soft computing
SOFT COMPUTING
social sciences behavioral sciences the humanities economics law medicine include
quantitative
Human sciences methods are
methods are often used separately qualitative
that means
numerical data precise objects conventional logic complicated mathematics computer models
that means
non-numerical data imprecise objects approximate reasoning interpretation manual work
2
Traditional Approaches to Computerized Modeling
• Mathematical models:
Complicated, black boxes, number crunching. • Rule-based systems
(crisp & bivalent):
Large rule bases.
3
What is Hard Computing?
• Hard
computing, i.e., conventional computing, requires a precisely stated analytical model and often a lot of computation time.
• Many analytical models are valid for ideal cases.
• Real world problems exist in a non-ideal environment.
• Premises and guiding principles of Hard Computing are
– Precision, Certainty, and rigor.
• Many contemporary problems do not lend themselves to precise solutions such as – Recognition problems (handwriting, speech, objects, images) – Mobile robot coordination, forecasting, combinatorial problems etc.
V
L
Ø s(t) md2/dt2(s(t)+L∙sinø(t) = H(t) md2/dt2(L∙cosø(t)) = V(t)-m∙g
Jd2/dt2 = (L∙V(t)∙sinø(t)-L∙H(t)∙cosø(t)) = V(t)-m∙g
Md2/dt2∙s(t) = μ(t)-H(t)-Fd/dt∙s(t)
M
H(t)
mimic human reasoning that is often linguistic and approximate by nature fuzzy systems, probabilistic reasoning, natural computing (evolutionary computing, cellular automata, DNA computing, neurocomputing, immunocomputing, swarm theory, etc.).
aims to
includes
Soft Computing (SC) can be used with mathematical or statistical methods
(hybrid methods)
can cope with also known as adaptive and intelligent systems or computational intelligence imprecision learning uncertainty optimization What is Soft Computing ?
(adapted from L.A. Zadeh)
•
social sciences behavioral sciences the humanities economics law medicine include
quantitative
Human sciences methods are
methods are often used separately qualitative
that means
numerical data precise objects conventional logic complicated mathematics computer models
that means
non-numerical data imprecise objects approximate reasoning interpretation manual work
2
Traditional Approaches to Computerized Modeling
• Mathematical models:
Complicated, black boxes, number crunching. • Rule-based systems
(crisp & bivalent):
Large rule bases.
3
What is Hard Computing?
• Hard
computing, i.e., conventional computing, requires a precisely stated analytical model and often a lot of computation time.
• Many analytical models are valid for ideal cases.
• Real world problems exist in a non-ideal environment.
• Premises and guiding principles of Hard Computing are
– Precision, Certainty, and rigor.
• Many contemporary problems do not lend themselves to precise solutions such as – Recognition problems (handwriting, speech, objects, images) – Mobile robot coordination, forecasting, combinatorial problems etc.
V
L
Ø s(t) md2/dt2(s(t)+L∙sinø(t) = H(t) md2/dt2(L∙cosø(t)) = V(t)-m∙g
Jd2/dt2 = (L∙V(t)∙sinø(t)-L∙H(t)∙cosø(t)) = V(t)-m∙g
Md2/dt2∙s(t) = μ(t)-H(t)-Fd/dt∙s(t)
M
H(t)
mimic human reasoning that is often linguistic and approximate by nature fuzzy systems, probabilistic reasoning, natural computing (evolutionary computing, cellular automata, DNA computing, neurocomputing, immunocomputing, swarm theory, etc.).
aims to
includes
Soft Computing (SC) can be used with mathematical or statistical methods
(hybrid methods)
can cope with also known as adaptive and intelligent systems or computational intelligence imprecision learning uncertainty optimization What is Soft Computing ?
(adapted from L.A. Zadeh)
•