Download Neural Network and Fuzzy Logic

International Journal of Research in Engineering and Technology (IJRET) Vol. 2, No. 5, 2013 ISSN 2277 – 4378
Neural Network and Fuzzy Logic
1
Reshma H. Bonde, 2Shital D.Tatale, 3Rashmi V. Sawalakhe and 4Prashant C. Jikar
Abstract— In this topic, we introduce the basic concepts of
neural network is convinced by the Biological term Neurons. The
next concept is of human brain and artificial neurons. The application
of this neural network is also discussed. About fuzzy logic, we
discussed that, the fuzzy set theory exhibits immense potential for
effective solving of uncertainty in the problem. Fuzziness means
vagueness. It is the best tool to handle the uncertainty due to
vagueness.
II. BASIC CONCEPT OF NEURAL NETWORK
Neural network, which are simplified models of the
biological neuron system, is a massively parallel distributive
processing system made up of highly interconnected neural
computing elements that have the ability to learn the thereby
acquire knowledge and make it available for use.
The neural network to remain ‘plastic’ presented with
irrelevant information. This is known as stability- plasticity
dilemma.
Keywords— Neural Network, Fuzzy logic.
I. INTRODUCTION
A
RTIFICIAL Intelligence (AI) is an area of computer
science concerned with designing intelligent computer
system’ that is systems that exhibit the characteristics we
associated with intelligence in human behavior. Artificial
intelligence is the branch of computer science that is
concerned with automation of intelligent behavior. AI have
many of technologies, some of them are neural network, Fuzzy
logic, Cellular automata and probabilistic are prodomently
known as ‘soft computing’.
Neural network is simplified model of the Biological
nervous system and therefore have drawn their motivation
from the kind of computing performed by human brain.
Neural network adopt various learning mechanism of which
supervised learning and unsupervised learning methods have
turned out to be very popular.[1, 2] Neural network have been
successfully applied to problems in the field of pattern
recognition, image processing, data compression forecasting
and optimization to quote a few.
Neurons considered as a threshold units that fire when their
total input exceeds certain bias levels. There are many layers
are connected and each connection strength expressed by a
numerical value called a weight.
Fuzzy logic representations founded on fuzzy set theory try
to capture the way human represent and reason with real world
knowledge on the face of uncertainty. Uncertainty could arise
due to generality, vagueness, ambiguity, chance or incomplete
knowledge. Fuzzy logic has found extensive patronage in
consumer products especially promoted by the Japanese
companies and have found wide use in control systems, pattern
recognition applications and decision making to name a few.
Fig. 1. neuron (Carpenter & Gross Borg 188, 1988).
A. Structure of neuron in human brain.
The human brain is of the most complicated things which,
on the whole has been poorly understood. Brain contains about
1010 basic units called neurons. Each neuron in turn, is
connected to about 104 other neurons. A neuron is composed
of a nucleus- a cell body known as soma. Attached to the
some are long irregularly shaped filaments called dendrites.
Another type of link attached to the soma is the Axon. The
axon terminates in a specialized contact called synapse a
synaptic junction that connects axon with the dendrite links of
another neuron.
Reshma Haridas Bonde, Jawaharlal Darda Institute Of Engineering &
Technology Yavatmal, Maharastra, India.
Prashant C. Jikar, Mechanical Engg Dept., Jawaharlal Darda Institute Of
Engineering & Technology Yavatmal, Maharastra, India
Shital D.Tatale and Rashmi V. Sawalakhe, Jawaharlal Darda Institute Of
Engineering & Technology Yavatmal, Maharastra, India.
B. Model of an artificial neuron
Highly inter connected network of simple processing
elements called neurons. Every component of the model bears
a direct analogy to the actual constituents of a biological
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International Journal of Research in Engineering and Technology (IJRET) Vol. 2, No. 5, 2013 ISSN 2277 – 4378
neuron and hence tiered as artificial neuron. Artificial neural
network is an abstract stimulation of a rear nervous system the
contains a collection as neuron units communicating with
each other via axon connections. Such a model bears a strong
resemblance to axons and dendrites in the nervous system.
approach for explicit representation of the impreciseness in
human knowledge and problem solving techniques.
The basic building block of a fuzzy logic control system i8s
set of fuzzy if-then ruler that approximates a functional
mapping. [4]
C. Characteristics Of Neural Network.
1) The neural network exhibit mapping capabilities that is
they can map input patterns to their associated output
patterns.
2) The neural network learns by examples. Than neural
network architectures can be trained with known
examples of a problem before they are tested for their
inference capability an unknown instances of the
problem. They can therefore, identity new objects
previously untrained.
3) Neural network possess the capability to generalizes.
Thus, they can predict new outcomes from past trends.
4) The neural networks are robust system and are fault
tolerant. They can therefore recall full patterns from
incomplete, partial or noisy patterns.
5) The neural network can process information in parallel,
at high speed and in distributed manners.
IV. BASIC CONCEPT OF FUZZY LOGIC
The core technique of fuzzy logic is based on four basic
concepts.
1) Fuzzy sets: Sets with smooth boundaries.
2) Linguistic variables: Variables whose values are both
qualitatively and quantitatively described a fuzzy set.
3) Possibility distributions: Constrains on the value of a
linguistic variable imposes by arraigning it a fuzzy set
and.
4) Fuzzy if-then rules: A knowledge representation scheme
for describing a functional mapping on a logic formula
that generalizes an implication in two- valued logic.
V. APPLICATIONS OF NEURAL NETWORK
A. Linear programming Modeling network
The interconnected network of analog processors can be
used for the solution of contained optimization problems,
including the linear programming problem.
III. WHAT IS FUZZY LOGIC?
The term fuzzy logic has been used in two different senses.
In a narrow sense, fuzzy logic refers to a logical system that
generalizes classical two- valued logic for reasoning under
uncertainty. In a broad sense, fuzzy logic refers to all of the
theories and technologies that employ fuzzy sets, which are
classes with un sharp boundaries [5] For e.g. the concept of
‘warm room temperature” may be expressed as an interval in
classical set theory.
B. Character Recognition Networks
In this character recognition networks of both printed and
handwritten characters. Initial emphasis is placed on the
comparison of different learning techniques for feed forward
architectures. The handwritten character recognition task can
be successfully performed by unusual and unconventional
neural network designs.
C. Neural networks control application
The use of neural network log in control applicationsincluding process control, robotics, industrial manufacturing
and aerospace applications. The basic objective of control is
to movies the appropriate input signal to a given physical
process to guild its desired response. In control system. The
physical process to be controlled is called plant the plant up
signal called actuating signal. As in fig. 3 The neural newbased controller generates the actuating signals; the come can
be termed neural control, or neuron control.
Fig. 2 Classical and Fuzzy Set Representation
The idea of fuzzy sets wan born in July 1964 by prof. lofty
A. Zadeh. Fuzzy logic was motivated by two objectives. First
it aims to alleviate difficulties in developing and analyzing
complex system’ encountered by conventional mathematical
tools. Second, it is motivated observing that human reasoning
can utilize concepts and knowledge that do not have were
defined, sharp boundaries.
[3] Fuzzy logic achieves machine intelligence offering a
way for representing and reasoning about human knowledge
that is imprecise nature. Even though fuzzy logic is not the
only technique for developing AI systems. it is unique in its
Fig. 3 Control Problem
D. Network for Robot kinematics
Neural new models are commandeered for solving a no of
robot kinematics Moslem robot lineation involves the study of
the geometry of manipulation arm motions. [7]
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International Journal of Research in Engineering and Technology (IJRET) Vol. 2, No. 5, 2013 ISSN 2277 – 4378
5) Connectionist expert system for medical Diagnosis.
1) In skin discuses Diagnosis.
2) for low back pain diagnosis
3) For coronary occlusion
Diagnosis
condition involving multiple variables.
3) Compute the normalized machines degree.
4) Compute the conclusion inferred by a fuzzy rule.
5) Combine the conclusion of all fuzzy rules in a model.
VIII. NEURAL NETWORKS IMPLEMENTATION
VI. APPLICATIONS OF FUZZY LOGIC
Neuro computing concepts and applications are tested
through stimulations of neural algorithm on digital computers,
for this a very wide spectrum of computers called
programmable neuro computers can be used.
What could be called an ‘Ideal Neural network.
 Such a network should possess the following
characteristics.
 Work for any neural processing algorithm.
 Contains at least 1000 neurons, which can be
interconnected globally.
 Have programmable analog weights.
 Be able to learn on – Chip.
 Consist of small – area neurons and interconnections.
 Operate at low – power levels.
 Be stable, reproducible, and extendable so that larger
systems can be through interconnecting neural network
building blocks.
 Be affordable.
A. In control engineering
The concept of control problem and the manner in which it
is formulated and ultimately resolved are outlined. In this
connection it is crucial to recognize the role of informal means
in both formulation and resolution of control problem.[6]
B. In 21st Century
a) Fuzzy logic has been used to defect incidents on freeways
or urban streets.
b) It also used to controls vehicles on future intelligent
freeways. So that drivers can use the traveling time in a more
productive way.
c) Fuzzy logic used to synthesis type expert system to
describe imprecise constraints and fuzzy goals.
C. In mobile robot navigation
The use of fuzzy logic in behavioral approaches to mobile
robot navigation.
A. Fuzzy Implications
To focus on fuzzy implication for two reasons.
 It is the most commonly used reasoning scheme in
application of fuzzy logic.
 There is not a unique definition of fuzzy implications.[9].
D. In emotional intelligent agent
Fuzzy logic can be used to model emotions and related
concepts such that n agent can demonstrate a certain degree of
emotional intelligence.
E. Application in medical Image segmentation.
a) The foundation of the FCM algorithm is to optimize an
objective function that mesas the compactness & the
separation of luster formed.
b) Fuzzy pattern recognition techniques can be applied to a
segmentation of human brain MR images with abnormal
tissue.[8]
IX. CONCLUSION
From the above basic study of neural network and fuzzy
logic are concludes that data and feedback, however
understanding the knowledge on the pattern learned by the
neural networks has been difficult. More especially, it is
difficult to develop an insight about the meaning associated
with each neuron and each weight. Hence neural network are
often viewed as a ‘Black Box’.
Fuzzy logic is very useful various fields. Due to fuzzy logic
truth values are multi valued such as absolutely true,
absolutely false, very true and so on and are numerically
equivalent to (0-1).
VII. COMBINATION OF NEURAL NETWORK & FUZZY LOGIC
Neural network & fuzzy logic are two complementary
technologies. The combination of both new created new term
neuro-fuzzy system. Neuro- fuzzy systems can be classified
into three categories.
1) A fuzzy rule- based model constructed using a supervised
neural network learning technique.
2) A fuzzy rule-based model constructed using reinforcement
–based learning.
3) A fuzzy rule- based model using neural network to
construct its fuzzy partition of the input space.
A neuro – fuzzy architecture has five layers of node. The
functionalities associated with different layers usually include
the following.
1) Compute the matching degree to a fuzzy condition
involving one variable.
2) Compute the matching degree of a conjunctive fuzzy
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