Download Sistem Kecerdasan Buatan

Sistem Kecerdasan
Buatan
RAHMAT FAUZI , S.T. M.T
Objective

To have an understanding of artificial neural networks (ANNs), fuzzy
systems, and evolutionary algorithms; and their application
intelligent systems.

To have a capability in realizing an intelligent system consists of the
ANN, fuzzy system, and evolutionary algorithm (programming skill).
content

• Artificial Neural Networks

• Fuzzy Systems

• Evolutionary Algorithm

• Special topics of intelligent systems
reference

Principles of neurocomputing for science & engineering, Fredric M Hum,
Ivica Kostanic, McGraw-Hill Inc., 2001

Neuro-fuzzy and soft computing, JSR Jang, CT Tsun, E. Mizutani, Prentice
Hall Inc., 1997.

Neural network fundamental, NK Bose and P. Liang, McGraw Hill, 1996.

Evolutionary Computation, David B Fogel, IEEE Press.

Fuzzy logic and intelligent systems, Hua Li, M Gupta (Eds), Kluwer
AcPress, 1995.

Fundamentals of the new artificial intelligence:

beyond traditional paradigm, Springer-Verlag, 1998.

Ahmad Arifin, Presentation Material. ITS. 2011.
Intelligence means…

The ability to solve hard problem (Minsky).

Capability to learn to perform various function within a changing
environment to survive and to prosper (Carne).

Property in which the organism senses, reacts, learns from, and
subsequently adapts its behavior to its present environment in order
to better promote its own survival (Atmar)

Capability of a system to adapt its behavior to meet its goals in a
range of environments (Fogel)
From Conventional AI to
Computational Intelligence

An advance computer technology
which is concerned with making
machines work in intelligent way,
mimicking the way of human mind.

Conventional AI: physical symbol
system hypothesis.
Expert System (most successful
conventional AI)
Intelligent System Scheme
Methodology
Strength
Neural Network
Learning and Adaptation
Fuzzy Set Theory
Knowledge representation via
fuzzy if-then rules
Genetic Algroithm and simulated
annealing
Systematic random search
Artificial Neural
Network
Basics of Neuroscience and
Neuron Models

ANNs (also called parallel
distributed processing systems)
and connectionist systems are
intended for modeling the
organizational principles of the
central nervous system.

Biologically inspired computing
capabilities of ANN is expected to
allow the cognitive and sensory
task to be performed more easily
NERVOUS SYSTEM
Basics of Neuroscience

Human nervous system is made up of a
vast network of computing elements
(neurons) with brain as a central unit.

The brain is connected to receptors that
bring sensory information, and it delivers
command to effectors.

A biological neuron consists three main
components: dendrite, cell body, and
axon.

Function of nervous system is represented
by activities of neurons, topology of the
connections, and theirstrength.