Transcript Intelligent System

Intelligent System
Ming-Feng Yeh
Department of Electrical Engineering
Lunghwa University of Science and Technology
E-mail: [email protected]
Website: http://mfyeh.myweb.hinet.net
Office: F412B-III Tel: #5518
Introduction
What is an “intelligent system”?
It is hard to define what exactly an “intelligent
system” is.
No one can deny that the intelligent system
already has an increasing impact on the
quality of life in many areas.
Intelligence in a system refers to its ability
to learn or adapt, and to modify its functional
dependences in response to new
experiences or due to changes in the
functional relationship.
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Introduction
This course will focus on introducing
the intelligent system technologies.
The students are expected to learn the
basic modeling techniques and to know
where to apply the knowledge.
The following materials will be covered
in this course:
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CONTENTS
Grey System Theory
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Grey Model / Grey Prediction
Grey Relational Analysis
Fuzzy Control
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Fuzzy Logic
Fuzzy Control
Neural Networks
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Neural Networks
Cerebellar Model Articulation Controller
Genetic Algorithm
Hybrid Systems
Applications
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SYLLABUS
Textbook:
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No textbook.
Some references will be assigned in the
class.
Evaluation Criteria:
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Midterm Oral Report: 50%
Final Oral Report: 50%
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Soft / Hard Computing
Hard computing whose prime desiderata are
precision, certainty, and rigor.
Soft computing is tolerant of imprecision,
uncertainty, and partial truth. (Lotfi Zadeh)
The primary aim of soft computing is to exploit
such tolerance to achieve tractability, robustness,
a high level of machine intelligence, and a low
cost in practical applications.
Fuzzy logic, neural networks (including CMAC),
probabilistic reasoning (genetic algorithm,
evolutionary programming, and chaotic systems)
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Soft Computing
Methodology
Strength
Neural network
Learning and adaptation
Fuzzy set theory
Knowledge representation
via fuzzy if-then rule
Systematic random search
Genetic algorithm
and simulated
annealing
Conventional AI
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Symbolic manipulation
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Computational Intelligence
Fuzzy logic, neural network, genetic algorithm,
and evolutionary programming are also
considered the building blocks of
computational intelligence. (James Bezdek)
Computational intelligence is low-level
cognition in the style of human brain and is
contrast to conventional (symbolic) artificial
intelligence (AI).
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