A human science approach to soft computing - PowerPoint PPT Presentation

1 / 42
About This Presentation
Title:

A human science approach to soft computing

Description:

A human science approach to soft computing. Vesa A. Niskanen ... the interpretative approach is hermeneutics. ... continuous process (hermeneutic circle). VAN ... – PowerPoint PPT presentation

Number of Views:131
Avg rating:3.0/5.0
Slides: 43
Provided by: VAN151
Category:

less

Transcript and Presenter's Notes

Title: A human science approach to soft computing


1
A human science approach to soft computing
  • Vesa A. Niskanen
  • Univ. of Helsinki
  • vesa.a.niskanen_at_helsinki.fi

2
Application areas
  • Data compression, clustering.
  • Regression analysis.
  • Analyzing paths, nets and graphs.
  • Virtual worlds, artificial life.
  • Interpretation of texts stories, narratology.

3
Relation between input and output
  • Goal y1/sqrt(2?)exp(-x2/2) no noise in data
  • (standard normal distribution)

4
Fuzzy model cluster centres
  • if x then y
  • -2.90 0.01
  • -1.00 0.24
  • 2.10 0.04
  • Subclust (d0.9),
  • these are rules

5
Fuzzy model (Tagaki-Sugeno)
  • If model unsatisfactory, we can tune it with NN.
  • Goodness criteria eg. Rmse, Analysis of
    variance.
  • Non-parametric non-linear models.

6
Neuro-fuzzy model (Anfis)
7
Model construction
  • If data available, we can use fuzzy clustering or
    SOM.
  • Otherwise knowledge of experts.
  • We can also use both of these sources.

8
Model construction
Fuzzy rules(SOM, c-means etc.) Fuzzy
reasoning
Training data
Experts
System evaluation(errors)
Tuning(NN)
Control data
New system
9
Fuzzy model (Mamdani)
  • Problem How much tip in restaurant in USA
    according to given criteria? (gt multi-criteria
    decision-making)
  • No data gt expertise.
  • Two criteria (inputs)
  • quality of service
  • quality of food
  • Output tip.

10
Decision model
Quality of service
Tipping
Quality of food
11
Linguistic values of variables
  • Service poor, good, excellent.
  • Food rancid, delicious.
  • Tip cheap, average, generous.

12
Fuzzy rules
  • 1 If service is poor or food is rancid, then tip
    is cheap.
  • 2 If service is good, then tip is average.
  • 3 If service is excellent or food is delicious,
    then tip is generous.

13
Meanings of values
  • The meanings of values are fuzzy sets in a given
    space.
  • Then, we can use fuzzy reasoning.
  • Inputs/outputs can be either crisp or fuzzy.

14
Model construction (Matlab)
15
Empiric data of schoolboys
  • Data (SAS/STAT)
  • 126 schoolboys.
  • Ages in months
  • Heights in inches
  • Weights in pounds.
  • The training data comprised 95 randomly selected
    observations, and the rest (31) were used as
    control data.

16
Model (schoolboys)
Age
Weight
Height
17
Sample of the data
Age / Height / Weight 159.0000 62.8000
99.0000 178.0000 63.8000 112.0000 153.0000
57.8000 79.5000 155.0000 57.3000
80.5000 178.0000 63.5000 102.5000 142.0000
55.0000 76.0000 164.0000 66.5000
112.0000 189.0000 65.0000 114.0000 164.0000
61.5000 140.0000 167.0000 62.0000
107.5000 151.0000 59.3000 87.0000
18
Input space (2D)
HEIGHT
AGE
19
Model space (3D)
20
Linear regression analysis
  • Training data
  • weight0.21age2.99height-116.60
  • R square 0.66
  • Rmse 11.52
  • Control data
  • Rmse 11.97

21
Generalized mean (non-linear)
  • weight(w1agepw2heightp)1/p
  • (w1w21)
  • w10.84
  • w10.16
  • p3.09
  • Rmse(control)10.76
  • Gen. means can be neurons in NN.

22
Fuzzy (Takagi-Sugeno)
  • Initial rules /
  • cluster centres
  • IF THEN
  • Age Height Weight
  • 1 151 58.3 86
  • 2 172 65 112
  • 3 193 67.8 127.5
  • 4 150 61.8 118
  • (d0.4, Rmse9.99)

4 If A is young and H is medium, then W is
medium
23
Fuzzy (Takagi-Sugeno)
  • Actual rules
  • IF THEN
  • Age Height Weight
  • 1 151 58.3 -0.178age - 1.910height 201.100
  • 2 172 65 1.047age 3.596height - 306.300
  • 3 193 67.8 0.849age 5.263height - 391.400
  • 4 150 61.8 1.155age - 0.718height 5.429
  • Cf. Ancova.

24
Neuro-fuzzy (Anfis)
  • Tune the rules if necessary.

25
Interpretation of stories and texts
  • Plot / intrigue of a story, report, an interview
    or a historical event.
  • Plot / intrigue of a picture or movie.
  • Internet / WWW gt


26
Interpretation
  • In a wide sense, interpretation (hermenuein in
    the Ancient Greek) means (i) delivering messages,
    (ii) explanation, (iii) exegesis or (iv)
    translation.
  • At the core of the interpretative approach is
    hermeneutics.
  • Interpretation is based on the foreknowledge
    (Vorverständnis) of the researcher. The
    foreknowledge will be modified according to our
    study.
  • The whole of the object or phenomenon may be
    understood according to its parts, and vice
    versa. This is a continuous process (hermeneutic
    circle).

27
Conventional qualitative tool Atlas
  • ATLAS.ti is a powerful workbench for the
    qualitative analysis of large bodies of textual,
    graphical, audio and video data.
  • It offers a variety of tools for accomplishing
    the tasks associated with any systematic approach
    to "soft" data, e.g., material which cannot be
    analyzed by formal, statistical approaches in
    meaningful ways.
  • ATLAS.ti's unique network building feature allows
    you to visually "connect" selected passages,
    memos, and codes by means of diagrams. This
    feature allows you to construct concepts and
    theories based on visible relations - often
    bringing to light yet other relations. You may
    instantly revert back to your notes or primary
    document selection at any time.
  • Networks open up a "context of discovery" and new
    approaches to theory building.

28
Hypertext our materials or data constitute
pieces of information and we may construct
networks in which their constituents are
interconnected
Linear
Non-linear
Classic examples I Ching, Homer's Odysseia,
several Aristotle's writings
29
Atlas Semantic network
30
Fuzzy cognitive maps (FCM)
  • A complex society is like a water balloon.
    Squeeze it here, and the water moves over there.
    Anything that makes big changes in one sector
    will affect other sectors.
  • For example, What happens under health care
    reform? The problem is how to model complex
    feedback dynamical systems.
  • What happens when, for example, the reform
    movement starts? The fuzzy cognitive maps (FCM)
    are expressly designed for this kind of
    high-level modeling.

31
Fuzzy cognitive maps (FCM)
  • FCM are fuzzy signed graphs with possible
    feedback.
  • The nodes are causal concepts.
  • They can model events, actions, values, goals,
    stories etc.

32
South African apartheid politics (Kosko)
33
NN approach
  • Edge matrix lists the connections of causal
    links.
  • The inputs and outputs are state vectors, and
    matrix multiplication is used.
  • Outputs are modified with transformation function
    if necessary.

34
NN structure of FCM
(0,1,0) o
(x,y,z)
Transformation /thresholding
35
FCM
  • Matrix cells can contain bivalent 0,1,
    trivalent -1,0,1 or real valued values.
  • The values in each node can oscillate, be chaotic
    or they can finally obtain stable values.
  • Adaptive FCMs learn from training data. They
    modify the edge matrix according this data. (gt
    NN)
  • Fuzzy Thought Amplifier

36
South African politics (Kosko)
1"positive correlation"0"no correlation"-1"ne
gative correlation"
37
South African politics (simulation)
Start Support foreign investment policy(keep
c11 constantly)
Transformation xlt0 gt x0xgt0 gt x1
In1(1,0,0,0,0,0,0,0,0), out1(0,1,1,0,0,0,0,1,1),
transformgt in2(1,1,1,0,0,0,0,1,1),
out2(0,1,2,1,-1,-1,-1,4,1), transformgt
in3(1,1,1,1,0,0,0,1,1), out3(0,1,2,1,-1,0,0,4,1)
, transformgt in4(1,1,1,1,0,0,0,1,1)out3 gt
system is stable, and mining, black employment,
strength of gov. and nat. party constituency are
on. Hence, sustained foreign investment maintains
a balance of government stability and racism.
38
Linguistic FCM
  • We can use linguistic inputs and outputs.
  • The relations between nodes are described by
    linguistic rules.

Node
Node
Linguistic rules
39
Fuzzy virtual worlds (Kosko)
  • A virtual world (cyberspace) is a dynamical
    system which changes with time as the user moves
    through.
  • It links humans and computers in a medium that
    can trick the mind and senses.

40
Virtual undersea world (Kosko)
shark
fish school
dolphin herd
41
Node matrix for undersea world
We may link animations to these maps
42
References
R. Axelrod, Structure of decision the cognitive
maps of political elites. Princeton Univ. Press,
1976. H. Bandemer W. Näther, Fuzzy data
analysis (Kluwer, Dordrecht, 1992). S. Chiu,
Fuzzy model identification based on cluster
estimation, Journal of Intelligent and Fuzzy
Systems, 2 (1994) 267-278. H. Dyckhoff W.
Pedrycz, Generalized means as model of
compensative connectives, Fuzzy Sets and Systems
14 (1984) 143-154. R. Jang, ANFIS
Adaptive-network-based fuzzy inference system,
IEEE Transactions on Systems, Man and Cybernetics
23/3 (1993) 665-685. Kacprzyk, J. and Fedrizzi,
M. (Eds.), Fuzzy Regression Analysis (Physica
Verlag, Heidelberg, 1992). W. Kickert, Fuzzy
Theories on Decision Making (M. Nijhoff, Boston,
1978). B. Kosko, Neural networks and fuzzy
systems, (Prentice-Hall, Englewood Cliffs,
1992). V.A. Niskanen, A brief logopedics for the
data used in a neuro-fuzzy milieu, Lecture Notes
in Artificial Intelligence, 1566, Springer
Verlag, Berlin, 1999, pp. 222-233. I. Rojas
al., Statistical analysis of the main parameters
in the fuzzy inference process, Fuzzy Sets and
Systems,102/2 (1999) 157-173. M. Smithson, Fuzzy
set analysis for behavioural and social sciences
(Springer Verlag, New York, 1987). T. Takagi M.
Sugeno, Fuzzy identification of systems and its
applications to modeling and control, IEEE
Transactions on Systems, Man and Cybernetics,
SMC-15/1 (1985) 116-132. R. Yager D. Filev,
Generation of fuzzy rules by mountain clustering,
Journal of Intelligent and Fuzzy Systems 2 (1994)
209-219. L. Zadeh, Fuzzy logic Computing with
words, IEEE Transactions on Fuzzy Systems, vol.
2, pp. 103-111, 1996. L. Zadeh, From Computing
with Numbers to Computing with Words -- From
Manipulation of Measurements to Manipulation of
Perceptions, IEEE Transactions on Circuits and
Systems, 45, 1999, 105-119. L. Zadeh, Toward a
theory of fuzzy information granulation and its
centrality in human reasoning and fuzzy logic,
Fuzzy Sets and Systems 90/2 (1997) 111-127.
Write a Comment
User Comments (0)
About PowerShow.com