Defuzzification techniques for fuzzy controllers

Saade, Jean J.; Diab, Hassan

doi:10.1109/3477.826965

Cited by 53 publications

(27 citation statements)

References 13 publications

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“…The process of transforming evaluated fuzzy set into crisp output is known as defuzzification and is computed by various conventional proficiency methods like Mean of Maximum, the centroid of the area, weighted average, etc. Since our aim is to reduce computational cost, we opted to apply MoM [17] technique for defuzzification process. The fundamental principle aspects of fuzzy logic are referred from [18,19].…”

Section: Defuzzificationmentioning

confidence: 99%

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Fuzzy-based fault-tolerant and instant synchronization routing technique in wireless sensor network for rapid transit system

Raghunath¹,

Thirukumaran²

2019

Automatika

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In the present era, rapid transits are one of the most affordable means of public transport with various useful integrated application systems. The majority of the integrated applications are deployed in concern over safety and precautionary measures against the worst side-effects of unfortunate emergencies. For such cases, high-end reliable and autonomous systems provide possible positive solutions. Wireless Sensor Network is one of the suitable choices for rapid transit applications to gain positive results with inexpensive implementation cost. However, managing few network consequences like fault tolerance, energy balancing and routing critical informative packets are considered to be the challenging task due to their limited resource usage restriction. In this paper, a novel fuzzy logic-based fault tolerance and instant synchronized routing technique have been proposed specifically for the rapid transit system. On utilizing the fuzzy logic concepts, most of the computational complexities and uncertainties of the system is reduced. The central thematic of the proposed design is concerned over the synchronized routing and permanent faults which abruptly depicts the non-functional nature of the sensor nodes during normal operations. Moreover, our proposed simulation outcomes proved to be improvised evidence on obtaining maximum packet delivery ratio which tends to handle an emergency situation in the compartments of rapid transits. ARTICLE HISTORY

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Section: Defuzzificationmentioning

confidence: 99%

“…The fundamental principle aspects of fuzzy logic are referred from [18,19]. A simple and logical explanation for MoM [17] has been depicted diagrammatically in Figure 3, where U j denotes the control action of membership function which attains maximum and k represents the number of control action that taken part.…”

Section: Defuzzificationmentioning

confidence: 99%

Fuzzy-based fault-tolerant and instant synchronization routing technique in wireless sensor network for rapid transit system

Raghunath¹,

Thirukumaran²

2019

Automatika

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“…It is a type of logic that recognizes more than simple true and false values. With fuzzy logic, propositions can be represented with degrees of truthfulness and falsehood in [0,1]. This allows us to perform fuzzy reasoning.…”

Section: Fuzzy Logic and Fuzzy Reasoningmentioning

confidence: 99%

“…Other methods such as WFM, FM and WA act on each fuzzy set obtained from each rule using implication operator on consequent sets and then aggregate the results to make the final crisp output of defuzzification procedure. However, there isn't any certain rule for selecting the defuzzification strategy, choosing the most appropriate technique of defuzzification depends on the properties of the application or problem, but because of some disadvantages of common defuzzification methods [1,2] we were looking for new techniques of defuzzification. In this approach two programmable methods derived from the general principle of weighting were developed.…”

Section: Introductionmentioning

confidence: 99%

Proposing Two Defuzzification Methods based on Output Fuzzy Set Weights

Amini¹,

Nikraz²

2016

IJISA

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Abstract-Defuzzification converts the final fuzzy output set of fuzzy controller and fuzzy inference systems to a significant crisp value. However, there are various mathematical methods for defuzzification, but there is not any certain systematic method for choosing the best strategy. In this paper, first we explain the structure of a fuzzy inference system and then after a short review of defuzzification criteria and properties, the main classification groups of most widely used defuzzification methods are presented. In the following after discussing some existing techniques, two new defuzzification methods are proposed by presenting their general performance and computational formulas. However, the principle of these two methods is using weights associated with output fuzzy set like WFM or QM, but unlike the existing approaches, they consider the final aggregated consequent and implicated functions simultaneously to calculate the weights. To show how the proposed methods act, two numerical examples are solved using the presented methods and the results are compared with some of common defuzzification techniques.

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“…To overcome this obstacle, some existingcdefuzzificationmethods whicharereferenced inthis simplifications have been presented in [3]. Also a wide range paper include True Center of Gravity (COG) [3], [7], Center of other defuzzification methods are proposed with different of Sum (COS) [ …”

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confidence: 99%

Hardware Implementation and Comparison of New Defuzzification Techniques in Fuzzy Processors

Mahdiani

Banaiyan

Fakhraie

2006 IEEE International Symposium on Circuits and Systems

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This paper deals with hardware implementation defuzzification method according to the nature of the aspects of the defuzzification block in fuzzy controllers and application [8] and also the trade-off between accuracy processors. Three new defuzzification methods are introduced (defuzzified output error) and computational power demand which are suitable for low cost hardware implementation. A (hardware complexity or software execution time). For complete set of common existing defuzzification methods are software implementation, there are some reports which reviewed to be compared with these new methods from determine the trade-off between output error and execution different hardware implementation aspects. Two different termin e deffication method executior hardware models with different structures are developed for time of different defuzzification methods [3], [4]. For each method. The first model realizes the full combinational or hardware implementation of a fuzzy controller however, fastest possible hardware implementation, and the second there are only some scattered works [5] and an integrated model demonstrates the full sequential or the smallest possible comprehensive study cannot be found.hardware implementation of each method. All models are This paper is organized as follows. In Section II, the synthesized on 0.18 micron CMOS technology cells to analyze paper terminology about naming of the existing and compare the area, delay and power consumption of defuzzification method is clarified and also a brief different realizations of defuzzification methods. Some areadescription of our three new methods is presented. Section power-delay-accuracy analysis diagrams are then introduced l l according to the synthesis results to guide the designers dII demonstrates the simulation results, and includes through choosing the defuzzification method and also the discussions about the defuzzified output accuracy analysis of implementation structure which best suites their application different defuzzification methods. The hardware models for from implementation cost, speed, power consumption and these defuzzification methods are then presented in Section output accuracy points of view.IV. Section V contains the area-delay-power synthesis results of the developed models. Section VI includes our conclusion

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Defuzzification techniques for fuzzy controllers

Cited by 53 publications

References 13 publications

Fuzzy-based fault-tolerant and instant synchronization routing technique in wireless sensor network for rapid transit system

Fuzzy-based fault-tolerant and instant synchronization routing technique in wireless sensor network for rapid transit system

Proposing Two Defuzzification Methods based on Output Fuzzy Set Weights

Hardware Implementation and Comparison of New Defuzzification Techniques in Fuzzy Processors

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