Hierarchical Fuzzy Research Articles

Feature Extraction Using Refined Composite Hierarchical Fuzzy Dispersion Entropy and its Applications to Bearing Fault Diagnosis

Feature Extraction Using Refined Composite Hierarchical Fuzzy Dispersion Entropy and its Applications to Bearing Fault Diagnosis

Human–Object Interaction: Development of a Usability Index for Product Design Using a Hierarchical Fuzzy Axiomatic Design

Consumer product usability has been addressed using tools that evaluate objects to improve user interaction. However, such diversity in approach makes it challenging to select a method for the type of product being assessed. This article compiles the concepts used since the origin of usability in product design. It groups them by attributes to formulate a usability index proposal. Due to the nature of the data, fuzzy, hierarchical, and axiomatic tools were applied to a trial group of experts and users. Three questionnaires were designed and administered throughout a five-stage process to collect and select attributes, rank them in importance, assign fuzzy values, obtain their numerical representation of use, and assign a qualitative category. By analyzing a case study, this research demonstrates the value of the index by comparing the use of computer mice. Unlike other approaches to evaluating usability, the proposed index incorporates the hierarchical importance of attributes. It allows for participants to express their opinions, transforming subjective responses into linguistic values represented in triangular areas, resulting in a more accurate representation of reality. Additionally, the complexity of the human–object interaction is treated by an information axiom to compute the usability index on a scale from 0 to 1, which reflects the probability of the product meeting the desired usability attributes.

Fuzzy-Logic Based Model for VANET Performance Metrics Analysis

Vehicular Ad-hoc Networks (VANETs) allows a spontaneous communication between vehicle to vehicles and road-side units(RSU) using wireless interfaces and road-side infrastructure to form Intelligent Transportation System (ITS). VANET, prime objective is to provide information about accidents or unwanted situations and traffic conditions to the drivers. As VANETs are dynamic in nature, the vehicle nodes to discover the best possible route to broadcast an emergency message is a challenge. To study different highway and urban road scenarios, there is a need to simulate and evaluate the network performance analysis of routing protocols beforehand since real-time implementation is rather challenging or expensive. This paper focuses on the VANET routing protocols evaluation, to find an appropriate protocol which provide suitable Quality of Service (QoS) to end users in both safety and non-safety conditions. To simplify the QoS evaluation problem, Hierarchical Fuzzy Inference System (HFIS) model has been designed and simulated in MATLAB R2018b that analysis the network performance metrics of VANET routing protocols. From the simulations, it is observed that that OLSR routing protocol for both safety and non-safety applications, outperforms other routing protocols as its global QoS reaches to approximately 84% efficiency.

Assessing ship collision risks in maritime transport based on seafarers’ experience

Assessing ship collision risks considering human factors presents persistent challenges due to the multivariate uncertainties faced by seafarers. Therefore, this study proposes a fuzzy-logic-based method to model ship collision risks in an area considering human factors. This method utilizes a hierarchical fuzzy system consisting of two fuzzy systems, fuzzy system 1 and fuzzy system 2, which are used to calculate the temporary risks and human factors, respectively. Aggregating the risks from both subsystems produces the overall collision risk index. This approach streamlines on-board risk appraisal, thereby reducing the navigator workload. While the current study delineates the risk modeling methodology, succeeding works validate the framework through simulated experiments. With adequate verification, this hierarchical fuzzy approach can assist next-generation risk-regulated navigation practices and enrich maritime agencies' traffic risk management.

Assessing ship collision risks in maritime transport based on seafarers’ experience

Assessing ship collision risks considering human factors presents persistent challenges due to the multivariate uncertainties faced by seafarers. Therefore, this study proposes a fuzzy-logic-based method to model ship collision risks in an area considering human factors. This method utilizes a hierarchical fuzzy system consisting of two fuzzy systems, fuzzy system 1 and fuzzy system 2, which are used to calculate the temporary risks and human factors, respectively. Aggregating the risks from both subsystems produces the overall collision risk index. This approach streamlines on-board risk appraisal, thereby reducing the navigator workload. While the current study delineates the risk modeling methodology, succeeding works validate the framework through simulated experiments. With adequate verification, this hierarchical fuzzy approach can assist next-generation risk-regulated navigation practices and enrich maritime agencies' traffic risk management.

Quantum Assisted Hierarchical Fuzzy Neural Network for Image Classification

Quantum Assisted Hierarchical Fuzzy Neural Network for Image Classification

Hierarchical Fuzzy Framework for EV Supported Islanded Microgrid Frequency Stabilization

Hierarchical Fuzzy Framework for EV Supported Islanded Microgrid Frequency Stabilization

Dynamic-Event-based Hierarchical Fuzzy Prescribed Performance Control for Underactuated Systems with Uncertain Dead Zone

Dynamic-Event-based Hierarchical Fuzzy Prescribed Performance Control for Underactuated Systems with Uncertain Dead Zone

A Multiantenna Spectrum Sensing Method Based on HFDE‐CNN‐GRU under Non‐Gaussian Noise

In many practical communication environments, traditional feature extraction methods in spectrum sensing fail to fully exploit the information of primary users. Additionally, conventional machine learning methods have weak learning capabilities, making it difficult to maintain efficient and stable spectrum sensing performance in complex noise environments. Furthermore, non‐Gaussian noise can significantly affect the detection performance of spectrum sensing. To address these issues, this paper first proposes a feature extraction method based on Hierarchical Fuzzy Dispersion Entropy (HFDE) to better extract high‐frequency and low‐frequency information from signal samples, providing more comprehensive features for subsequent models to optimize feature extraction effectiveness. Then, a parallel model combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) is constructed to enhance learning ability. While CNN extracts local features, GRU processes temporal relationships, and the features output by both are concatenated to achieve effective feature learning and temporal modeling of primary user signal data represented by HFDE. Finally, using the feature vectors output by the CNN‐GRU model, detection statistics and detection thresholds for spectrum sensing are constructed for online detection. Simulation results validate the effectiveness and robustness of this method in spectrum sensing under non‐Gaussian noise. In the presence of significant non‐Gaussian noise intensity and a signal‐to‐noise ratio of −14 dB, the detection probability can reach 97.1%. Additionally, for the detection of unknown signals, the model can still maintain a detection probability of over 90%.

Construction of Universal Approximators for Multi-Input Single-Output Hierarchical Fuzzy Systems

As an important branch of fuzzy systems, hierarchical fuzzy system (HFS) has a wide range of applications in system science, medical science and engineering. Using the semi-tensor product of matrices, this paper investigates the universal approximation of multi-input single-output HFSs, and develops the construction algorithms of universal approximators. Initially, based on the fuzzy relation matrices of fuzzy logic units with Mamdani-type fuzzy rules, the algebraic formulation of HFSs consisting of fuzzy logic units is proposed. After that, the universal approximation of HFSs is explored via the algebraic formulation, and some effective algorithms are presented to construct the universal approximators of HFSs with respect to different scenarios of approximated functions. Finally, the effectiveness of obtained results is verified by the on-ramp metering of freeway.

Algebraic Formulation and Application of Multi-Input Single-Output Hierarchical Fuzzy Systems With Correction Factors

Hierarchical fuzzy systems (HFSs) are successfully applied to control systems and computer networks. Particularly, correction factor is introduced to regulate fuzzy rules and alleviate the problems of memory usage and real-time performance of systems. This paper considers the multi-input single-output HFSs with correction factors, and proposes an algebraic framework by using the semi-tensor product of matrices. Firstly, a kind of fuzzy correction factor matrix (FCFM) is constructed to realize the fuzzification of correction factor, based on which, an algebraic form is developed for two-input single-output fuzzy systems. Secondly, the structure diagram of HFSs is decomposed into several fuzzy logic units, which have the form of two-input singleoutput fuzzy systems with FCFM. Based on this operation, the algebraic formulation of serial, parallel and hybrid HFSs with correction factors is proposed. Finally, the effectiveness of HFSs with FCFM is verified by studying the on-ramp metering of freeway.

A Fuzzy Autoencoder-Based Self-Organizing Approach to a Hierarchical Fuzzy Logic System

A Fuzzy Autoencoder-Based Self-Organizing Approach to a Hierarchical Fuzzy Logic System

A Self-Organized Method for a Hierarchical Fuzzy Logic System Based on a Fuzzy Autoencoder

In this article, a novel design of a hierarchicalfuzzy system (HFS) based on a self-organized fuzzy partition and fuzzy autoencoder is proposed. The initial rule set of the system is empty, and all the fuzzy sets and fuzzy rules are generated by a self-organized fuzzy partition algorithm. By adopting an improved box plot data standardization method, the processed data can more accurately represent the distribution characteristics of the input data, which improve the accuracy and the rationality. A fuzzy autoencoder is used to train the HFS layer by layer, which can not only ensure the effectiveness of the fuzzy system's hidden layer variables but also provide interpretability. Compared with the traditional fuzzy logic system, the HFS reduces the total number of rules and the complexity. The proposed HFS is tested on three different regression datasets. The experimental results illustrate that the hierarchical self-organized fuzzy system still performs better in terms of regression accuracy indicators than the self-organized fuzzy system.

Layer-specific evaluation-based Hierarchical Fuzzy Failure Mode and Effect Analysis

Reliability and safety have always been the main focus while developing critical automotive systems such as brakes. Failure Modes and Effect Analysis (FMEA) is one of the primary systematic reliability analysis tools. Due to the arising uncertainties and subjectivity, in models such as this one the fuzzy approach is highly popular, i.e., the fuzzy rule-based risk assessment methods can be used to model and depict the subjective opinions of estimators mathematically. In this paper, the authors propose a methodological approach to the implementation of fuzzy rule-based Hierarchical FMEA (H-FMEA), where the membership functions are different depending on the layer. Moreover, the information between each layer is transferred in the form of fuzzy numbers instead of crisp values, in order to further improve the reliability of the system.

Hierarchical fuzzy deep learning for image classification

Considerable interest has been shown over the last several decades for fuzzy logic and its application. The intelligent and deep learning systems are gaining breakthroughs in all walks of life to solve real-life problems for the future. The conventional fuzzy has the constraint to work with limited rule dimensions, whereas deep neural networks are unable to handle uncertain and imprecise data implicitly in the system. The objective of this paper is to develop a generalized algorithm for intelligent systems that can handle uncertainty and imprecise behavior especially for processing of large image datasets. In this paper, the hierarchical fuzzy approach is suggested, as it is gaining attention to tackle large real-life problems. The strategy used is to partition a large image dataset into small data samples and connect all the fuzzy subsystems in a hierarchical manner. In the literature, as far as authors know, no one has developed a hierarchical fuzzy approach to handle a large image dataset of real images. The algorithm for hierarchical fuzzy logic for a large image data using image thresholding has been discussed. To make the assessment, the real image database has been considered. The image classification has attained the potential applications to defense and security especially for target identification and classification. The accuracy and computational time comparisons of hierarchical fuzzy systems with existing methodologies such as deep neural networks have been discussed.

Hierarchical fuzzy‐based Quality of Experience (QoE)‐aware application placement in fog nodes

AbstractFog computing or a fog network is a decentralized network placed in between data source and the cloud to minimize the network latency issues and thus support in‐time service delivery, of Internet of Things (IoT) applications. However, placing computational tasks of IoT applications in fog infrastructure is a challenging task. State of the art focuses on quality of service and quality of experience (QoE) based application placement. In this article, we design hierarchical fuzzy based QoE‐aware application placement strategy for mapping IoT applications with compatible instances in the fog network. The proposed method considers user application expectation parameters and metrics of available fog instances, and assigns the priority of applications using hierarchical fuzzy logic. The method later uses Hungarian maximization assignment algorithm to map applications with compatible instances. The simulation results of the proposed policy show better performance over the existing baseline algorithms in terms of resource gain (RG), processing time reduction ratio (PTRR), and similarly network relaxation ratio. When considering 10 applications in the fog network, our proposed method simulation results show 70.00%, 22.44%, 37.83% improvement in RG, and 28.46%, 37.5%, 23.07% improvement in PTRR, when compared with QoE‐aware, randomized, FIFO algorithms, respectively.

Provide a Lean and Agile Strategy for an Antifragile Sustainable Supply Chain in the Construction Industry(residential complex)

Supply chain management is one of the main approaches in the construction industry to deal with chaotic situations. Sanctions, corruption, Covid-19 pandemic, inflation, and other factors have created chaotic situations for Iranian companies. The main solution for companies is to formulate their supply chain strategies in line with their high-level goals. Lean and agile approaches are competitive strategies that, together with task strategies, can improve the performance of organizations in the medium and long term. In this study, the purpose is to provide the appropriate strategy among lean and agile strategies according to the performance of sustainable supply chain management in Didas Company. Its goal is to maintain and improve the competitive situation, and functional and environmental criteria of the company in order to deal with complexity and chaos when constructing residential complexes. The managers of Didas company who are familiar with the subject are the statistical population in this article. The snowball method was used to select individuals. By studying the articles, the criteria and performance indicators of the construction industry were extracted. Then, the Hierarchical Fuzzy Best-Worst Method (HF-BWM) was used to prioritize. Finally, lean, agile, lean-agile, and agile-lean strategy were ranked by Grey VIKOR method. The results of the first analysis show that economic performance with a weight of 0.4515 is placed in the first priority, operational social performance and environmental performance with a weight of 0.395 and 0.1533 are placed in the second and third priority, respectively. According to the results of the second analysis, the Agile-Lean strategy based on sustainable supply chain leadership strategy with a weight of 0.0219 is placed as the first priority. It is a suitable strategy to improve performance indicators in the supply chain of Didas company. Because this strategy has a high potential that might keep balance and improving the situation in the chaotic and unsustainable situations.

Hierarchical Fuzzy Optimal Controller for Hamilton Energy of a Generalized Chaotic Lorenz System with Hidden Attractors

Hierarchical Fuzzy Optimal Controller for Hamilton Energy of a Generalized Chaotic Lorenz System with Hidden Attractors

Integration of Edge Computing and Blockchain for Provision of Data Fusion and Secure Big Data Analysis for Internet of Things

Intelligent computing provides efficient, real-time, and secure data analysis services for the Internet of Things (IoT). As the number of IoT devices increases, IoT generates massive, diverse, and multisourcing datasets that can be used to improve IoT services further. Models trained by intelligent computing from a single system or sensor are often not global, and sending all data directly to the computing platform wastes network bandwidth and may cause network congestion and even privacy leakage. To ensure IoT applications’ quality of service and privacy, we propose a framework that integrates edge computing and blockchain to provide lightweight data fusion and secure data analysis for IoT. We propose a lightweight data fusion method that can reduce the amount of data at the node level and prevent network congestion and bandwidth waste. Furthermore, we propose a hierarchical fuzzy hashing method to check and locate anomalies of IoT machine learning models to ensure the validity of IoT intelligent computing and the security of sensitive data. Finally, we demonstrate the effectiveness of the method proposed in this paper through experiments.

Fault Diagnosis of Bearing Based on Improved Refined Composite Hierarchical Fuzzy Entropy and Least Squares Support Vector Machine

Fault Diagnosis of Bearing Based on Improved Refined Composite Hierarchical Fuzzy Entropy and Least Squares Support Vector Machine