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

Authors: Kim, Jong Kyu | Mehmood, Nayyar | Al Rawashdeh, Ahmed

Article Type: Research Article

Abstract:  In this article, we study the notion of the variational inequalities for lattice-valued fuzzy relations. In this context, a variational inequality problem has been proposed that generalizes many results in the literature. The conditions for the existence of solutions of the proposed problem have been discussed. It has been shown that the proposed variational inequality problem is equivalent to a fixed point problem. This fixed point formulation allows us to present an iterative algorithm to approximate solution of the variational inequality problem. For applications, first the existence result for the solutions of an ℒ-fuzzy Caputo-Fabrizio fractional differential inclusion initial …value problem involving a projection operator has been proved. Then the solutions of an obstacle boundary value variational inequality problem in function spaces has been obtained. Show more

Keywords: ℒ-fuzzy relations, fixed points, variational inequalities, iterative algorithm, 46S40, 47H10, 54H25

DOI: 10.3233/JIFS-190894

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 145-153, 2020

Authors: Gao, Rong | Ahmadzade, Hamed | Rezaei, Kamran | Rezaei, Hassan | Naderi, Habib

Article Type: Research Article

Abstract: A similarity measure determines the similarity between two objects. As important roles of similarity measure in chance theory, this paper introduces the concept of partial similarity measure for two uncertain random variables. Based on maximum similarity principle, partial similarity measure are used to recognize pattern problems. As an application in finance, partial similarity measure is applied to optimize portfolio selection of uncertain random returns via Monte-Carlo simulation and craw search algorithm.

Keywords: Chance theory, uncertain random variable, partial similarity measure, portfolio selection, pattern recognition

DOI: 10.3233/JIFS-190942

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 155-166, 2020

Authors: Li, Chunhua | Xu, Baogen | Huang, Huawei

Article Type: Research Article

Abstract: The adequate analysis of bipolar information of a semigroup using a fuzzy set requires incorporation of a bipolar fuzzy set and an appropriate semigroup structure. Motivated by studying partial order and lattice of bipolar fuzzy sets, and algebraic framework of bipolar fuzzy sets, in this paper, we introduce the notion of a bipolar fuzzy abundant semigroup by developing a new technique for constructing fuzzy semigroups. After obtaining some properties of bipolar fuzzy abundant semigroups, we give necessary and sufficient conditions of a bipolar fuzzy subset of an abundant semigroup to be bipolar fuzzy abundant. As an application, we extend our …results to the case of regular semigroup. In particular, bipolar fuzzy regular semigroups are investigated. Show more

Keywords: Bipolar fuzzy set, bipolar fuzzy abundant semigroup, good homomorphism, regularity condition, bipolar fuzzy regular semigroup, 20M20

DOI: 10.3233/JIFS-190951

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 167-176, 2020

Authors: Anoor, Muhammad Marwan | Jahidin, Aisyah Hartini | Arof, Hamzah | Megat Ali, Megat Syahirul Amin

Article Type: Research Article

Abstract: Intelligence and learning styles are among most widely studied traits in cognitive psychology. Currently, both aspects of cognition can only be assessed using paper-based psychometric tests. The methods however, are exposed to inconsistency issues due to the variation of examination format and language barriers. Hence, this study proposes an intelligent system for assessing intelligence quotient (IQ) level and learning style from the resting brainwaves using artificial neural network (ANN). Eighty-five individuals from varying educational backgrounds have participated in this study. Resting electroencephalogram (EEG) is recorded from the left prefrontal cortex using NeuroSky. Control groups are established using Kolb’s Learning Style …Inventory (LSI) and a model developed based on Raven’s Progressive Matrices (RPM). Subsequently, theta, alpha and beta power ratio is extracted from the pre-processed EEG. Distribution and pattern of features show a correlation with the Neural Efficiency Hypothesis of intelligence and Alpha Suppression Theory. The power ratio features are then used to train, validate and test the ANN model. The system has demonstrated satisfactory performance for IQ classification with accuracies of 98.3% for training and 94.7% for testing. The proposed model is also able to classify learning style with accuracies of 96.9% for training and 80.0% for testing. Show more

Keywords: EEG, intelligent system, IQ, learning style, neural network

DOI: 10.3233/JIFS-190955

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 177-194, 2020

Authors: Zaheeruddin, | Singh, Kavita

Article Type: Research Article

Abstract: Due to integration of different distributed power sources in microgrid, power quality is adversely affected and has caused many control problems. Hence power system requires much more proficiency and adaptability in control and optimization to overcome these problems. The power quality issues in microgrid system are mainly from frequency fluctuations. In real scenario, frequency fluctuations happen because of impulsive variations in load/generation or both. This research study presents a Fractional Order Fuzzy PID (FOFPID) controller for frequency control in microgrid. To test effectiveness of proposed controller, its performance is evaluated and compared with standard PID and Fuzzy PID (FPID) controller. …To find optimal parameters of the FOFPID, Gravitational Search Algorithm (GSA) is employed. To illustrate the effectiveness of GSA, its outcome is compared with existing algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. Further performance of each controller and optimizing method is assessed by looking at the fitness function value, statistical data, frequency deviation, amplitude and oscillations of control signal. Finally, the most optimized algorithm-based controller is tested for robustness against parameter variations and nonlinearities like Generation Rate Constraint (GRC). Show more

Keywords: Fuzzy PID controller, fractional order fuzzy PID controller, microgrid, frequency deviation

DOI: 10.3233/JIFS-190963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 195-212, 2020

Authors: Hu, Guolin | Wang, Likui | Liu, Xiaodong

Article Type: Research Article

Abstract: This paper addresses local H ∞ control method to reject the disturbance for continuous-time T-S fuzzy models. Firstly, in order to overcome a few drawbacks of the previous results such as the special structure of free variable, redundant restrictive conditions and parameters, the time derivatives of the membership functions are analyzed and new linear matrix inequalities are obtained. Secondly, the H ∞ control theorem is obtained based on the new conditions. Finally, two examples are given to illustrate the effectiveness of the results.

Keywords: Takagi-Sugeno fuzzy model, linear matrix inequalities (LMIs), non-quadratic fuzzy Lyapunov function, H∞ control

DOI: 10.3233/JIFS-190974

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 213-220, 2020

Authors: Long, Xin | Zeng, Xiangrong | Liu, Yan | Xiao, Huaxin | Zhang, Maojun | Ben, Zongcheng

Article Type: Research Article

Abstract:  The deployment of large-scale Convolutional Neural Networks (CNNs) in limited-power devices is hindered by their high computation cost and storage. In this paper, we propose a novel framework for CNNs to simultaneously achieve channel pruning and low-bit quantization by combining weight quantization with Sparse Group Lasso (SGL) regularization. We model this framework as a discretely constrained problem and solve it by Alternating Direction Method of Multipliers (ADMM). Different from previous approaches, the proposed method reduces not only model size but also computational operations. In experimental section, we evaluate the proposed framework on CIFAR datasets with several popular models such as …VGG-7/16/19 and ResNet-18/34/50, which demonstrate that the proposed method can obtain low-bit networks and dramatically reduce redundant channels of the network with slight inference accuracy loss. Furthermore, we also visualize and analyze weight tensors, which showing the compact group-sparsity structure of them. Show more

Keywords: Convolutional neural network (CNN), weight quantization, sparse group lasso (SGL), alternating direction method of multipliers (ADMM), channel pruning

DOI: 10.3233/JIFS-191014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 221-232, 2020

Authors: Ashokkumar, S.R | MohanBabu, G.

Article Type: Research Article

Abstract: Epilepsy is a nervous disorder that causes arbitrary recurrent seizures within the cerebral cortex region of the encephalon. The early diagnosis of a seizure is important in clinical therapy. An automatic epileptic seizure detection method for electroencephalogram (EEG) signals can significantly enhance the patient’s life in clinical aspect. The proposed paper is principally based on a completely unique approach of epileptic seizure detection using Q-Tuned Wavelet Transform (QTWT) and Approximate entropy (ApEn). This work focuses by utilizing and testing the common sense of Extreme Learning Adaptive Neuro-Fuzzy Inference System Model (EXL-ANFIS) which foresees the elements of the mind states as …a trajectory that results in the seizure event. QTWT is used for decomposing EEG signals into sub-band frequency signals. Approximate entropy is carried out to those sub-band signals as a discriminatory function because of its indefinite disordered feature. The solutions obtained by directing towards EXL- ANFIS shows an incredible advancement in the perpetual performance outlay for the classification of an epileptic seizure. The proposed classification method is implemented on publicly available Bonn dataset. The outcome confirms that by combining extreme learning and ANFIS model improves the classification accuracy and decrease the feature dimension with reduced computational complexity. This method achieves 99.72% of classification accuracy over existing models. Show more

Keywords: Epilepsy, electroencephalogram (EEG), Q-Tuned wavelet transform (QTWT), approximate entropy (ApEn), extreme learning adaptive neuro-fuzzy inference system model (EXL-ANFIS)

DOI: 10.3233/JIFS-191015

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 233-248, 2020

Authors: Lin, Yi-Nan | Wang, Sheng-Kuan | Yang, Cheng-Ying | Shen, Victor R.L. | Juang, Tony Tong-Ying | Wei, Chin-Shan

Article Type: Research Article

Abstract: Currently, JavaScript is a popular scripting language for building web pages. It allows website creators to run any program code they want when users are visiting their websites. Meanwhile, malicious JavaScript becomes one of the biggest threats in the cyber world. Researchers are now searching for a convenient and effective way to detect JavaScript malware. Consequently, this paper aims to propose a novel method of detecting the JavaScript malware by using a high-level fuzzy Petri net (HLFPN). First, the web pages are crawled to get JavaScript files. Second, those main features are extracted from JavaScript files. In total, six main …features of the JavaScript, including longest word size, entropy, specific character, commenting style, function calls, and abstract syntax tree (AST) features are collected. Finally, an HLFPN model is used to determine whether the malicious code is available or not. The experimental results have fully demonstrated the effectiveness of our proposed approach. Show more

Keywords: Fuzzy reasoning, JavaScript malware detection, high-level fuzzy Petri net, cyber security

DOI: 10.3233/JIFS-191038

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 249-261, 2020

Authors: Rashid, Muhammad Aamer | Ahmad, Sarfraz | Siddiqui, Muhammad Kamran

Article Type: Research Article

Abstract: In this paper, we introduce the concepts of total uniform vertex fuzzy soft graphs and total uniform edge fuzzy soft graphs. In the view of this concept, we study the degree of a vertex, the total degree of a vertex and the complement fuzzy soft graphs. Also, we prove our main results about regular and totally regular fuzzy soft graphs, and the conditions under which the complement of regular fuzzy soft graph becomes regular as well as totally regular fuzzy soft graphs. We also describe applications of fuzzy soft graphs in telecommunication network.

Keywords: Fuzzy soft graph, regular fuzzy soft graph, totally regular fuzzy soft graph, degree of a vertex, total degree of a vertex, complement fuzzy soft graph

DOI: 10.3233/JIFS-191058

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 263-275, 2020