Neuro-fuzzy systems combine neural networks and fuzzy logic to utilize the advantages of both. The neuro-fuzzy system has the ability to self-learn and generate rules from data without expert knowledge. It consists of layers that perform fuzzification, rule evaluation, implication, aggregation, and defuzzification. Such a system can provide effective advisory and self-learning capabilities for small-scale economic problems where data is available but generalization and expertise are limited.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Fuzzy soft set approach in decision making plays a crucial role by using Dempster–Shafer theory of evidence. First, the uncertain degrees of several parameters are obtained via grey relational analysis that apply to calculate the grey mean relational degree. Secondly, a mass functions of different independent choices with several parameters have given according to the uncertain degree. Lastly, aggregate the choices into a collective choices, Dempster’s rule of evidence combination have been utilized. The aforesaid soft computing based method have been applied on decision making problem.
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
A hybrid intelligent system is one that combines at least two intelligent technologies.
For example, combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system.
Fuzzy logic and neural networks are natural complementary tools in building intelligent systems.
While neural networks are low-level computational structures that perform well when dealing with raw data.
fuzzy logic deals with reasoning on a higher level, using linguistic information acquired from domain experts
However, fuzzy systems lack the ability to learn and cannot adjust themselves to a new environment.
On the other hand, although neural networks can learn, they are opaque to the user.
Integrated neuro-fuzzy systems can combine the parallel computation and learning abilities of neural networks with the human-like knowledge representation and explanation abilities of fuzzy systems.
As a result, neural networks become more transparent, while fuzzy systems become capable of learning.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Fuzzy soft set approach in decision making plays a crucial role by using Dempster–Shafer theory of evidence. First, the uncertain degrees of several parameters are obtained via grey relational analysis that apply to calculate the grey mean relational degree. Secondly, a mass functions of different independent choices with several parameters have given according to the uncertain degree. Lastly, aggregate the choices into a collective choices, Dempster’s rule of evidence combination have been utilized. The aforesaid soft computing based method have been applied on decision making problem.
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
A hybrid intelligent system is one that combines at least two intelligent technologies.
For example, combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system.
Fuzzy logic and neural networks are natural complementary tools in building intelligent systems.
While neural networks are low-level computational structures that perform well when dealing with raw data.
fuzzy logic deals with reasoning on a higher level, using linguistic information acquired from domain experts
However, fuzzy systems lack the ability to learn and cannot adjust themselves to a new environment.
On the other hand, although neural networks can learn, they are opaque to the user.
Integrated neuro-fuzzy systems can combine the parallel computation and learning abilities of neural networks with the human-like knowledge representation and explanation abilities of fuzzy systems.
As a result, neural networks become more transparent, while fuzzy systems become capable of learning.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Artificial Neural Networks: Applications In ManagementIOSR Journals
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
X-TREPAN: A MULTI CLASS REGRESSION AND ADAPTED EXTRACTION OF COMPREHENSIBLE D...cscpconf
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees
from neural networks. We empirically evaluated the performance of the algorithm on a set of
databases from real world events. This benchmark enhancement was achieved by adapting
Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The
models are then compared with X-TREPAN for comprehensibility and classification accuracy.
Furthermore, we validate the experimentations by applying statistical methods. Finally, the
modified algorithm is extended to work with multi-class regression problems and the ability to
comprehend generalized feed forward networks is achieved.
Similar to A neuro fuzzy decision support system (20)
Leveraging Big Data to Manage Transport Operations (LeMO) project will address these issues by investigating the implications of the utilisation of such big data to enhance the economic sustainability and competitiveness of European transport sector.
Big Data and Harvesting Data from Social MediaR A Akerkar
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How to Create Map Views in the Odoo 17 ERPCeline George
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Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
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Digital Tools and AI for Teaching Learning and Research
A neuro fuzzy decision support system
1. Rajendra Akerkar1 and Priti Srinivas Sajja2
1Senior
S i Researcher, Vestlandsforsking, Norway
R h V tl d f ki N
2Associate Professor, Sardar Patel University, India
1 rak@vestforsk.no 2 priti_sajja@yahoo.com
2. NEURO-FUZZY COMPUTING
Neural Nets Fuzzy Logic
Knowledge
K l d Implicit, th
I li it the system cannot
t t Explicit,
E li it verification and
ifi ti d
Representation be easily interpreted or optimization easy and
modified (-) efficient (+++)
Trains itself by learning from None,
None you have to define
Trainability data sets (+++) everything explicitly (-)
Get “best of both worlds”:
f
Explicit Knowledge Representation from
Fuzzy Logic with Training Algorithms
from Neural Nets
3.
4. The neuro-fuzzy system has the ability of self-
learning and it can auto generate rules from data
historians other than from expert’s prior knowledge.
The i
Th aim of the rule extraction is to get rules in the
f th l t ti i t t l i th
following form from input–output data historians:
R(k) : If x1 is A1, x2 is A2 , …., xm is Am then Y is Yk
Where R(K) represents the kth rule;
e e ep ese ts t e u e; x1, x2, xm are
a e
input variables; Y is the output variable; A1, A2, Am
are input fuzzy sets; and YK is corresponding
output fuzzy sets.
p y
5.
6. Each neuron in the input layer, Layer 1, transmits
normalized values from the environment to the next layer.
Layer 2 is fuzzification layer Neurons in this layer
layer.
represent fuzzy sets used in the antecedents of fuzzy
rules. A fuzzification neuron receives a normalized input
from the previous layer and determines the degree to
which this input belongs to the neuron s fuzzy set. The
neuron’s set
activation function of a membership neuron is set to the
function that specifies the neuron’s fuzzy set.
Layer 3 is fuzzy rule layer Each neuron represents a fuzzy
layer.
rule. A fuzzy rule neuron receives inputs from the
fuzzification neurons of layer 2 that represent fuzzy sets in
the rule antecedents. For instance, neuron R1, which
corresponds to Rule 1, receives inputs from neurons A1
1
and B1. In a neuro-fuzzy system, intersection can be
implemented by the product operator or by Minimum
operator.
7. Layer 4 is output membership layer. Neurons in this
layer represent fuzzy sets used in the consequent of
fuzzy rules An output membership neuron
rules.
combines all its inputs by using the fuzzy operation
union. The probabilistic OR or Maximum operator
can implement this operation.
operation
Layer 5 is defuzzification layer. Each neuron in this
layer represents a single output of the neuro-fuzzy
l t i l t t f th f
system. It takes the output fuzzy sets clipped by the
respective integrated firing strengths and combines
them into a single ffuzzy set Ne ro f
set. Neuro-fuzzy s stems
systems
can apply standard defuzzification methods,
including techniques like centroid and sum-product
composition.
composition
8. Importance of small scale system in economy
Though data is available, generalization of
rules is difficult
Expertise i scarce resource
E i is
Requirement
◦ Need of effective advisory
◦ User interface
◦ Self-learning
9.
10. Example rules can be given as follows:
If qualification(-0.76), area(-1.6) and
dependents(1.9) then opt f more education.
d d (1 9) h for d i
If qualification(1 6) capital(-1.0), area( 2 9)
qualification(1.6), capital( 1 0) area(-2.9)
and dependents(0) then opt for job.
11.
12.
13.
14. Such advisory system can be more
generalized and can be considered as a step
towards expert system shell with empty
knowledge base and an additional editor to
document domain knowledge.