Journal of Intelligent & Fuzzy Systems - Volume 41, issue 1

Authors: Wang, Tianxiong | Zhou, Meiyu

Article Type: Research Article

Abstract: When users choose a product, they consider the emotional experience triggered by the product form. In view of the fact that traditional kansei engineering can not effectively reflect the complex and changeable psychological factors of users, and it has not explored the complex relationship between customer satisfaction and perceptual demand characteristics. To address this problem, some uncertainty techniques including rough sets and fuzzy sets are applied to capture more accurate emotion knowledge. Therefore, this research proposes an integrated evaluation gird method (EGM), rough set theory (RST), continuous fuzzy kano model (CFKM), fuzzy weighted association rule mining method to extract the …significant relationship between user needs and product morphological features. The EGM is applied to analyze the attractive factor of morphological characteristics of the product, and then the demand items with the highest satisfaction are analyzed through CFKM. The semantic difference method is combined to construct a decision table, and through attribute reduction and importance calculation to obtain the weight of the core product design items. In order to explore the non-linear relationship between design elements and kansei images, the fuzzy weighted association rule mining method was applied to obtain the set of frequent fuzzy weighted association rules based on evidence theory’s reliability indices of minimum support and confidence so as to realize user demand-driven product design. Taking the design of electric bicycle as an example, the experiment results show that the proposed method can help companies or designers develop products to generate good solutions for customer need. Show more

Keywords: Rough set, semantic difference method, fuzzy set, customer satisfaction, kansei engineering

DOI: 10.3233/JIFS-201829

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 331-353, 2021

Authors: Taimoor, Muhammad | Lu, Xiao | Shabbir, Wasif | Sheng, Chunyang | Samiuddin, Muhammad

Article Type: Research Article

Abstract: This research is concerned with the adaptive neural network observer based fault approximation and fault-tolerant control of time-varying nonlinear systems. A new strategy for adaptively updating the weights of neural network parameters is proposed to enhance fault detection accuracy. Lyapunov function theory (LFT) is applied for adaptively updating the learning parameters weights of multi-layer neural network (MLNN). The purpose of using adaptive learning rates to update the weight parameters of MLNN is to obtain the global minima for highly nonlinear functions without increasing the computational complexities and costs and increase the efficacy of fault detection. Results of the proposed adaptive …MLNN observer are compared with conventional MLNN observer and high gain observer. The effects of various faults or failures are studied in detail. The proposed strategy shows more robustness to disturbances, uncertainties, and unmodelled system dynamics compared to the conventional neural network, high gain observer and other existing techniques in literature. Fault tolerant control (FTC) schemes are also proposed to account for the presence of various faults and failures. Separate sliding mode control (SMC) based FTC schemes are designed for each observer to ensure stability of the faulty system. The suggested strategy is validated on Boeing 747 100/200 aircraft. Results demonstrate the effectiveness of both the proposed adaptive MLNN observer and the FTC based on the proposed adaptive MLNN compared to the conventional MLNN, high gain observer and other existing schemes in literature. Comparison of the performance of all the strategies validates the superiority of the proposed strategy and shows that the FTC based on proposed adaptive MLNN strategy provides better robustness to various situations such as disturbances and uncertainties. It is concluded that the proposed strategy can be integrated into the aircraft for the purpose of fault diagnosis, fault isolation and FTC scheme for increasing the performance of the system. Show more

Keywords: Sensors, fault detection, fault-tolerant control, neural networks, sliding mode control, observer

DOI: 10.3233/JIFS-201830

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 355-386, 2021

Authors: Li, Li | Xie, Yongfang | Chen, Xiaofang

Article Type: Research Article

Abstract: Root cause diagnosis is of great significance to make efficient decisions in industrial production processes. It is a procedure of fusing knowledge, such as empirical knowledge, process knowledge, and mechanism knowledge. However, it is insufficient and low reliability of cause analysis methods by using crisp values or fuzzy numbers to represent uncertain knowledge. Therefore, a dynamic uncertain causality graph model (DUCG) based on picture fuzzy set (PFS) is proposed to address the problem of uncertain knowledge representation and reasoning. It combines the PFS with DUCG model to express expert doubtful ideas in a complex system. Then, a new PFS operator …is introduced to characterize the importance of factors and connections among various information. Moreover, an enhanced knowledge reasoning algorithm is developed based on the PFS operators to resolve causal inference problems. Finally, a numerical example illustrates the effectiveness of the method, and the results show that the proposed model is more reliable and flexible than the existing models. Show more

Keywords: Root cause diagnosis, uncertain knowledge, picture fuzzy set, dynamic uncertainty causality graph

DOI: 10.3233/JIFS-201837

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 387-397, 2021

Authors: Liu, Qianqian | Shi, Gang | Sheng, Yuhong

Article Type: Research Article

Abstract: In this paper, an uncertain SEIR rumor model driven by one uncertain process is formulated to investigate the influence of perturbation in the transmission of rumor. Firstly, the deduced process of the uncertain SEIR rumor model is presented. Then, we proposed the existence and uniqueness theorem for the solution of the model. Moreover, the study of the stability of the uncertain SEIR rumor model was carried out, and then we came to the conclusion that the model stable in mean. In addition, computer algorithm and numerical simulation is used to verify the accuracy of the theoretical results. The simulation results …show that the proposed model can explain the trend of rumor propagation correctly and describe the rumor propagation accurately. Finally, we have compared the propagation process of the uncertain rumor model and the deterministic model according to the numerical algorithm, and drew the conclusion that the model with uncertain perturbation fluctuates around the deterministic model. Show more

Keywords: Uncertain differential equation, Liu process, Uncertain SEIR model, Existence and uniqueness, Stability

DOI: 10.3233/JIFS-201865

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 399-412, 2021

Authors: Trisal, Sushil Kumar | Kaul, Ajay

Article Type: Research Article

Abstract: Stress has become a household word which generates emotional distress, physical diseases, dysfunction and social ills. An abundant evidence is present in the literature that makes the stress research and theory high profile and important for physiological, psychological and social health. It can be legitimately said that due to the advent of social media, it has opened up inputs for the exploration of stress. The social media has become very prominent as it has touched daily lives. It has changed the way we are looking at the things, it has changed the life style, it has changed the way we …are consuming the information. It has created a bridge of trust among the people of different professional’s. Social media has become undeniably a global phenomenon in the last decade or so, since the founding of social media sites like Twitter and Facebook. It is of significant importance to detect and manage the stress from theses interactions at early stage otherwise it wreaks havoc on your emotional equilibrium and your physical health. It narrows your ability to think clearly, function effectively and enjoy life. In this work our endeavor is that to present a novel method to detect the different stress levels from the social media interactions using fuzzy and factor graph methods. A correlation analysis between stressed, non-stressed and emotion tweets is carried out for social engagement correlation and behavior correlation analysis of the social media users. The proposed method performs better when results are compared with the other state of art machine learning methods. Show more

Keywords: Stress, social engagement, correlation, psychological stress, machine learning, social media

DOI: 10.3233/JIFS-202035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 413-430, 2021

Authors: Palraj, K. | Kalaivani, V.

Article Type: Research Article

Abstract: In modern times, digital medical images play a significant progression in clinical diagnosis to treat the populace earlier to hoard their lives. Magnetic Resonance Imaging (MRI) is one of the most advanced medical imaging modalities that facilitate scanning various parts of the human body like the head, chest, abdomen, and pelvis and identify the diseases. Numerous studies on the same discipline have proposed different algorithms, techniques, and methods for analyzing medical digital images, especially MRI. Most of them have mainly focused on identifying and classifying the images as either normal or abnormal. Computing brainpower is essential to understand and handle …various brain diseases efficiently in critical situations. This paper knuckles down to design and implement a computer-aided framework, enhancing the identification of humans’ cognitive power from their MRI. Images. The proposed framework converts the 3D DICOM images into 2D medical images, preprocessing, enhancement, learning, and extracting various image information to classify it as normal or abnormal and provide the brain’s cognitive power. This study widens the efficient use of machine learning methods, Voxel Residual Network (VRN), with multimodality fusion architecture to learn and analyze the image to classify and predict cognitive power. The experimental results denote that the proposed framework demonstrates better performance than the existing approaches. Show more

Keywords: Medical image processing, MRI, deep learning, 3D MRI, 2D brain segmentation, classification, cognitive power of brain

DOI: 10.3233/JIFS-202069

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 431-449, 2021

Authors: Du, Wen Sheng

Article Type: Research Article

Abstract: Granular computing is a relatively new platform for constructing, describing and processing information or knowledge. For crisp information granulation, the universe is decomposed into granules by binary relations on the universe, say, preorder, tolerance and equivalence relations. A knowledge structure is composed of all information granules induced by a relation that corresponds to the granulation. This paper establishes a novel theoretical framework for the measurement of information granularity of knowledge structures. First, two new relations between knowledge structures are introduced through the use of their respective Boolean relation matrices, where the granular equality relation is defined based on an orthogonal …transformation with the transformation matrix being a permutation matrix, and the granularly finer relation is presented by combining the classical finer relation and the orthogonal transformation. Then, it is demonstrated that the simplified knowledge structure base with the granularly finer relation is a partially ordered set, which can be represented by a Hasse diagram. Subsequently, an axiomatic definition of information granularity is proposed to satisfy the constraints regarding these two relations. Moreover, a general form of the information granularity is given, and some existing measures are proved to be its special cases. Finally, as an application of the proposed measure, the attribute significance measure is developed based on the information granularity. Show more

Keywords: Granular computing, information granularity, knowledge structure, granular equality relation, granularly finer relation

DOI: 10.3233/JIFS-202086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 451-466, 2021

Authors: Liu, Jia | Wang, Shuwei

Article Type: Research Article

Abstract: It is impossible for agents on both sides to achieve complete rationality in the decision-making process of two-sided matching (TSM). The TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method considering the psychological behavior of decision-makers is well applied in the multiple criteria decision making (MCDM) problems. The TSM is a MCDM problem. Therefore, in this paper, a method based on TODIM technique is introduced to solve the TSM problem, in which the intuitionistic linguistic numbers are utilized to describe the mutual evaluation between candidates and hiring managers. The focus of this paper is to develop a method …for the multi-criteria TSM problem under intuitionistic linguistic environment. First, the evaluation matrices of each agent with respect to each criterion are provided by agents on the opposite side, and the weight assigned to each criterion is determined according to the importance of the evaluation criterion to the matching agent. Then, the dominance measurement of each agent over another one can be calculated based on the intuitionistic linguistic TODIM method. Next, a bi-objective optimization model which aims to maximize the overall satisfaction degree of agents on both sides is constructed to attain the optimal matching pair. Furthermore, the feasibility of the solution method is verified by a case study of person-position matching (PPM), and the matching result demonstrates that the proposed method is effective in dealing with multi-criteria PPM problem. Finally, the sensitivity of parameters and some comparative studies are discussed. Show more

Keywords: Two-sided matching, TODIM method, intuitionistic linguistic set, optimization model

DOI: 10.3233/JIFS-202087

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 467-480, 2021

Authors: Poonia, Mahima | Bajaj, Rakesh Kumar

Article Type: Research Article

Abstract: In the present work, the adjacency matrix, the energy and the Laplacian energy for a picture fuzzy graph/directed graph have been introduced along with their lower and the upper bounds. Further, in the selection problem of decision making, a methodology for the ranking of the available alternatives has been presented by utilizing the picture fuzzy graph and its energy/Laplacian energy. For the shake of demonstrating the implementation of the introduced methodology, the task of site selection for the hydropower plant has been carried out as an application. The originality of the introduced approach, comparative remarks, advantageous features and limitations have …also been studied in contrast with intuitionistic fuzzy and Pythagorean fuzzy information. Show more

Keywords: Picture fuzzy graph, score function, energy of graph, Laplacian energy, adjacency matrix, spectrum

DOI: 10.3233/JIFS-202131

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 481-498, 2021

Authors: Brás, Glender | Silva, Alisson Marques | Wanner, Elizabeth Fialho

Article Type: Research Article

Abstract: This paper introduces a new approach to build the rule-base on Neo-Fuzzy-Neuron (NFN) Networks. The NFN is a Neuro-Fuzzy network composed by a set of n decoupled zero-order Takagi-Sugeno models, one for each input variable, each one containing m rules. Employing Multi-Gene Genetic Programming (MG-GP) to create and adjust Gaussian membership functions and a Gradient-based method to update the network parameters, the proposed model is dubbed NFN-MG-GP. In the proposed model, each individual of MG-GP represents a complete rule-base of NFN. The rule-base is adjusted by genetic operators (Crossover, Reproduction, Mutation), and the consequent parameters are updated by …a predetermined number of Gradient method epochs, every generation. The algorithm uses Elitism to ensure that the best rule-base is not lost between generations. The performance of the NFN-MG-GP is evaluated using instances of time series forecasting and non-linear system identification problems. Computational experiments and comparisons against state-of-the-art alternative models show that the proposed algorithms are efficient and competitive. Furthermore, experimental results show that it is possible to obtain models with good accuracy applying Multi-Gene Genetic Programming to construct the rule-base on NFN Networks. Show more

Keywords: Neo-fuzzy-neuron, genetic programming, multi-gene, NFN-MG-GP, forecasting, non-linear system identification

DOI: 10.3233/JIFS-202146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 499-516, 2021