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Abstract This paper demonstrates to build a FuzzyInference System (FIS) for any model utilizing the FuzzyLogic Toolbox graphical user interface (GUI) tools. Adifferent conception for decision making process, based onthe fuzzy approach, is propounded by authors of the paper.The paper is worked out in two sections. Description aboutthe Fuzzy Logic Tool box is done in the first section.Illustration with an introductory example concludes thesecond section. Based on various assumptions the authors

construct the rule statements which are then converted intofuzzy rules and the GUI tools of the Fuzzy Logic Toolboxbuilt using MATLAB numeric computing environment isused to construct a fuzzy inference system for this process.The output membership functions are expected to be fuzzysets in Mamdani-type inference.Defuzzification of fuzzy setfor each output variable generated after the aggregationprocess has to be carried out. Application of informationtechnology for Decisions in today's environment which ishighly competitive are undeniable principles of organizationsand helps managers in making useful decisions meaningfully.

Keywords – Fuzzy Inference System (FIS), Fuzzy Logic(FL) ,Graphical user interface (GUI)Tools, Mamdani’s fuzzyinference method,MATLAB Programming Language,Decision making.

I. INTRODUCTION

Among various fuzzy methodology Mamdani's fuzzyinference method proposed by Ebrahim Mamdani in 1975is the most commonly seen fuzzy methodology[1] .Amidst the first control systems built using fuzzy settheory Mamdani's method was one and his effort wasbased on fuzzy algorithms for complex systems anddecision processes by Lotfi Zadeh's [2].Fuzzy logic hashighest visibility in the midst of various combinations ofmethodologies in soft computing.Variety of applicationsbased on fuzzy logic has grown rapidly in the past few

years and building a Mamdani system for decision makingprocess is new of this type. This paper heavily relies ongraphical user interface (GUI) of Fuzzy Logic Toolbox toaccomplish the work , which is a collection of functionsbuilt on the MATLAB numeric computing environment.The Fuzzy Logic Toolbox used here can be easilymastered , conveniently used and is highly impressive inall respects providing a reader-friendly approach in wide-ranging applications. The paper starts with a talk of thehypothetical establishment, emulated by a framework forfuzzy rules computing , introduction to the Fuzzy Logic

Toolbox and implementation using GUI tools.Subsequently, examinations of outcomes in light of currenttheory from the use of MATLAB and graphical userinterfaces is carried and implications are provided.

II. FUZZY LOGIC TOOL BOX

The Fuzzy Logic Toolbox is assortment of functionsengineered on MATLAB numeric computingenvironment. It gives instruments for us to make and alterfuzzy frameworks inside the system of MATLAB, or whenwe favor we can combine our fuzzy frameworks intorecreations with Simulink, or we can even raise remainsolitary C projects that approach fuzzy frameworks wefabricate with MATLAB. This tool compartment dependsvigorously on graphical user interface (GUI) tool to helpus achieve our work, despite the fact that we can workaltogether from the command line when we lean towards[3]. Three categories of tools are provided by this toolbox

• Command line functions

• Graphical, interactive tools• Simulink blocks and examples

The first category of tools is made up of capacities that wecan call from the command line or from our own particularprovisions. A considerable lot of these capacities areMATLAB M-files, arrangement of MATLAB articulationsthat actualize particular fuzzy logic algorithms. We can seethe MATLAB code for these capacities utilizing thestatement type function_name

We can change the way any toolbox function capacityworks by duplicating and renaming the M-file, thenchanging our duplicate. We can additionally amplify the

toolbox by including our M-files. Also, the toolbox givesvarious interactive tools that let us gain entrance to a largenumber of the capacities through a GUI. Together, theGUI-based tools furnish an environment for fuzzyframework, outline, dissection, and execution.A set of blocks for utilization with the Simulink simulationsoftware is the third category of tools. In the Simulinkenvironment these are particularly intended for top speedfuzzy logic inference . The Fuzzy Logic Toolbox permitsus to do some things, however the most imperative thing itgives us a chance to do is make and alter fuzzy systems.

[email protected], [email protected], [email protected]

4 Assistant Professor, Sethu Institute of Technology, Kariapatti, Tamil Nadu , India

3 Associate Professor , KLN College of Engineering, Pottapalayam, Sivagangai, Tamil Nadu, India

2

Professor, KLN College of Engineering, Pottapalayam, Sivagangai, Tamil Nadu, India

1 Research Scholar, Anna University Regional Centre, Madurai, Tamil Nadu, India

Ms.T.Ramya 1* , Dr.A.C.Kannan 2 , Mr.R.S.Balasenthil 3 and Ms.B.Anusuya Bagirathi 4

Fuzzy Logic Modeling for decision making processes using MATLAB

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We can make these frameworks utilizing graphical tools orcommand line functions, or we can produce themimmediately utilizing either clustering or adaptive neuro-fuzzy techniques. We can effortlessly test our fuzzyframework in a block diagram simulation environment,provided we have admittance to Simulink.

The toolbox likewise gives us a chance to run our ownparticular stand-alone C projects straightforwardly,without the necessity for Simulink. This is madeconceivable by a stand-alone Fuzzy Inference Engine thatperuses the fuzzy systems saved from a MATLAB session.We can modify the stand-alone engine to incorporatefuzzy inference with our own particular code. All givencode is ANSI agreeable. On account of the incorporatednature of the MATLAB's environment, we can make ourown particular tools to tweak the Fuzzy Logic Toolbox ortackle it with an alternate toolbox , for example, theControl System, Neural Network, or OptimizationToolbox to specify just a couple of the conceivableoutcomes.

Figure 1. Fuzzy inference system.

III.BUILDING MAMDANI SYSTEMS USINGFUZZY LOGIC TOOLBOX GRAPHICAL USER

INTERFACE TOOLS.

To Build, Edit, and View fuzzy inference systems thefollowing graphical tools can be used :

• Fuzzy Inference System (FIS) Editor to handlethe large amount issues for the framework - Thenumber of input and output variables? Theirnames? Fuzzy Logic Toolbox programming doesnot restrain the amount of inputs.Notwithstanding, the amount of inputs may beconstrained by the accessible memory of ourmachine. In the event that the amount of inputs isexcessively vast, or the amount of inputcapacities is too huge, then it might additionallybe troublesome to dissect the FIS utilizing theother GUI tools.

• Membership Function Editor outline theshapes of all the membership functions related toevery variable.

• Rule Editor to edit the list of rules that definesthe behavior of the system

• Rule Viewer to view the fuzzy inference diagram.Use this viewer as a diagnostic to envision, as an

example, that rules are active, or howeverindividual membership operate shapes influencethe results.

• Surface Viewer one of the outputs on any one ortwo of the inputs dependency can be viewed —that is, an output surface map for the system isgenerated and plotted.

These GUIs are rapidly interfaced, in thattransforms we make to the FIS utilizing one ofthem, influence what we see on any of the otheropen GUIs. Case in point, when we change thenames of the membership functions in theMembership Function Editor, the progressionsare reflected in the rules demonstrated in the RuleEditor. We can utilize the GUIs to read andcompose variables both to the MATLABworkspace and to a file (the read-only viewerscan at present trade plots with the workspace andrecovery them to a file). We can have any orevery last one of them open for any givenframework or have numerous editors open for anynumber of FIS systems [4]. The figure 2.Indicates how the principle segments of a FIS andthe three editors fit together. The two viewersinspect the conduct of the whole framework.

Figure 2 . Components of FIS Editors

IV.THE MANAGERIAL PROBLEM

Here, we construct a two-input, one outputsystem( Figure 3 ). The two inputs are Quality ofWorking Life (QoWL) and OrganizationCommitment (OC) . The one output is TurnoverIntention ( TI ). The authors propose statements ,based on assumptions which are converted intofuzzy rules and the GUI tools of the Fuzzy LogicToolbox built using MATLAB numericcomputing environment is used to construct a

fuzzy inference system for this process.

Figure 3 . A graphical example of an input-outputmap

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Statement 1 : If ( QoWL is poor ) or ( OrganizationCommitment is low ) then ( Turnover Intention is High )Statement 2 : If ( QoWL is good ) then ( Turnover

Intention is medium)Statement 3 : If ( QoWL is excellent ) or ( OrganizationCommitment is high ) then ( Turnover Intention is Low )

To build the Fuzzy Inference System described followingcommand at the MATLAB prompt has to be typed: fuzzy

The generic untitled FIS Editor opens, with one inputinput1 , and one output output1 . Addition of a secondinput variable has to follow the following steps :

1. Select Edit then Add variable then Input .input2 appears( second yellow box).

2. Select the yellow box input1 , a red outlinehighlights the box.

3. Change the Name field from input1 to requiredQoWL, and then Enter .

4. Select the yellow box input2 .5. Change the Name field from input2 to required

OC, and then Enter .6. Select the blue box output1 .7. Change the Name field from output1 to Turnover

Intention, and then Enter .8. Select File then Export then To Workspace .9. Enter the Workspace variable name QoWL and

OC on TI, and click OK .(Figure 5).

The graph is overhauled to reflect the new names of theinput and output variables. There is presently anothervariable in the workspace called QoWLandOConTI thatholds all the data about this framework. By saving to theworkspace with new name, we can additionally renamethe whole framework. Now window looks something

like the followingdiagram(figure4)

Figure 4 . FIS Editor ( 2 input )

Figure 5 . Step 8-9

In our case we assume that Given a number between 0 and5 that represents the Quality of Working Life in anorganization (where 5 is excellent), and another numberbetween 0 and 5 that represents the OrganizationCommitment of employees (again, 5 is excellent), what

would be the Turnover Intention of employees if range isbetween 0 and 30. By clicking any of the inputs themembership function editor opens and the range is fixedbetween 0 and 5,the name of the membership function ispoor,good,excellent .The type of the membership functionis Gaussian in QoWL,( Figure 6),OrganizationCommitment and it is Triangular in TurnoverIntention.

Figure 6.Membership Function Plot QoWL(Gaussian)

Figure 7.Membership Function Plot TI ( Triangular )

Right away that the variables have been named and themembership functions have proper shapes and names, theguidelines are entered. The statements that are proposed bythe authors are converted into rules. To ring the RuleEditor, head off to the Edit menu and select Rules. Two

operators to be specific 'OR 'AND" are accessible. Herewe utilize "OR" logic(figure 8 ). The Rule Viewer shows aguide of the entire fuzzy inference process. It is dependentupon the fuzzy inference chart and we can see a solitaryfigure window with 10 plots settled in it (figure 9). Thethree plots over the highest point of the figure speak to thepredecessor and ensuing of the first rule. Each rule is a lineof plots, and each column is a variable. The rule numbersare shown on the left of each row. We can click on a rulenumber to view the rule in the status line.

Figure 8. Rule Editor

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Figure 9. Rule Viewer

The membership functions referenced by the antecedent,or the if-part of each rule is indicated in the first twocolumns of plots (the six yellow plots).The membershipfunctions referenced by the resulting, or the then-part ofeach rule is demonstrated in the third column of plots (thethree blue plots) Notice that under OrganizationCommitment, there is a plot which is spotless. Thiscompares to the characterization of none for the variableOrganization Commitment in the second rule. The fourthplot in the third column of plots speaks to the totalweighted choice for the given inference system. Thischoice will hinge on upon the input values for theframework. The defuzzified output is shown as a strikingvertical line on thisplot.

Figure 10.Output of the process (Surface Viewer)

Upon looking into the Surface Viewer, a three-dimensional curve that represents the mapping fromQoWL and Organization Commitment to TurnoverIntention ( Figure 10) can be seen. A two-input one-outputcase is represented by this curve, the entire mapping canbe obtained in one plot. Accordingly, provisions withdrop-down menus X (input): Y (input): and Z (output): is equipped in the Surface Viewer for selecting any twoinputs and any one output for plotting. Below these menusare two input fields X grids: and Y grids: for specifyingthe number of x-axis and y-axis grid lines we want to

include. For handling cases with two (or more) inputs andone output: The Surface Viewer has a special capabilitythat is very helpful in grabbing the axes, with the help ofmouse and can be repositioned to get a different three-dimensional view on the data.

Assume we have a four-input one-output framework andmight want to see the output surface. The Surface Viewercan create a three-dimensional output surface where anytwo of the inputs differ, however two of the inputs must beheld steady on the grounds that PC screens can't show afive-dimensional shape. In such a case, the input is a four-dimensional vector with NaNs holding the spot of thefluctuating inputs while numerical qualities demonstratesthose values that remain altered. The IEEE symbol for Nota Number is NaN. The menu items permit us to open,close, save and alter a fuzzy framework utilizing the fivefundamental GUI tools. We can access data about theSurface Viewer by clicking Help and close the GUIutilizing Close. This finishes up the brisk stroll through ofeach of the primary GUI tools.

V. CONCLUSION

The Fuzzy Logic Toolbox is greatly amazing in allregards. It makes fuzzy logic a successful tool for theorigination and configuration of intelligent frameworks.The Fuzzy Logic Toolbox is not difficult to ace andadvantageous to utilize. The output created by the SurfaceViewer is three Dimensional and has an unique capacitythat is exceptionally accommodating in cases with two (ormore) inputs and one output. For our issue, the output ofthe fuzzy system matches our unique thought of the stateof the fuzzy mapping from QoWL, OrganizationCommitment to Turnover Intention. In insight into thepast, we may say, "Why trouble? We could have barelydrawn a speedy lookup table and been carried out a hourback!" However, in the event that we are intrigued bytackling a whole class of comparative choice makingissues, fuzzy logic may furnish a suitable instrument forthe result, provided for them its straightforwardness withwhich a framework might be rapidly changed.

ACKNOWLEDGEMENT

The authors would like to thank the Management ofK.L.N. College of Engineering for giving the offices tocomplete the exploration work.

REFERENCES

[1] Mamdani, E.H. and S. Assilian, "An experiment inlinguistic synthesis with a fuzzy logiccontroller," International Journal of Man-Machine Studies ,Vol. 7, No. 1, pp. 1-13, 1975.[2] Zadeh, L.A., "Outline of a new approach to the analysis ofcomplex systems and decision processes," IEEE Transactionson Systems, Man, and Cybernetics , Vol. 3, No. 1, pp. 28-44,Jan. 1973.[3] Matlab Fuzzy Logic Toolbox User guide .pp 10.[4] http://www.mathworks.in/help/fuzzy/building-systems-with-fuzzy-logic-toolbox-software.html

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