Application Of Fuzzy System Research Articles

Fuzzy Logic Concepts, Developments and Implementation

Over the past few decades, the field of fuzzy logic has evolved significantly, leading to the development of diverse techniques and applications. Fuzzy logic has been successfully combined with other artificial intelligence techniques such as artificial neural networks, deep learning, robotics, and genetic algorithms, creating powerful tools for complex problem-solving applications. This article provides an informative description of some of the main concepts in the field of fuzzy logic. These include the types and roles of membership functions, fuzzy inference system (FIS), adaptive neuro-fuzzy inference system and fuzzy c-means clustering. The processes of fuzzification, defuzzification, implication, and determining fuzzy rules’ firing strengths are described. The article outlines some recent developments in the field of fuzzy logic, including its applications for decision support, industrial processes and control, data and telecommunication, and image and signal processing. Approaches to implementing fuzzy logic models are explained and, as an illustration, Matlab (version R2024b) is used to demonstrate implementation of a FIS. The prospects for future fuzzy logic developments are explored and example applications of hybrid fuzzy logic systems are provided. There remain extensive opportunities in further developing fuzzy logic-based techniques, including their further integration with various machine learning algorithms, and their adaptation into consumer products and industrial processes.

Fixed Point Results for Fuzzy Enriched Contraction in Fuzzy Banach Spaces with Applications to Fractals and Dynamic Market Equillibrium

We introduce fuzzy enriched contraction, which extends the classical notion of fuzzy Banach contraction and encompasses specific fuzzy non-expansive mappings. Our investigation establishes both the presence and uniqueness of fixed points considering this broad category of operators using a Krasnoselskij iterative scheme for their approximation. We also show the graphical representation of fuzzy enriched contraction and analyze its graph for different values of beta. The implications of these findings extend to significant results within fuzzy fixed-point theory, enriching the understanding of iterative processes in fuzzy metric spaces. To demonstrate the versatility of our innovative concepts and the associated fixed-point theorems, we provide illustrative examples that showcase their applicability across diverse domains, including the generation of fractals. This demonstrates the relevance of fuzzy enriched contraction to iterated function systems, enabling the study of fractal structures under various contractive conditions. Additionally, we explore practical applications of fuzzy enriched contraction in dynamic market equilibrium, offering new insights into stability and convergence in economic models. Through this unified framework, we open new avenues for both theoretical advancements and real world applications in fuzzy systems.

Application of deep learning-based fuzzy systems to analyze the overall risk of mortality in glioblastoma multiforme

Abstract Glioblastoma multiforme (GBM) is the most aggressive brain cancer in adults, with 3.2-3.4 cases per 100 thousand. In the US, brain cancer does not rank in the top 10 causes of death, but it remains in the top 15. Therefore, this research proposes a fuzzy-based GRUCoxPH model to identify missense variants associated with a high risk of all-cause mortality in GBM. The study combines various models, including fuzzy logic, Gated Recurrent Units (GRUs), and Cox
Proportional Hazards Regression (CoxPh), to identify potential risk factors. The dataset is derived from TCGA-GBM clinicopathological information and mutations to create four risk score models: GRU, CoxPH, GRUCoxPHAddition, and GRUCoxPHMultiplication, analyzing 9 risk factors of the dataset. The Fuzzy-based GRUCoxPH model achieves an average accuracy of
86.97%, outperforming other models. This model demonstrates its ability to classify and identify missense variants associated with mortality in GBM, potentially advancing cancer research.

Critical study of type-2 fuzzy logic control from theory to applications: A state-of-the-art comprehensive survey

The review systematically explores the theoretical foundations of type-2 fuzzy sets and the associated mathematical frameworks of the advancements in type-2 fuzzy logic systems (T2-FLS), as well as a comprehensive overview of the state-of-the-art techniques and applications of type-2 fuzzy logic systems. The review's application-oriented section looks at a number of different areas where type-2 fuzzy logic has made a big difference. These areas include robotics, autonomous vehicle control, electric and electronic control, image processing, and railway control. This overview mainly discusses the past, present, and future trends in type-2 fuzzy logic applications. The primary contribution summarizes the most essential type-2 fuzzy logic research with theoretical and practical implications. This article will provide a critical path and a strong foundation for individuals interested in this field to pursue future research.

Design and application of interval type-2 fuzzy neural network systems optimized with hybrid algorithms

Interval type-2 fuzzy neural network systems (IT2 FNNSs) own the advantages of IT2 fuzzy logic systems (FLSs) and NN. The paper designs a kind of IT2 FNNSs for permanent magnetic drive (PMD) forecasting. For each rules of IT2 FNNSs, whose antecedents, consequents and input measurements are chosen as Gaussian IT2 membership functions (MFs). The proposed hybrid backpropagation (BP) algorithms and recursive least square (RLS) algorithms are adopted to tune all the parameters simultaneously. Simulation instances on the basis of data of permanent magnetic drive (PMD) torque and revolutions per minute (rpm) are adopted for testing the performances of proposed hybrid optimized IT2 FNNSs for forecasting. Convergence analysis illustrates that the IT2 FNNSs have excellent generalization capability compared with both singleton and non-singleton T1 FNNSs.

Design and application of fuzzy neural network systems optimized with hybrid algorithms

Design and application of fuzzy neural network systems optimized with hybrid algorithms

A noise‐tolerant fuzzy‐type zeroing neural network for robust synchronization of chaotic systems

SummaryAs a significant research issue in control and science field, chaos synchronization has attracted wide attention in recent years. However, it is difficult for traditional control methods to realize synchronization in predefined time and resist external interference effectively. Inspired by the excellent performance of zeroing neural network (ZNN) and the wide application of fuzzy logic system (FLS), a noise‐tolerant fuzzy‐type zeroing neural network (NTFTZNN) with fuzzy time‐varying convergent parameter is proposed for the synchronization of chaotic systems in this paper. Notably the fuzzy parameter generated from FLS combined with traditional convergent parameter embedded into this NTFTZNN can adjust the convergence rate according to the synchronization errors. For the sake of emphasizing the advantages of NTFTZNN model, other three sets of contrast models (FTZNN, VPZNN, and PTZNN) are constructed for the purpose of comparison. Besides, the predefined‐time convergence and noise‐tolerant ability of NTFTZNN model are distinctly demonstrated by detailed theoretical analysis. Furthermore, synchronization simulation experiments including two chaotic systems with different dimensions are provided to verify the related mathematical theories. Finally, the schematic of NTFTZNN model for chaos synchronization is accomplished completely through Simulink, further accentuating its effectiveness and potentials in practical applications.

Editorial for Thematic Section: Theory and Applications of Fuzzy Systems, Neural Networks, and Metaheuristics

Editorial for Thematic Section: Theory and Applications of Fuzzy Systems, Neural Networks, and Metaheuristics

Application of Fuzzy inference systems for preliminary analysis of the technical requirements of permission holders for the repair and maintenance of regulated measuring instruments

Application of Fuzzy inference systems for preliminary analysis of the technical requirements of permission holders for the repair and maintenance of regulated measuring instruments

Optimization-Based Fuzzy System Application on Deformation of Geogrid-Reinforced Soil Structures

When it comes to the geosynthetic-reinforced soil structures’ structural design, the analysis of deformation is of the highest relevance. In spite of this, the academic literature lays a substantial amount of attention on the potential of artificial intelligence approaches in efficiently addressing the many challenges that are faced in geotechnical engineering. The investigation of the possible use of approaches based on machine learning for the purpose of forecasting the geogrid-reinforced soil structures’ deformation (Dis\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{Dis}}$$\\end{document}) was the major focus of this study. These efforts are made to reduce the time and cost of numerical modeling. This study aimed to enable computers to learn patterns and insights from data to make accurate predictions or decisions about unseen data. This paper introduces novel systems that coupled the Beluga whale optimizer (BWH\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{BWH}}$$\\end{document}), Henry gas solubility optimization (HGSO\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{HGSO}}$$\\end{document}), gannet optimization algorithm (GOA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{GOA}}$$\\end{document}), and Harris hawks optimizer (HHO\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{HHO}}$$\\end{document}) with adaptive neuro-fuzzy inference system (ANFIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ANFIS}}$$\\end{document}). A dataset was created by gathering 166 finite element analyses accomplished in the literature. Between four ANFIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ANFIS}}$$\\end{document} systems, the integrated one with GOA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{GOA}}$$\\end{document} got the largest accuracy value, accounting for 0.9841 and 0.9895 in the train and test stages, better than ANFBWH\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ANF}}_{{{\ ext{BWH}}}}$$\\end{document}, followed by ANFHH\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ANF}}_{{{\ ext{HH}}}}$$\\end{document}. It was seen from U95\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{U}}_{{{95}}} { }$$\\end{document} that the ANFGOA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ANF}}_{{{\ ext{GOA}}}} { }$$\\end{document} scenario exhibits the least level of uncertainty in comparison to other models, hence demonstrating its greater capacity for generalization. Between four ANFIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ANFIS}}$$\\end{document} systems, the most accurate system with the lowest OBJ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{OBJ}}$$\\end{document} value is ANFGOA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ANF}}_{{{\ ext{GOA}}}}$$\\end{document} at 2.6098, followed by ANFBWH\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ANF}}_{{{\ ext{BWH}}}}$$\\end{document} at 2.8002. Sensitivity analysis depicts that removing the surcharge q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left( q \\right)$$\\end{document} parameter from the input group has a considerable negative impact on output accuracy.

Research on the influence of solar radiation fuzzy adaptive system on the wet and hot environment in greenhouse

Chinese solar greenhouse (CSG) is an artificially constructed agricultural facility that provides a suitable environment for crop growth. However, the imbalanced temperature and humidity phenomenon occurring in greenhouse during summer greatly affects crop yield. This article proposes the application of solar radiation fuzzy adaptive system in greenhouse to address this issue. The design process includes hardware and algorithm design. The hardware part involves designing the travel distance of the curtain machine and the film winding machine, laying out the temperature and humidity sensors, and designing the control cabinet. The algorithm is optimized based on fuzzy control theory, data acquisition module consists of temperature and humidity sensors, executive components include the curtain machine and the film winding machine, and the data transmission, storage and feedback module consists of the control cabinet and the cloud-based database. These form an adaptive system, which will be subjected to a 10-day comparative experiment. The experimental results showed that in region 3, the peak value of temperature reduction in the third layer during the daytime was 26.2 °C, and the humidity increased by a maximum of 31.5 %. In region 2, the temperature increased by a maximum of 4.2 °C during the nighttime in the second layer, and the humidity decreased by 4.1 °C. Introduction of adaptive systems can play an important role in maintaining microclimate temperature and humidity in summer.

Fault detection in analog electronic circuits using fuzzy inference systems and particle swarm optimization

Fault detection in analog circuits is of great importance to predict the correct operation of the circuit. For this purpose, soft computing techniques such as those based on the application of fuzzy inference systems stand out. However, given the large variability that can exist in analog circuits due to component tolerance, the initial fuzzy inference system (FIS) may not be able to accurately diagnose the different hard faults. This study presents a methodology to diagnose and detect the faults that can occur in analog circuits, which is based on the development of a FIS, starting from a specific fault situation in the analog circuit, and subsequently on the optimization of the membership functions using an evolutionary algorithm so that the adjusted FIS can classify and predict different failure situations. To this end, the application of optimization techniques based on particle swarm optimization (PSO) will be analyzed to develop a FIS capable of predicting different faults. In addition, pattern search algorithm will also be analyzed. A Sallen-Key band-pass filter and a single stage of a small-signal amplifier are used as test circuits. The proposed methodology shows that it is possible to accurately predict the faults that could arise in the circuits under study.

Max-min fuzzy bi-level programming: resource sharing system with application

This study delves into the application of min-max fuzzy relation systems, specifically focusing on equality and inequality, in the examination of educational institution networks and their resource-sharing dynamics, akin to a P2P network structure. Expanding upon this framework, this article intricately explores a mathematical system featuring three or more terminals within an education resource system, each comprising distinct sharing capabilities. The terminals exchange information on various educational facets, encompassing variations and expenditures per share data, within the framework of a programming-type fuzzy objective function, constrained by the parameters of an educational information system. In these scenarios, the transfer of resources from one terminal to another is interlinked, introducing complexities that warrant a comprehensive examination. Notably, the consideration of greater downloading resources in specific cases is proposed for enhanced practicality. The primary objective of this article is to minimize network congestion within different educational networks, given fixed priority grades assigned to the terminals. To achieve this, the concept of a Lexicographic minimum solution is introduced to address max-min fuzzy relation inequalities, aligning with the defined objectives. The article presents a detailed and effective scenario for implementing the Lexicographic minimal solution. For validation purposes, the proposed scenario is supported by illustrative examples, offering tangible insights into its applicability and efficacy.

The problems and countermeasures of private education management based on fuzzy logic system

At present, private education plays an important role in China’s education system, but there are a series of problems and challenges in the management of private education. The purpose of this study is to discuss and solve the problems existing in the management of private education through the method of fuzzy logic system. Firstly, the application of fuzzy logic systems in related fields is reviewed to understand the potential and advantages of fuzzy logic systems. Then, through the analysis of the main problems in the management of private education, the root of these problems and their impact on the education system are revealed. By establishing a private education management model based on fuzzy logic system, the application and function of this model in problem analysis and decision making are demonstrated. In the analysis of the results, the accuracy and effectiveness of the fuzzy logic system in the management of private education are evaluated, and the targeted management strategies and countermeasures are put forward to solve the problems in the management of private education.

Self-Healing Grids: AI Techniques for Automatic Restoration after Outages

The reliability and resilience of power grids are paramount for sustaining modern society's energy demands. However, power outages resulting from natural disasters, equipment failures, or human errors remain persistent challenges. Traditional approaches to power grid restoration, relying heavily on manual intervention, often lead to delays and inefficiencies in restoring services. [1],[2] Recent advancements in artificial intelligence (AI) have spurred the development of self-healing grids capable of autonomously detecting, diagnosing, and restoring power after outages. This paper presents a comprehensive overview of AI techniques employed in self-healing grids and their applications in automatic restoration following outages. The traditional methods of power grid restoration, characterized by manual inspection and decision-making processes, are discussed, highlighting their limitations and challenges. Subsequently, the paper delves into various AI techniques employed in self-healing grids. Machine learning algorithms, such as supervised and unsupervised learning, are utilized for outage detection by analyzing historical data to identify patterns indicative of faults or anomalies. Fault diagnosis is facilitated through the application of Bayesian networks, neural networks, and fuzzy logic systems, enabling operators to accurately identify the root cause of outages and prioritize restoration efforts. Optimization algorithms, including evolutionary algorithms and reinforcement learning, play a crucial role in planning and coordinating restoration efforts to minimize downtime and maximize efficiency. The benefits of self-healing grids, including improved reliability, reduced downtime, and enhanced safety, are discussed alongside the challenges posed by data quality, scalability, and cybersecurity concerns. Finally, the paper outlines future directions, emphasizing advancements in AI techniques, integration with emerging technologies, and the importance of standardization and regulatory frameworks for the future of power grid management. DOI: https://doi.org/10.52783/pst.302

APPLICATION OF ARTIFICIAL NEURON NETWORKS AND FUZZY LOGIC IN DIAGNOSTIC AND FORECASTING THE TECHNICAL CONDITION OF TRACTION MOTORS

The article is devoted to the application of artificial intelligence in diagnosing and predicting the technical condition of electric motors. Currently, a number of traditional and modern methods are used to perform diagnostic monitoring of electric motors, and research is being conducted in this field. The application of traditional diagnostic monitoring systems in the diagnosis of motors faces problems in determining the normal and threshold values of the diagnostic parameters that cannot be measured in the working condition due to the lack of uncertain information. Various traditional methods are applied to partially overcome these problems and increase the effectiveness of diagnostic control in working conditions. The development of computer technology and its application in technology paved the way for the creation of more modern diagnostic monitoring systems. Modern diagnostic monitoring methods based on Soft Computing play an important role in optimizing the working condition of motors and increasing their stability. As a result of the research, it was found that the application of artificial neural networks and fuzzy logic-based diagnostic systems, which are pioneers of these methods, together with traditional methods in monitoring the technical condition of motors, will lead to the creation of new hybrid and complex systems. Keywords: Traction motor, fuzzy logic, neural networks, diagnostic monitoring.

Takagi-Sugeno-Kang fuzzy system fusion: A survey at hierarchical, wide and stacked levels

With excellent global approximation performance and interpretability, Takagi-Sugeno-Kang (TSK) fuzzy systems have enjoyed a wide range of applications in various fields, such as smart control, medical, and finance. However, in handling high-dimensional complex data, the performance and interpretability of a single TSK fuzzy system are easily degraded by rule explosion due to the curse of dimensionality. Ensemble learning comes into play to deal with the problem by the fusion of multiple TSK fuzzy systems using appropriate ensemble learning strategies, which has shown to be effective in eliminating the issue of the curse of dimensionality curse problem and reducing the number of fuzzy rules, thereby maintaining the interpretability of fuzzy systems. To this end, this paper gives a comprehensive survey of TSK fuzzy system fusion to provide insights into further research development. First, we briefly review the fundamental concepts related to TSK fuzzy systems, including fuzzy rule structures, training methods, and interpretability, and discuss the three different development directions of TSK fuzzy systems. Next, along the direction of TSK fuzzy system fusion, we investigate in detail the current ensemble strategies for fusion at hierarchical, wide and stacked levels, and discuss their differences, merits and weaknesses from the aspects of time complexity, interpretability (model complexity) and classification performance. We then present some applications of TSK fuzzy systems in real-world scenarios. Finally, the challenges and future directions of TSK fuzzy system fusion are discussed to foster prospective research.

Mobbing among seafarers: Scale development and application of an interval type-2 fuzzy logic system

Mobbing is a universal problem in every industry, but it is a particularly serious problem for seafarers who are tied to their work 24 h a day, 7 days a week. In this study, it was first aimed to develop a mobbing scale for seafarers from Turkey as one of the countries that provide the most seafarers to the international maritime sector and to examine their mobbing behaviors. A total of 518 people participated in the development of the scale, while 211 participated in a validation study. Factors were determined using exploratory factor analysis and then the validity of the developed scale was verified utilizing confirmatory factor analysis. Secondly, an interval type-2 fuzzy logic system was designed based on the developed scale to deal with the imprecision and vagueness inherent to the answers to the questions. To our knowledge, this is the first study to employ interval type-2 fuzzy sets to measure mobbing behavior. The results of the study demonstrate that there are inverse relationships between the age, professional experience, and position of seafarers and the severity of the mobbing to which they are exposed. The study recommends that the awareness of mobbing be promoted among seafarers.

Enhancing smart grids with a new IOT and cloud-based smart meter to predict the energy consumption with time series

More energy-consuming devices such as household electronics and more production facilities worldwide are causing increases in electricity demand and energy prices. The public service has difficulty maintaining the energy balance. These problems can be overcome by rapidly transforming the traditional electricity grid into a smart grid (SG) infrastructure. Smart meters (SMs) are an essential component of SGs and have vital tasks. This study has developed an Internet of Things (IoT) based SM that can reach high data rate of 38,400 bps or frequency of 160 MHz, using SQL Server for data storage and bidirectional data transmission with 174 W total load. In this way, consumers can track their energy consumption hourly, daily, and monthly, learn how much they spent on consumption, and receive warnings for a power outage. A fuzzy system and mobile application software are integrated into the SM structure for all these purposes. The designed device is believed to contribute significantly to the spread of SGs due to all these features.

Rolling bearing fault diagnosis under variable working conditions using deep convolutional fuzzy system

This paper addresses the application of a deep convolutional fuzzy system (DCFS) for the fault diagnosis of rolling element bearings. The limitations of the conventional deep convolutional neural network (CNN) are the huge computational load of training the tones of parameters and the lack of interpretability for the corresponding parameters. In this paper, a DCFS-based bearing fault diagnosis method under variable working conditions is proposed. The DCFS on a high dimensional input space is a multilayer connection of many low dimensional fuzzy systems, which can overcome the computational and interpretability problems of the traditional CNN. Moreover, to improve the identification efficiency and diagnosis accuracy, the infinite feature selection (Inf-FS) algorithm is employed to select the most informative fault features. The proposed approach is experimentally demonstrated to be able to identify the different fault types and fault severities of rolling bearings under variable running states.