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ICT619 Intelligent Systems

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Involves comparing the problem attributes with those of a typical fuzzy model ... Case Study 2 : Bond rating (McNeill & Thro 1994) ... – PowerPoint PPT presentation

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Title: ICT619 Intelligent Systems


1
  • ICT619 Administrative Notices
  • Note No class Thursday 20th Sept.
  • If you haven't already, please submit your 1
    page project proposal as soon as possible

2
ICT619 Intelligent SystemsTopic 3 Fuzzy Systems
3
Fuzzy Systems
  • PART A
  • Introduction
  • Applications
  • Fuzzy sets and fuzzy logic
  • Probability and fuzzy logic
  • Fuzzy reasoning
  • Design of a fuzzy controller
  • PART B
  • Building fuzzy systems
  • Advantages and limitations of fuzzy systems
  • Case Studies

4
Building fuzzy systemsPrincipal issues
  • Validation of proposed application
  • Fuzzy set and data representation
  • Fuzzy set hedges
  • Dealing with Boolean variables
  • Uncertain and noisy data
  • Fuzzy system design methodology

5
Validation of proposed application
  • Involves comparing the problem attributes with
    those of a typical fuzzy model
  • Characteristics of a fuzzy model
  • Intrinsically imprecise parameters (control
    variables)
  • The domain expert expresses heuristics using
    vague linguistic expressions for example,
    relatively high profit margin, small-to-medium
    sized companies.
  • Draws on knowledge from multiple experts

6
Validation of proposed application (contd)
  • Characteristics of a fuzzy model (contd)
  • Experts express apparently conflicting opinions.
    For example,
  • Our price must be high Company financial
    expert
  • Our price must be low Company marketing and
    sales expert.
  • Complex, poorly understood, nonlinear problems
  • No mathematical model exists to represent it.
  • Too difficult for traditional expert systems,
    mathematical and statistical approaches.

7
Fuzzy set and data representation
  • Problem variables define data used and produced
    by the system
  • Variables decomposed into one or more fuzzy sets,
    with each set describing some range of the
    variables values
  • Each domain value (for example, profit figures in
    dollars) must belong to at least one fuzzy set

8
Fuzzy set and data representation(contd)
There should be between 25 and 50 overlaps
between adjacent fuzzy sets.
9
Designing and eliciting fuzzy sets
  • Knowledge acquisition
  • Sources include domain experts, articles,
    procedure manuals, or other documents
  • All relevant objects (variables) and their
    properties (fuzzy sets) are extracted
  • Derivation of fuzzy sets
  • Voting by multiple experts
  • Statistical analysis of domain data
  • Data mining techniques

10
Derivation of fuzzy sets
  • Voting by multiple experts
  • A fuzzy sets membership function can be based on
    cumulative frequency of votes by experts
  • Eg, Membership value of 170 cm in tall determined
    by how many regard 170 cm as tall
  • Experts must agree on the underlying domain
    tall for a man? tall for a basket ball player?

11
Derivation of fuzzy sets (contd)
  • Statistical analysis of domain data
  • For example, data are normally distributed, mean
    and standard deviation are known
  • But data in financial analysis, marketing, risk
    assessment, project management and so on are
    seldom normally distributed and can be subject to
    sudden changes
  • Data mining techniques
  • Used to decompose underlying variables into
    arbitrary collections of fuzzy sets

12
Fuzzy set hedges
  • Hedges are modifiers of fuzzy set membership
    functions.
  • Act like adjectives and adverbs in English, such
    as "very" or "often"
  • Allow a closer and more intuitive modelling of
    the underlying knowledge as expressed in language
  • Defining hedges can be a subjective process and
    may vary from one problem domain to another

13
Fuzzy set hedges (contd)
  • Some examples
  • Very, almost, about, roughly, quite, more or
    less, somewhat, rather
  • Membership very of variable x (membership of
    variable x)2
  • If income is low to a degree of 0.8, then income
    will be very low to a degree of (0.8)2 0.64.
  • Membership more or less of variable x
    ?(membership of variable x)
  • Hedges reduce the number of fuzzy sets that need
    to be created
  • Hedges also increase the readability of rules and
    consequently ease maintenance.

14
Dealing with Boolean variables
  • Problem with Boolean expressions
  • Boolean variables in the antecedents of rules may
    saturate consequent fuzzy space, and pre-empt all
    the other rules
  • Example
  • Rules from a vehicle underwriting risk
    assessment model
  • IF age is young THEN risk is increased
  • IF distance_to_work is far THEN risk is slightly
    increased
  • IF accidents_in_last2years is excessive THEN risk
    is very increased
  • IF drink_driving_convictions is significant THEN
    risk is totally increased
  • Now consider the addition of the following rule
  • IF sexmale THEN risk is increased
  • may cause the consequent truth value to evaluate
    to 1.0 if sex happens to be male

15
Dealing with Boolean variables(contd)
  • Boolean filters
  • A Boolean filter can be added to the antecedent
    of a rule as a qualifying proposition to avoid
    saturation
  • IF sexmale AND age is young THEN risk is
    highly increased
  • If the expression sexmale is true (value
    1), and the truth of age is young is say 0.38,
    the consequent will be assigned the truth value
    minimum(1.0, 0.38) or 0.38 (using the min-max
    rule).
  • When the expression sexmale is false (value
    0),
  • the minimum is zero and the rule is not executed
    (which is the original intent).
  • Explicit truth values for Boolean propositions
  • For example the rule
  • IF sexmale.60 THEN risk is increased
  • agrees with the intuition overall risk is
    increased independent of any other factors if the
    driver is male

16
Uncertain and noisy data
  • Data may be noisy or uncertain due to
  • imprecise measurement processes or
  • lack of confidence in the data source
  • May be handled by associating a fuzzy set with
    the data also
  • As an example, one of the input variables in the
    rule
  • IF income is moderately_high THEN credit_risk
    is medium
  • is the income stated by the candidate

17
Uncertain and noisy data (contd)
  • The assessor may associate a degree of
    confidence, say 0.8, with the income
  • the lower the confidence value, the broader
    the fuzzy numbers bell curve
  • The truth value of the antecedent is found by
    finding the point of maximum correlation between
    the antecedent fuzzy set moderately_high and the
    fuzzy set associated with the stated income
    (135,000 in this example)

Confidence set
18
Uncertain and noisy data (contd)
  • Application of the rule
  • IF income is moderately_high THEN credit_risk
    is medium
  • with cf0.8 for variable income will thus
    truncate the fuzzy space for medium credit risk
    to the level 0.7.

19
Fuzzy system design methodology
  • The basic steps
  • Validate application
  • Define functional and operational characteristics
  • Decompose variables into fuzzy sets
  • Write rules to describe behaviour
  • Define defuzzification methods
  • Define performance metrics
  • Test system
  • Evaluate and tune system

20
Definition of fuzzy sets
  • No formal procedure for designing these
    functions
  • Practical experience and intuition used
  • Experimental evidence indicates a high tolerance
    to fuzzy shapes
  • In control applications, the fuzzy sets are
    usually trapezoidal or triangular
  • Bell-shaped fuzzy sets may also be used instead
    of triangles
  • Sigmoids (S-curves) and linear surfaces are also
    used in information systems and applications in
    social sciences.

21
Writing rules
  • Rules are usually written in the form
  • IF x is S THEN y is T
  • where x and y are linguistic variables and S and
    T are fuzzy sets
  • In this rule, x is a control (input) variable and
    y is the solution (output) variable
  • Any unconditional rules of the form
  • x is S
  • is also entered.
  • Such rules act to constrain the shape of the
    solution fuzzy set.

22
Review of rules and addition of any hedges
  • If the fuzzy system supports hedges, the rule set
    should be examined to write any hedged rules
  • These rules generally capture extreme behaviour
    patterns, eg,
  • IF x is very S THEN y is very T
  • Addition of any alpha cuts to individual rules
  • An alpha cut establishes the minimum truth
    threshold for a rule
  • For example, given the rule
  • (Alpha0.2) IF age is young THEN risk is high
  • if the truth of the proposition age is young
    falls below 0.2, this rule will not fire
  • Prevents production of residual fuzzy spaces
    with very small but finite truth values

23
Entering rule execution weights
  • Importance of rules can be indicated by giving a
    weight multiplier. For example,
  • (Weight0.8) IF age is young THEN risk is high
  • Definition of defuzzification method
  • Defuzzification selects the expected value of a
    solution variable from the aggregated fuzzy
    region.
  • The most popular method is the centre of gravity
    or centroid as it gives a smoother transition
    from one observation to the next.

24
Advantages of fuzzy systems
  • Important attributes used by organisations in
    evaluating systems
  • Mean-time-between-failure (MTBF)
  • Mean-time-to-repair (MTTR)
  • Extensibility of existing systems
  • Agreement with real-world processes
  • Understandability
  • Business intelligent systems based on fuzzy logic
    show advantages in all these aspects

25
Advantages of fuzzy systems (contd)
  • Some benefits of fuzzy systems are
  • Ability to model highly complex business problems
  • Conventional expert systems failed to grow in
    business applications mainly due to the complex
    nature of many business problems
  • Fuzzy systems are highly suited for modelling
    computationally complex non-linear problems which
    are poorly understood
  • Efficient knowledge base
  • Fuzzy rule based systems require fewer rules and
    execute faster than conventional rule based
    systems.
  • A single rule applies by different degrees to a
    variety of situations.

26
Advantages of fuzzy systems (contd)
  • Improved cognitive modelling of expert systems
  • Specialized knowledge is often best expressed
    using imprecise terms. Conventional expert
    systems force experts to express rules using
    crisp boundaries
  • This contributes to poor performance in many
    systems.
  • Ability to model systems involving multiple
    experts
  • Since rules are applied in parallel rather than
    in sequence,
  • two rules reflecting two different opinions can
    both contribute to the final outcome.

27
Advantages of fuzzy systems (contd)
  • Reduced model complexity
  • Fuzzy systems use fewer and the rules are closer
    to the way knowledge is expressed in natural
    language. These factors greatly improve overall
    MTTR.
  • The understandability and ease of maintenance
    also leads to improved MTBF.
  • Improved handling of uncertainty
  • Fuzzy systems provide a better, more consistent
    and mathematically sound method of handling
    uncertainties
  • Example
  • IF height is tall THEN weight is heavy
  • A given height will produce an estimated weight
    with its degree of membership in the fuzzy set
    heavy.
  • An expert system would contain many rules, like
  • IF height is gt 135 and height is lt 165 THEN
    weight is 75, CF0.8

28
Limitations of fuzzy intelligent systems
  • Lack of learning capability
  • Hybrid, particularly neurofuzzy systems, attempt
    to remedy this
  • Determining and tuning membership functions
  • Is not an easy task
  • Difficult to say how many are really required
  • Requires extensive testing and fine tuning
  • Though easier to design and prototype than
    conventional systems, fuzzy systems require more
    fine tuning before they are operational.
  • Bias among traditionalists, especially in Western
    countries, for crisp logic based systems
  • Mistrust of fuzzy logic

29
Case Studies
  • Case 1 Quality control and monitoring (Dhar
    Stein 1997 p.203-210)
  • German superstore chain Kaufhof
  • Manufacturers supply directly to 16 warehouses.
  • Problem
  • 112,000 deliveries from manufacturers each day
  • About 1 have a problem (quality, type, number,
    breakages, etc.)
  • Checking all items individually is very labour
    intensive

30
Case Study 1 (contd)
  • Solution goal
  • A continual monitoring system for identifying
    shipments with high risk
  • Constraints
  • Should concentrate on riskier shipments
  • Be flexible enough to deal with new products,
    suppliers and quality control policies
  • Be fast enough not to disrupt the workflow at the
    warehouse

31
Case Study 1 (contd)
  • Relevant issues
  • Factors affecting shipments
  • The nature of items being delivered
  • Past performance of the supplier
  • Recent performance trends of the supplier
  • The size of the order
  • Factors interact in complex ways
  • Evaluation
  • Error rate of the brute-force system to be used
    as a benchmark for evaluating new system.

32
Case Study 1 (contd)
  • Possible solutions
  • ANN and statistical analysis solutions ruled out
    Reason No data available relating input
    (shipment attributes) to output (shipment errors)
  • Expert systems considered Reason Experts able
    to articulate criteria for wanting to inspect
    shipments

33
Case Study 1 (contd)
  • The fuzzy system solution - Invent/W
  • Fuzzy system solution chosen due to ability to
  • model processes with varying degrees of truth and
  • deal with the interaction of continuously varying
    variables
  • Information in shipment barcode (eg, product,
    supplier) used by Invent/W to gather input
    variables based on
  • historical data
  • trend data
  • product type data etc

34
Case Study 1 (contd)
  • Invent/W uses
  • Inputs from Kaufhof stores in the inventory
    system
  • ancillary databases
  • special tables used for building risk profiles
  • Analyses these data and produces the solution
    variable - a score between 0 and 100
  • The lower the number, the more the shipment
    should be inspected

35
Case Study 1 (contd)
  • The Invent/W rule base
  • 164 rules in the system
  • Rules consider suppliers' recent performance
  • A typical rule
  • IF recent_shipment_problem is high AND
    item_shipment_risk is high THEN risk is high
  • System integrated into a COBOL-based inventory
    management system, which runs on Kaufhofs
    mainframe computer
  • A Windows based graphical environment enable
    users to easily define rules and the shapes of
    fuzzy sets

36
Case Study 1 (contd)
  • Invent/W performance test
  • All shipments inspected regardless of score
    output by Invent/W
  • In first four months of testing, 98.5 of all
    erroneous shipments identified correctly
  • Currently performing at about 99.5 accuracy
  • Shipment inspection capacity required reduced by
    more than 50.

37
Case Study 2 Bond rating (McNeill Thro 1994)
  • A neuro-fuzzy system for rating the investment
    safety of bonds
  • Developed by Fujitsu in Japan
  • The system uses 10 fuzzy rules
  • Fuzzy membership functions and the rule weights
    adjusted using a neural network.
  • Rules cover the inputs and the financial
    condition of the company issuing the bonds

See http//www.fuzzysys.com/books/FLLib/FUZZYPDF
/FUZZYLOG.PDF
38
Case Study 2 (contd)
  • Financial condition of company issuing bonds
    determined by
  • Ordinary profit (with membership functions for
    fuzzy sets large, medium, small)
  • Owned capital (large, small)
  • Interest coverage ratio (high, low)
  • Long-term loan ratio (low, high)
  • Owned capital ratio (low)
  • Outputs (bond ratings) are high, medium, and low

39
Case Study 2 (contd)
  • The system uses two classes of rules
  • Basic rules
  • receive an initial weighting of 1, are related to
    ordinary profits
  • IF ordinary profit is large THEN rating is high
  • IF ordinary profit is medium, THEN rating is
    medium
  • IF ordinary profit is small, THEN rating is low
  • Auxiliary rules

40
Case Study 2 (contd)
  • Auxiliary rules
  • Cover the other inputs and receive an initial
    weighting of 0.2
  • IF owned capital is large, THEN rating is high
  • IF owned capital is small, THEN rating is small
  • IF interest coverage ratio is high, THEN rating
    is high
  • IF interest coverage ratio is low, THEN rating is
    low
  • IF long-term loan ratio is high, THEN rating is
    high
  • IF long-term loan ratio is low, THEN rating is
    low
  • IF owned capital ratio is low, THEN rating is
    low
  • Initial weight values associated with rules
    either increased or decreased after learning by
    the ANN.

41
REFERENCES
  • Cox, E., The Fuzzy Systems Handbook, AP
    Professional, San Diego 1999.
  • Dhar, V., Stein, R., Seven Methods for
    Transforming Corporate Data into Business
    Intelligence., Prentice Hall 1997, pp. 126-148,
    203-210.
  • Mcneill, F., Thro, E., Fuzzy Logic a Practical
    Approach, AP Professional, Boston 1994.
  • Munakata, T., Jani, Y., Fuzzy Systems an
    Overview, Communications of the ACM, Vol.37,
    No.3, 1994, pp.69-76.
  • Medsker,L., Hybrid Intelligent Systems, Kluwer
    Academic Press, Boston 1995.
  • Negnevitsky, M. Artificial Intelligence A Guide
    to Intelligent Systems, Addison-Wesley 2005.
    Sangalli, A., The Importance of being Fuzzy,
    Princeton University Press, 1998.
  • Zahedi, F., Intelligent Systems for Business,
    Wadsworth Publishing, Belmont, California, 1993.

42
ICT619 Student Projects from 2004
Iris dataset - ANN (MATLAB)
SW project cost estimation - ANN
Social skills assessment for kids - ES (EXpert2go)
HDD problem diagnosis - ES
PC troubleshooting - ES
Display troubleshooting - ES (CLIPS)
NW troubleshooting - ES (Exper2go)
Char recognition - ANN (JOONE)
Wine recommender - ES(Exp2Go)
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