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THE MODEL OF ASIS FOR PROCESS CONTROL APPLICATIONS P.Andreeva, T.Atanasova, J.Zaprianov Institute of Control and System Researches. Topic Area: 12. Intelligent Learning Support Systems Nom et ipsa scientia potestas est. “ Knowledge itself is power.“ Francis Bacon (1561-1626).

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Topic area 12 intelligent learning support systems nom et ipsa scientia potestas est

THE MODEL OF ASIS FOR PROCESS CONTROL APPLICATIONSP.Andreeva, T.Atanasova, J.ZaprianovInstitute of Control and System Researches

Topic Area: 12. Intelligent Learning Support Systems

Nom et ipsa scientia potestas est.

“ Knowledge itself is power.“

Francis Bacon (1561-1626)


Topic area 12 intelligent learning support systems nom et ipsa scientia potestas est

1.INTRODUCTION

2. THE MODEL OF ASIS2.1 Structure and Elements of ASIS 2.2 Information Processing in Knowledge Base2.3 Fuzzy Rules in ASIS

3. DYNAMIC METHOD3.1 Fuzzy Relational Data Base Operation

4. KNOWLEDGE PRESENTATION4.1 Operation on Knowledge Base4.2 Method for Fuzzy Variable Description

CONCLUSION

P. Andreeva, T. Atanasova, J. Zaprianov


1 introduction

1. INTRODUCTION

  • The aim of ASIS -> to support the decision making process in KBS

    automatic control system environment with KB management as an integral part

  • ASIS -> to model the knowledge in KBS and to suggest the most appropriate decision

  • Information processing -> by fuzzy rules

  • Fuzzy control formulates the control algorithm by logical rules

P. Andreeva, T. Atanasova, J. Zaprianov


Derivation of the process control rules

Derivation of the process control rules

  • From the experience-based knowledge of the process

  • using linguistic description (fuzzy model of the process)

  • on the base of non-linear model making fuzzy version of Sliding Mode Control.

P. Andreeva, T. Atanasova, J. Zaprianov


Adaptive self learning information system

Adaptive Self-learning Information System

  • Heuristic knowledge -> describing the rules linguistically

  • estimation of the best alternative & suggestion for the best decision

  • the given input is followed -> adaptive

  • the fuzzy classifier proceeds new input & executes the rule to find a new fact -> self-learning

P. Andreeva, T. Atanasova, J. Zaprianov


2 the model of asis

2. The MODEL of ASIS

P. Andreeva, T. Atanasova, J. Zaprianov


2 1 structure and elements of asis

2.1 Structure and Elements of ASIS

  • linguistic variable base;

  • fuzzy classifier ;

  • decision making block.

P. Andreeva, T. Atanasova, J. Zaprianov


Information processing in asis

Information Processing in ASIS

P. Andreeva, T. Atanasova, J. Zaprianov


2 2 information processing in kb

2.2 Information Processing in KB

  • In fuzzy relational DB, domain values dj1, dj2, ... djm need not be atomic.

  • Interpretation  [a1, a2,... an] of tuple Tj = [dj1, dj2, ... djm] is any value assignment such that aidij for all i.

  • The space of interpretations is the set cross product D1 x D2 x ... Dn.

P. Andreeva, T. Atanasova, J. Zaprianov


Topic area 12 intelligent learning support systems nom et ipsa scientia potestas est

2.3 Fuzzy Rules in ASISCluster approach (Bezdek, 1981)similarity measure between k-th point Xkand i-th collection of typical points-centroid V

P. Andreeva, T. Atanasova, J. Zaprianov


The method of fuzzy clustering mean fcm

The method of Fuzzy Clustering mean (FCM)

  • Fuzzy cluster approach is used to classify the input data and to receive the rules. The original idea of using fuzzy sets in clustering techniques was introduced by (Bezdek, 1981) and consists in the fuzzification of the partition functional.

P. Andreeva, T. Atanasova, J. Zaprianov


Fuzzy clustering based on fcm

Fuzzy Clustering based on FCM

P. Andreeva, T. Atanasova, J. Zaprianov


3 dynamic method

3. DYNAMIC METHOD

  • On-line Information System.

  • Dynamic changes and Updates.

  • Dynamic method for retrieval.

  • Adding new rules to KB.

  • Fuzzy query processing (imprecise predicate) -> extension of SQL.

P. Andreeva, T. Atanasova, J. Zaprianov


3 1 fuzzy relational db operation

3.1 Fuzzy Relational DB Operation

  • find the most specific rule to be fired;

  • execute the rule on the last data facts;

  • do this (in cycle) while no rules are left, or until criterion is satisfied;

  • every rule can be fired only once.

P. Andreeva, T. Atanasova, J. Zaprianov


4 knowledge presentation approaches to represent knowledge

4. KNOWLEDGE PRESENTATIONApproaches to Represent Knowledge:

  • first-order logic

  • production system

  • semantic nets

  • frame

P. Andreeva, T. Atanasova, J. Zaprianov


Rule based system

Rule-based System

  • Information knowledge is codified in rules, which consist of linguistic variables.

  • It is possible to assign to each one of them an ‘evaluation’ µ.

    µ() = 1 ; µ() = 0 ; µ() = 1/2

P. Andreeva, T. Atanasova, J. Zaprianov


4 1 operation on knowledge base

4.1 Operation on Knowledge Base

- Store facts - check that it does not conflict with previously stored facts;

- Retrieve facts – performs queries;

- Inference, i.e. the retrieval of implicit knowledge;

- Return definition of concepts;

- Remove facts from the knowledge base.

P. Andreeva, T. Atanasova, J. Zaprianov


4 2 method for fuzzy variable description

4.2 Method for Fuzzy Variable Description

  • Membership function µ [0,1]

  • Operation -unary (modifier)

    -binary (composition of fuzzy numbers)

  • R: A x B -> [0,1]

    xfrom A and yfrom B, µR(x,y) express the strength of their bond

  • formal association between antecedents (if-part) and consequent (then-part)

P. Andreeva, T. Atanasova, J. Zaprianov


Conclusion

CONCLUSION

  • Best alternative is estimated

  • fuzzy presentation of knowledge

  • adaptive information system -> the given input is followed

  • self-learning -> executes rule to find a new fact

  • dynamic method for reasoning -> stepwise refinement

  • fuzziness in relational DB in 3 aspects

P. Andreeva, T. Atanasova, J. Zaprianov


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