1. ALI AKRAM SABER
ID : P71082
SUBJECT : INTELLIGENT URBAN TRAFFIC CONTROL SYSTEM
2. INTRODUCTION
Artificial intelligence (AI) is the human-like
intelligence exhibited by machines or software. It is
also an academic field of study. Major AI researchers
and textbooks define the field as "the study and design
of intelligent agents", where an intelligent agent is a
system that perceives its environment and takes
actions that maximize its chances of success. It as "the
science and engineering of making intelligent
machines".
3. NEURAL NETWORK
Neural network" redirects here. For networks of living
neurons, see Biological neural network.
Neural Networks can be loosely separated into Neural Models,
Network Models and Learning Rules. the earliest mathematical
models of the Neuron pre-date Mcullock and Pitts who
developed the first Network models to explain how the signals
passed from one neuron to another within the network. When
you hear of a network being described as a feed forward or
feedback network, they are describing how the network connects
neurons in one layer to neurons in the next. Weiners work
allowed Mculloch and Pitts to describe how these different
connection types would affect the operation of the network.
4. Hopfield: A Hopfield network is a fully connected network. A
unit receives input from all other units. There is no distinction
between input units, hidden units and output units. When an
input pattern is presented, all units obtain their initial state from
the input pattern.
NEURAL NETWORK
5. Boltzmann divides all network nodes into three groups: input
nodes, output nodes, and hidden nodes
Multi-layered network is a feed forward network. Three or
more layers of artificial neurons are used with one layer
representing input data and one layer representing the
corresponding output.
Adaptive Resonance Theory The term "resonance" refers here
to the so called resonant state of the network in which a
category prototype vector matches the current input vector
close enough so the orienting subsystem will not generate a
reset signal to the second layer.
NEURAL NETWORK
6. in the computer science field of artificial intelligence, genetic
algorithm (GA) is a search heuristic that mimics the process of
natural selection. This heuristic (also sometimes called a
metaheuristic) is routinely used to generate useful solutions to
optimization and search problems. Genetic algorithms belong to the
larger class of evolutionary algorithms (EA), which generate
solutions to optimization problems using techniques inspired by
natural evolution, such as inheritance, mutation, selection, and
crossover.
Genetic algorithms find application in bioinformatics, phylogenetic,
computational science, engineering, economics, chemistry,
manufacturing, mathematics, physics, pharmacometrics and other
fields.
GENETIC ALGORITHM
7. Initially many individual solutions are (usually) randomly
generated to form an initial population. The population
size depends on the nature of the problem, but typically
contains several hundreds or thousands of possible
solutions. Traditionally, the population is generated
randomly, allowing the entire range of possible solutions
(the search space). Occasionally, the solutions may be
"seeded" in areas where optimal solutions are likely to be
found.
INITIALIZATION OF GENETIC ALGORITHM
9. Before a genetic algorithm can be put to work on any problem,
a method is needed to encode potential solutions to that
problem in a form so that a computer can process.
Common approaches are:
Binary Encoding : every chromosome is a string of 0 or 1
• Permutation Encoding : every chromosome is a string of
numbers that represent position in a sequence
• Tree Encoding : a tree structure represents the chromosome
• Value Encoding : every chromosome is a sequence of some
values (real numbers, characters or objects)
ENCODING
10. Expert Systems are computer programs that are derived from a
branch of computer science research called Artificial
Intelligence (AI). AI's scientific goal is to understand
intelligence by building computer programs that exhibit
intelligent behavior. It is concerned with the concepts and
methods of symbolic inference, or reasoning, by a computer,
and how the knowledge used to make those inferences will be
represented inside the machine.
The term intelligence covers many cognitive skills, including
the ability to solve problems, learn, and understand language;
AI addresses all of those. But most progress to date in AI has
been made in the area of problem solving.
EXPERT SYSTEMS
11. Every expert system consists of two principal parts: the
knowledge base; and the reasoning, or inference, engine.
The knowledge base of expert systems contains both factual
and heuristic knowledge. Factual knowledge is that knowledge
of the task domain that is widely shared, typically found in
textbooks or journals, and commonly agreed upon by those
knowledgeable in the particular field.
Heuristic knowledge is the less rigorous, more experiential,
more judgmental knowledge of performance. In contrast to
factual knowledge, heuristic knowledge is rarely discussed, and
is largely individualistic. It is the knowledge of good practice,
good judgment, and plausible reasoning in the field. It is the
knowledge that underlies the "art of good guessing."
THE BUILDING BLOCKS OF
EXPERT SYSTEMS
12. In conventional computer programs, problem-
solving knowledge is encoded in program logic
and program-resident data structures. Expert
systems differ from conventional programs both
in the way problem knowledge is stored and
used.
DISTINGUISHING FEATURES
13. Expert systems are especially important to organizations
that rely on people who possess specialized knowledge
of some problem domain, especially if this knowledge
and experience cannot be easily transferred. Artificial
intelligence methods and techniques have been applied
to a broad range of problems and disciplines, some of
which are esoteric and others which are extremely
practical.
UTILITY OF EXPERT SYSTEMS
14. A rule-based, expert system maintains a separation
between its Knowledge-base and that part of the
system that executes rules, often referred to as the
expert system shell. The system shell is indifferent to
the rules it executes. This is an important distinction,
because it means that the expert system shell can be
applied to many different problem domains with little
or no change.
ADVANTAGES OF RULE-BASED SYSTEMS
16. Fuzzy logic is a form of many-valued logic; it deals with
reasoning that is approximate rather than fixed and exact.
Compared to traditional binary sets (where variables may
take on true or false values), fuzzy logic variables may
have a truth value that ranges in degree between 0 and 1.
Fuzzy logic has been extended to handle the concept of
partial truth, where the truth value may range between
completely true and completely false. Furthermore, when
linguistic variables are used, these degrees may be
managed by specific functions. Irrationality can be
described in terms of what is known as the fuzzjective.
FUZZY LOGIC
