Journal of Intelligent & Fuzzy Systems - Volume 38, issue 4

Authors: Liu, Yan | Wang, Xiao-Kang | Wang, Jian-Qiang | Li, Lin | Cheng, Peng-Fei

Article Type: Research Article

Abstract: This paper proposes a cloud model-based Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) method with 2D uncertain linguistic variables (2DULVs). 2DULVs are adopted by decision makers (DMs) to evaluate each alternative under the criteria because they can provide extra evaluation information. Cloud model is adopted to depict randomness and fuzziness. The possibility degree and possibility degree index are defined to develop an improved PROMETHEE II method for sorting alternatives. Entropy weight method is used to calculate the weight of each criterion. A renewable energy performance sample is used to illustrate the applicability of the proposed method. Sensitivity analysis and …four comparative experiments demonstrate the stability and accuracy of the proposed approach. Show more

Keywords: 2D uncertain linguistic variable, cloud, possibility degree, possibility degree index, improved PROMETHEE

DOI: 10.3233/JIFS-191546

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4869-4887, 2020

Authors: Qin, Xuezhi | Lin, Xianwei | Shang, Qin

Article Type: Research Article

Abstract:  In order to introduce the long memory property of financial markets into the study of binary option pricing under fuzzy environment, the fractional Brownian motion is used to describe the dynamics of the stock price. This paper develops a new framework for pricing the binary option by using fuzzy set theory based on the long memory property of financial markets. The fuzzy price of the binary option is obtained by using a risk-neutral pricing principle and quasi-conditional expectation. To better understand the pricing model, some Greeks of this pricing model are given. In addition, the influence of the Hurst parameter …H , a measure of long memory in the financial market, on binary option pricing is analyzed. Finally, the study provides an example that study binary option by fuzzifying the maturity value of the stock price using the triangular fuzzy number. The numerical experiment demonstrates the fuzzy pricing model proposed is rational and practicable. Show more

Keywords: Binary option, fuzzy option pricing, fractional brownian motion, asset-or-nothing option

DOI: 10.3233/JIFS-191551

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4889-4900, 2020

Authors: Yin, Shizhuang | Wang, Tao

Article Type: Research Article

Abstract: In order to solve the clustering problem of unknown binary protocols, an improved k -means unknown binary protocol clustering method is proposed, which determines the initial clustering center and improves the clustering distance. Firstly, the k value is determined and the clustering center is extracted by using DCBP (Determine the initial clustering center of binary Protocol) algorithm and the change rate of error square, and then the data are clustered by improving the k -means algorithm of distance function. The unknown binary protocol bit stream is divided into different subsets of binary protocols. By improving the k -means algorithm, …the Pearson distance improves the accuracy of binary protocol clustering from 96% to 98.9%. The DCBP algorithm helps us to determine the k value accurately. The k value determined in this paper is 5, and the clustering accuracy is 98.9%. The clustering accuracy is 80% when k is 4 and 92.2% when k is 6. And the operation speed of the improved k -means algorithm is better than that of the AGNES algorithm. The algorithm is better adapted to the clustering of unknown binary protocols, and improves the accuracy of clustering and the speed of operation. Show more

Keywords: Protocol identification, unknown binary protocol, Pearson distance, determine cluster center

DOI: 10.3233/JIFS-191561

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4901-4913, 2020

Authors: Gao, Yi | Sun, Xia | Wang, Xin | Guo, Shouxi | Feng, Jun

Article Type: Research Article

Abstract:  Forum posts in Massive Open Online Courses (MOOCs) support an important way for online learners to interact with each other and with instructors. Instructors explore the sentiment from posts in MOOCs to detect learners’ trending opinions towards the course so that they can improve MOOCs. However, it is unrealistic to expect instructors to adequately track learners’ sentiment under the large number of messages exchanged on the forums. Fortunately, sentiment classification can automatically analyze learners’ emotion on the course of MOOCs from posts. Traditional classifiers based on machine learning algorithm, which often depend on human-designed features and have data sparsity problem. …In contrast to traditional approaches, we develop a novel neural network model called parallel neural network (PNNs) for sentiment classification of MOOCs discussion forum to alleviate the aforementioned problems. In our model, we design a parallel neural network structure to replace the popular serial neural network structure so that PNNs can preserve the validity of features as far as possible when neural network model training. Meanwhile, we also introduce Self-attention mechanism that automatically identifies which features play key roles in sentiment classification to obtain the important components in posts. We experiment on a public MOOCs dataset and two common sentiment classification datasets, and achieve a good performance. That means PNNs is a substantially reliable classification model for identifying the sentiment polarity of posts. The study has great potential application value on the platform of large scale courses, which can help instructors to gain the emotional tendency of learners for the course content in real time, so that timely intervention to support learning and may reduce the dropout rates. Show more

Keywords: Parallel neural network, sentiment classification, MOOCs, learners’ sentiment

DOI: 10.3233/JIFS-191572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4915-4927, 2020

Authors: Alos, Ahmad | Dahrouj, Z.

Article Type: Research Article

Abstract: The importance of detecting faults in Unmanned Aerial Vehicles motivated researchers to work in this area over recent years. Complex relationships among UAV attributes (Sensor readings, and Commands) make the task a bit challenging. Many known algorithms consider detecting the faults by spotting data anomalies in the values of each attribute without concern for their context, which leaves an opportunity for potential improvement. The contextual faults occur when a defected sensor shows an invalid value concerning other attributes. Our contribution is a novel matrix platform for detecting the potential contextual faults. This platform consists of multiple small Decision Trees, instead …of using one huge single Decision Tree, which could be difficult and time-consuming to produce, particularly in the case of a large dataset with too many attributes. We propose to use the C4.5 decision tree algorithm to build each decision tree. The Decision Tree is a machine learning technique, which is an effective supervised method used for classification. It is computationally inexpensive and capable of dealing with noisy data. Besides, our approach uses a sliding window technique during training and testing phases, which brings into consideration the effect of the previous state of the system on the process of detecting the contextual faults. The algorithm starts by collecting the attributes of the UAV into a table of pairs, where each pair consists of two attributes; then, it defines the Decision Tree matrix by assigning one Decision Tree for each pair of attributes. The Training step includes constructing training sub-datasets using the values of sliding windows. The C4.5 algorithm uses each constructed training sub-dataset to induce one Decision Tree in the matrix. Finally, the testing step is responsible for reading the values of the sliding windows and using the concerned Decision Tree to detect the contextual faults. We evaluated our approach using Detection Rate, False Alarm Rate, Precision, and F1-score indicators. Moreover, we made a comparison with other broadly used algorithms, such as K-Means and One-Class SVM. Our approach showed superior results in detecting different types of faults (sensor-offset, sensor-stuck, sensor-drift, and sensor-cut). The DT-Matrix performance was neither affected by the small values of the outliers, nor by the number of the outliers, and this caused the DT-Matrix to work better in most of the experiments compared to the other algorithms. Show more

Keywords: UAV, decision tree, anomaly detection, abnormal, classification, system failure, sensor faults, contextual faults, supervised algorithm

DOI: 10.3233/JIFS-191575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4929-4939, 2020

Authors: Xue, Wei | Wang, Qi | Liu, Xiaona

Article Type: Research Article

Abstract: Although the Takagi-Sugeno-Kang (TSK) fuzzy classifier has achieved great success, how to further improve its classification performance and enhance its interpretability is still one of the most difficult challenges. Involved with the fusion of existing decision information and pre-known classification task, a newly proposed deep/hierarchical TSK fuzzy classifier (EDIPK-TSK) with interpretable fuzzy rules makes full use of the classification advantages of each base classifier to construct a multi-layer deep learning structure. This study first considers that the existing decision information of each training sub-block is sequentially projected into the subsequent sub-blocks for training. Undoubtedly, the existing decision information has played …a guiding role in the current learning process to some extent. Simultaneously, the pre-known classification task is fused into the decision information for fine-tuning of it, which can significantly improve the efficiency of guidance and accelerate the fitting speed of the model. In each layer, the use of interpretable integration input space guarantees that EDIPK-TSK is not a black box. The proposed deep classifier can realize learning by using short fuzzy rules, which ensures the satisfactory interpretability of the classifier. The final experimental results also verify that EDIPK-TSK has strong classification advantages and interpretability. Show more

Keywords: Fuzzy classifier, deep learning structure, existing decision information, interpretability, classification performance

DOI: 10.3233/JIFS-191579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4941-4957, 2020

Authors: Chang, Wen-Jer | Chang, Chih-Ming | Lin, Yann-Hong

Article Type: Research Article

Abstract: A novel robust fuzzy controller design problem subject to multi-variance constraints and pole location constraints for nonlinear discrete-time systems with internal and external noises is studied in this paper. Based on the Takagi-Sugeno fuzzy model, the nonlinear discrete-time systems are represented by blending many linear subsystems. The control performances considered in this paper include stability requirement, pole location constraint, individual state variance constraint, and minimum output variance. Applying the Lyapunov theory, a discrete-time robust fuzzy controller is designed based on parallel distributed compensation technology and the relevant conditions are deduced in the form of linear matrix inequalities. By solving these …conditions, a discrete-time robust fuzzy controller can be obtained to satisfy the above performance constraints. At last, some simulations for controlling a nonlinear inverted pendulum system and a nonlinear ship steering system are provided to show the feasibility and applicability of the proposed robust fuzzy control method. Show more

Keywords: Robust fuzzy control, discrete-time Takagi-Sugeno fuzzy model, variance constraints and pole location constraints

DOI: 10.3233/JIFS-191600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4959-4975, 2020

Authors: Shahzadi, Gulfam | Akram, Muhammad | Davvaz, Bijan

Article Type: Research Article

Abstract:  A Pythagorean fuzzy soft set, an extension of intuitionistic fuzzy soft set, plays an essential role to handle the vagueness in many real-life problems. We apply this concept to graph theory, and present certain new notions including, perfectly regular Pythagorean fuzzy soft graphs (PFSGs), perfectly edge-regular PFSGs and explore some of their properties. We formulate the notion of perfectly irregular PFSGs, perfectly edge-irregular PFSGs and open neighborhood degree sum ( O ˆ NDS ) and closed neighborhood degree sum ( C ˆ NDS ) of PFSGs. …Finally, we discuss some decision-making problems of PFSGs. Show more

Keywords: Pythagorean fuzzy soft graphs, perfectly edge-regular, perfectly edge-irregular

DOI: 10.3233/JIFS-191610

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4977-4991, 2020

Authors: Taimoor, Muhammad | Aijun, Li

Article Type: Research Article

Abstract: An online fault detection, isolation, and reconstruction strategy is proposed for actuators and sensors fault detection of an aircraft. For increasing the fault detection capabilities, the Extended Kalman Filter (EKF) is used for the weight updating parameters of multi-layer perceptron (MLP) neural network. The main purpose of using the EKF is to make the weight updating parameters of MLP adaptive in order to increase the fault detection, isolation and reconstruction preciseness, efficiency and rapidness compared to the conventional MLP where the fixed learning rate due to which it has slow response to faults occurrence. Because of the online adaptation of …weighting parameters of MLP, the preciseness of the faults detection is increased. For testing and validation of the proposed strategy, the nonlinear dynamics of Boeing 747 100/200 are used. Results demonstrate that the proposed strategy has better accuracy and rapid response to fault detection compared to convention multi-layer perceptron neural network based faults detection schemes. Show more

Keywords: Actuators, sensors, fault detection and isolation, aircraft, neural networks, nonlinear systems

DOI: 10.3233/JIFS-191627

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4993-5012, 2020

Authors: Xue, Zhan’ao | Zhao, Li-Ping | Zhang, Min | Sun, Bing-Xin

Article Type: Research Article

Abstract: Three-way decisions have become a representative of the models dealing with decision-making problems with uncertainty and fuzziness. However, most of the current models are single granular structures that cannot meet the needs of complex fuzzy environmental decision-making. Multi-granulation rough sets can better deal with fuzzy problems of multiple granularity structures. Therefore, three-way decisions will be a more reasonable decision-making model to address uncertain decision problems in the context of multiple granularity structures. In this paper, firstly we propose the four different conditional probabilities based on support intuitionistic fuzzy sets, which are referred to as support intuitionistic fuzzy probability. Then, a …multi-granulation support intuitionistic fuzzy probabilistic approximation space is defined. Secondly, we calculate the thresholds α and β by the Bayesian theory, and construct four different types of multi-granulation support intuitionistic fuzzy probabilistic rough sets models in multi-granulation support intuitionistic fuzzy probabilistic approximation space. Moreover, some properties of lower and upper approximation operators of these models are discussed. Thirdly, by combining these proposed models with three-way decision theory, the corresponding three-way decision models are constructed and three-way decision rules are derived. Finally, an example of person-job fit procedure is given to prove and compare the validity of these proposed models. Show more

Keywords: Support intuitionistic fuzzy sets, rough sets, support intuitionistic fuzzy probabilistic, multi-granulation rough sets, three-way decisions

DOI: 10.3233/JIFS-191657

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5013-5031, 2020