaluation phase. In order to solve this problem, this paper proposes a novel hybrid two-stage feature selection method based on differential evolution (HTSDE). In the first stage, a cluster validity index named DB index is employed to evaluate the feature subset and the wrapper approach in used in the second stage to improve the classification accuracy of the feature subsets. In order to …find global optimal feature subsets, different trail vector generation strategies of DE are used in the two stages where the first stage focuses on global exploration and the second stage emphasizes fast convergence. The hybrid method is able to combine the advantages of both DB index and wrapper approach and improve the computational efficiency of the wrapper approach while maintaining the classification performance. HTSDE is compared with several state-of-the-art feature selection methods on 12 datasets. Experimental results show the proposed HTSDE achieves higher classification accuracy than both wrapper and filter approaches. Moreover, its computational cost is much less than those wrapper approaches. Show more

Keywords: Feature selection, cluster validity index, wrapper approach, differential evolution, trial vector generation strategy

DOI: 10.3233/JIFS-191765

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 871-884, 2020