[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-618664-105":3,"detail-sidebar-cat-0-en-105":79,"doc-detail-618664-en":129},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":72,"head_meta":74,"extra_data":76,"updated_unix":78},105,"en","hierarchical-fuzzy-logic-systems-original-contribution-introductory-approach","Hierarchical Fuzzy Logic Systems - Original Contribution - Introductory approach","","Fuzzy logic has generated sustained research interest across many fields, yet its rule base for real-life applications has grown rapidly and becomes difficult to manage as dataset sizes expand. Hierarchical fuzzy logic addresses this complexity by decomposing a large system into multiple small fuzzy subsystems arranged in a hierarchical structure. This design perspective reduces rule explosion and supports advanced applications including IoT, intelligent systems, cyber security, and WSNs.",{"@graph":14,"@context":71},[15,34,54],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/hierarchical-fuzzy-logic-systems-original-contribution-introductory-approach/618664/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":48,"encodingFormat":47,"isAccessibleForFree":49,"interactionStatistic":50},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/hierarchical-fuzzy-logic-systems-original-contribution-introductory-approach/618664.png","ImageObject",300,407,{"name":42,"@type":43},"CatatanPagi","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-19",true,{"@type":51,"interactionType":52,"userInteractionCount":4},"InteractionCounter",{"@type":53},"ViewAction",{"@type":55,"mainEntity":56},"FAQPage",[57,63,67],{"name":58,"@type":59,"acceptedAnswer":60},"Why does the paper introduce hierarchical fuzzy logic?","Question",{"text":61,"@type":62},"Because the number of fuzzy-system rules in real-life applications increases exponentially as data grows, making conventional fuzzy systems too complex to manage.","Answer",{"name":64,"@type":59,"acceptedAnswer":65},"What is the hierarchical fuzzy logic design approach described?",{"text":66,"@type":62},"It designs a system using several small fuzzy subsystems organized in a hierarchical structure, enabling large rule sets to be handled more manageably.",{"name":68,"@type":59,"acceptedAnswer":69},"How do neuro-fuzzy systems relate to fuzzy logic?",{"text":70,"@type":62},"Neuro-fuzzy systems combine fuzzy logic with neural networks, using IF–THEN fuzzy rule bases for interpretability and neural components to improve accuracy and handle larger data dimensions.","https://schema.org",{"og:url":32,"og:type":73,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":75,"canonical":32},"index,follow",{"doc_id":77,"site_id":7},618664,1792449744,{"code":4,"msg":80,"data":81},"success",[82,86,90,94,99,104,109,113,118,121,125],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":83,"show_sort_weight":84,"slug":85},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":87,"show_sort_weight":88,"slug":89},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":91,"show_sort_weight":92,"slug":93},"Exam",70,"exam",{"id":95,"doc_module":4,"doc_module_name":25,"category_name":96,"show_sort_weight":97,"slug":98},5,"Comic",60,"comic",{"id":100,"doc_module":4,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},6,"Technology",50,"technology",{"id":105,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},7,"Healthcare",40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":111,"slug":112},8,30,"research-report",{"id":114,"doc_module":4,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},9,"Religion & Spirituality",20,"religion-spirituality",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":119,"show_sort_weight":116,"slug":120},"World Cup","world-cup",{"id":122,"doc_module":4,"doc_module_name":25,"category_name":123,"show_sort_weight":122,"slug":124},10,"Lifestyle","lifestyle",{"id":126,"doc_module":4,"doc_module_name":25,"category_name":127,"show_sort_weight":95,"slug":128},19,"General","general",{"code":4,"msg":80,"data":130},{"doc_id":77,"user_id":131,"nickname":42,"user_avatar":132,"doc_module":4,"category_id":110,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":114,"language":138,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":12,"update_tm":78,"read_time":142},962090894170,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","J. Inst. Eng. India Ser. B (August 2022) 103(4):1167–1175 [https://doi.org/10.1007/s40031-022-00728-4](https://doi.org/10.1007/s40031-022-00728-4)  \nORIGINAL CONTRIBUTION  \nHierarchical Fuzzy Logic Systems  \nShashank Kamthan1 • Harpreet Singh1  \nReceived: 22 January 2021/Accepted: 7 February 2022/Published online: 14 March 2022 􀀂 The Institution of Engineers (India) 2022  \nAbstract The concept of fuzzy logic has created an immense interest for various research workers in the different fields. Various offshoots of fuzzy logic appeared in the literature during the last four decades or so. As the data involved for several applications has grown considerably, the number of rules of fuzzy systems for real-life applications has increased exponentially and is unmanageable. To reduce the complexity of a fuzzy system, hierarchical fuzzy logic emerged as one of the most viable options. This paper gives an introductory approach to design a system that includes various small dimension fuzzy subsystems, where all subsystems are arranged in a hierarchical structure. This approach handles large numbers of rules and paves a way to design advanced big data applications such as IoT, Intelligent systems, cyber security and WSNs.  \nKeywords Fuzzy logic 􀀂 Hierarchical systems 􀀂 Neuro-fuzzy systems 􀀂 Fuzzy hierarchical model 􀀂 Hierarchical logic  \nIntroduction  \nIn 1965, the term ‘fuzzy logic’ was introduced by Lotfi Zadeh in one of his papers [1] on fuzzy sets. Fuzzy logic provides an adequate route for conflict resolution and making real assessments. Fuzzy logic has the capability to deal with imprecise, uncertain and vague information.  \n& Shashank Kamthan [shashank.kamthan@wayne.edu](shashank.kamthan@wayne.edu)  \n1 Department of Electrical and Computer Engineering, Wayne State University College of Engineering, Farmington Hills, Detroit, MI, USA  \nFuzzy logic comprises linguistic variables that support the design of mathematical and realistic models. The fuzzy logic facilitates a huge number of real-life applications. In this paper, a concise review of the hierarchical fuzzy logic has been presented. This will pave a way for researchers to gain the necessary foundations keeping in view the explosion of large datasets in various fields.  \nA. Fuzzy systems.  \nThe fuzzy logic [1, 2] has been derived from the conventional logic, i.e., the fuzzy set theory. The fuzzy logic consolidates the smooth transformation between false and true. Instead of presenting the output as extreme ‘0’ or ‘1,’the output results in the form of degree of truth that includes [0, 1] . The fuzzy logic follows the notion of partial truth. In fuzzy logic, the value fluctuates between ‘0’ and‘1.’ Fuzzy logic [2, 3] constitutes the set theory in which set A in universe U is categorized by various elements defined by ‘x’ that appear as real values for each element inuniverse U. Equation (1a) represents the membership relation for set ‘A,’ where l(x) is the membership function for elements ‘x’ in set ‘A.’  \nA ¼ fx; lðxÞjx 2 Ug ð1aÞ  \nFor more than one fuzzy set, the binary mapping is used in general to aggregate the membership function of all the sets. Consider two fuzzy sets as ‘A’ and ‘B,’ then the binary mapping can be shown in Eq. (1b) as:  \nlðA; BÞ ¼ TflA ðxÞ; lB ðxÞg ð1bÞ  \nwhere lA(x) and lB(x) are the membership functions for elements ‘x’ in the set ‘A’ and the set ‘B,’ respectively.  \nInterpretability is the main constituent of a fuzzy system that characterizes linguistic approach and human interpretation. Not all the conventional systems derived from  \nsample data can comprehend human language. Mamdani and Takagi–Sugeno–Kang proposed a model [4] to boost the objectives of interpretability and accuracy. The Mamdani model defines a linguistic fuzzy modeling concept to enhance the interpretability, whereas the Takagi–Sugeno– Kang model [4] focuses on precision modeling to increase the accuracy. The fuzzy logic consists of a nonlinear behavior and presents a white-box system that f","cbCaigTZdw4qNhKP","https://ap.wps.com/l/cbCaigTZdw4qNhKP","pdf",1525439,"English","# Abstract\n# Introduction\n## Fuzzy systems\n## Real-life examples of fuzzy systems\n## Neuro-fuzzy systems\n## Problem statement","[{\"question\":\"Why does the paper introduce hierarchical fuzzy logic?\",\"answer\":\"Because the number of fuzzy-system rules in real-life applications increases exponentially as data grows, making conventional fuzzy systems too complex to manage.\"},{\"question\":\"What is the hierarchical fuzzy logic design approach described?\",\"answer\":\"It designs a system using several small fuzzy subsystems organized in a hierarchical structure, enabling large rule sets to be handled more manageably.\"},{\"question\":\"How do neuro-fuzzy systems relate to fuzzy logic?\",\"answer\":\"Neuro-fuzzy systems combine fuzzy logic with neural networks, using IF–THEN fuzzy rule bases for interpretability and neural components to improve accuracy and handle larger data dimensions.\"}]","Hierarchical Fuzzy Logic Systems - Original Contribution - Introductory approach | PDF",23]