An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

Fayaz, Muhammad; Ullah, Israr; Kim, Do‐Hyeun

doi:10.3390/electronics8020132

Cited by 17 publications

(16 citation statements)

References 28 publications

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“…In the proposed CHFIS model we used the triangular MFs [25]. There is no standard way to determine MFs; hence we also proposed a heuristic based membership function determination (HBMFD) method.…”

Section: Proposed Water Supply Pipeline Risk Index Methodologymentioning

confidence: 99%

Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System

et al. 2019

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As populations grow, facilities such as roads, bridges, railways lines, commercial and residential buildings, etc., must be expanded and maintained. There are extensive networks of underground facilities to fulfil the demand, such as water supply pipelines, sewage pipelines, metro structures, etc. Hence, a method to regularly assesses the risk of the underground facility failures is needed to decrease the chance of accidental loss of service or accidents that endanger people and facilities. In the proposed work, a cohesive hierarchical fuzzy inference system (CHFIS) was developed. A novel method is proposed for membership function (MF) determination called the heuristic based membership functions determination (HBMFD) method to determine an appropriate MF set for each fuzzy logic method in CHFIS. The proposed model was developed to decrease the number of rules for the full structure fuzzy inference system with all rule implementation. Four very crucial parameters were considered in the proposed work that are inputs to the proposed CHFIS model in order to calculate the risk of water supply pipelines. In order to fully implement the proposed CHFIS just 85 rules are needed while using the traditional Mamdani fuzzy inference system, 900 rules are required. The novel method greatly reduces implementation time and rule design sets that are extremely time consuming to develop and difficult to maintain.Processes 2019, 7, 182 2 of 15 different methods in order to assess water supply pipelines. An efficient risk assessment methodology is fundamental to take measures in time to escape from accidents.Recently, the fuzzy logic (FL) method has grasped the consideration of various scholars and has been widely used in several areas for different purposes [9,10]. Fuzzy logic methods have been extensively used for risk index analysis and assessment. Fuzzy inference system (FIS) can be used to solve the problems related to the exact mathematical models. However, conventional fuzzy inference systems are not suitable due to its rules-explosion with every new entry of variables. For a fuzzy model having q input parameters, for each input parameters p MFs are defined. Then, for a full fuzzy inference system q p fuzzy rules are required, such as in [11,12] a fuzzy inference system has been designed where there are 12 input variables and for each variable five MFs are allocated. Hence, the entire number of rules obligatory to completely implement the system are 5 12 . It is particularly difficult for an expert to incorporate that large number of rules with attention. Any abnormality in rule designing can cause casualties of people, wastage of money or both losses. Hence, the minimization of rules in rule-base is an issue of high concern. To overcome the issue of rule-explosion, a solution is to divide the fuzzy inference system in sub-modules in a hierarchical form. In this hierarchical fuzzy logic method, the low-level modules provide fractional solutions; these fractional solutions are then combined in the high-level modules to provide...

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Section: Proposed Water Supply Pipeline Risk Index Methodologymentioning

confidence: 99%

Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System

et al. 2019

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“…Among these, the simplest and frequently used are triangular types of MFs. Therefore in the proposed work, we have used the triangular MFs [50]. Equation 1represents the formulas for triangle MFs.…”

Section: Proposed Methodologymentioning

confidence: 99%

A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference

et al. 2019

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The standard manufacturing organizations follow certain rules. The highest ubiquitous organizing principles in infrastructure design are modular idea and symmetry, both of which are of the utmost importance. Symmetry is a substantial principle in the manufacturing industry. Symmetrical procedures act as the structural apparatus for manufacturing design. The rapid growth of population needs outstrip infrastructure such as roads, bridges, railway lines, commercial, residential buildings, etc. Numerous underground facilities are also installed to fulfill different requirements of the people. In these facilities one of the most important facility is water supply pipelines. Therefore, it is essential to regularly analyze the water supply pipelines’ risk index in order to escape from economic and human losses. In this paper, we proposed a simplified hierarchical fuzzy logic (SHFL) model to reduce the set of rules. To this end, we have considered four essential factors of water supply pipelines as input to the proposed SHFL model that are: leakage, depth, length and age. Different numbers of membership functions are defined for each factor according to its distribution. The proposed SHFL model takes only 95 rules as compared to the traditional mamdani fuzzy logic method that requires 1225 rules. It is very hard and time consuming for experts to design 1225 rules accurately and precisely. Further, we proposed a Do-it-Yourself (DIY) system for the proposed SHFL method. The purpose of the DIY system is that one can design the FIS model according to his or her need.

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“…Taking in account [30,31] fuzzy logic allows the modeling of a system using fuzzy sets and rules that describe the system behavior. Fuzzy systems permit to model and control non-linear processes; after designing a fuzzy system (based on knowledge), optimization algorithms can be used to achieve better performance [32,33]. Moreover, relations among variables in fuzzy systems are presented using rules as:…”

Section: Fuzzy Systemsmentioning

confidence: 99%

Design Methodology for the Implementation of Fuzzy Inference Systems Based on Boolean Relations

et al. 2019

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This paper proposes a methodology for the design of fuzzy inference systems based on Boolean relations. The approach using Boolean sets presents limited performance due to the abrupt transitions that occur during its functioning, therefore, fuzzy sets can be used aiming the improvement of the performance. In this approach, firstly, the design of a Boolean controller is performed, which is later extended into fuzzy under design guidelines proposed in this paper. The methodology uses Kleene algebra via truth tables for the fuzzy system design, allowing the simplification of the equations that implement the fuzzy system.

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An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

Cited by 17 publications

References 28 publications

Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System

Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System

A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference

Design Methodology for the Implementation of Fuzzy Inference Systems Based on Boolean Relations

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