Evaluating Product System Behavior using Soft Computing in Product Structure Modeling

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15th IEEE International Symposium on Applied Machine Intelligence and Informatics(SAMI)

Marta Takacs, Yatish Bathla                               John von Neumann Faculty of Informatics, Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Budapest, Hungary                                             

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OUTLINE

PURPOSEPRODUCT EXAMPLEPROPOSED WORK: RFLP STRUCTURE & BLOCKSROLE OF SOFT COMPUTINGMAMDANI FUZZY INFERENCE SYSTEMADAPTIVE NUERO FUZZY INFERENCE SYSTEMCONCLUSION & FUTURE WORK

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PURPOSE

To monitor the behavior of a productTo improve the behavior of a product systemTo propose the innovative concepts of soft computing in the product modeling

Drone with Camera is used as an ProductSituation: Tracking a car by droneSoftware: Catia V6Element: RFLP (Requirement Functional Logical Physical) Structure

PRODUCT EXAMPLE

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RFLP STRUCTURE & BLOCKS

• Requirement, Function and Logical Block are proposed corresponds to RFLP Structure

• Requirement Block: Collection of behavior blocks expected by the customers. Every Behavior has priority.

• Function Block: Collection of sub-functionality of single or multiple engineering objects correspond to the product.

• Logical Block: Different kind of logical connection exist in function Block

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 Representation from Clock-wise Direction: Requirement Block, Function Block, Logical Block   

BLOCK REPRESENTATION

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ROLE OF SOFT COMPUTING

• It is tolerant of imprecision, uncertainty, partial truth, and approximation

• .The principal constituents of Soft Computing (SC) are Fuzzy Logic(FL), Evolutionary Computation (EC), Neural Network(NN), Machine Learning(ML) and Probabilistic Reasoning(PR)

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Clockwise Direction: Simulink Model with n Mamdani Controller, Evaluation System behavior, Membership Function

MAMDANI FIS

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Clockwise Direction: FIS for tracking car as behavior, Input member function, Output member function

MAMDANI FIS MEMBER FUNCTION

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MAMDANI RULE BASE

Rule base of tracking car as member function

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MAMDANI FIS SIMULINK MODEL

• SImulink Model for evaluating System Behavior

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• Convert Mamdani to Sugeno FIS

• Clockwise direction: Sugeno FIS with Track car as behavior, Input Function, Output Function

ADAPTIVE NUERO FIS MEMBER FUNCTION 

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ADAPTIVE NUERO RULE BASE

Rule base of tracking car as member function

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• Clockwise Direction: Anfis Model for given behavior, Anfis Model for System Behavior, Trained Data vs Loaded Data

ADAPTIVE NUERO FIS 

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CONCLUSION & FUTURE WORK

Mamdani’s FIS can be considered as good approach for general system while Adaptive Nuero FIS can be considered as good approach for critical systems .

Both of the FIS are capable to monitor the behavior of product system but, accuracy of obtained behavior is still questionable  

THANK YOU

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