dharmu ppt 2
TRANSCRIPT
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PRESENTED BY
DHARMENDRA MAHAPATRA
ROLL NO-0701101250CIVIL ENGINEERING
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INTRODUCTION
Neuro fuzzy is combination of Neural Network and Fuzzy logic.
Neuro-fuzzy hybridization results in a hybrid intelligent .
system that synergizes these two techniques by combining the
human-like reasoning style of fuzzy systems with the learning
and connectionist structure of neural networks.
Flood forecasting is very important for flood control and
mitigation. It can effectively provide advance information for
flood warning to people who are living in flood prone areas.
The accuracy of flood forecast is evaluated by using statisticalefficiency index (EI), root mean square error (RMSE) and mean
absolute error (MAE).
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FUZZY LOGIC
Fuzzy inference is a powerful problem-solving
methodology with wide applications in industrial
control and information processing.
It provides a simple way to draw definite conclusions
from vague, ambiguous or imprecise information.
It resembles human decision making with its ability to
work from approximate data and find precise
solutions.
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WORKING PROCEDURE OFFUZZY LOGIC
It uses three simple rules:-
Fuzzification-to convert numeric data
aggregation (rule firing)-computation of fuzzy numbers
defuzzification - convert the obtained fuzzy number
back to the numeric data
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FUZZY LOGIC ADVANTAGES
Mimic human decision making to handle vague
concepts.
Rapid computation due to intrinsic parallel processing
nature.
Ability to deal with imprecise or imperfect information
Improved knowledge representation and uncertainty
reasoning. Modeling of complex, non-linear problems.
Natural language processing/programming capability.
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LIMITATION OF FUZZY LOGIC
highly abstract and heuristic.
need experts for rule discovery (data relationships).
lack of self-organizing & self-tuning mechanisms of
NN.
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NEURAL NETWORK
information processing paradigm inspired by biological
nervous systems, such as our brain.
Structure: large number of highly interconnected
processing elements (neurons) working together.
Neural networks are configured for a specificapplication, such as pattern recognition or data
classification, through a learning process .
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ADVANTAGES OF NEURAL NETWORK
no need to know data relationships
self-learning capability
self-tuning capability
applicable to model various systems
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LIMITATION OF NEURAL NETWORK
unable to handle linguistic information
unable to manage imprecise or vague information
unable to resolve conflicts
unable to combine numeric data with linguistic or
logical data
difficult to reach global minimum even by complex BP
learning rely on trial-and-errors to determine hidden layers and
nodes
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NEURO FUZZY TECHNIQUES
handle any kind of information (numeric, linguistic,
logical, etc.).
manage imprecise, partial, vague or imperfect
information. resolve conflicts by collaboration and aggregation.
self-learning, self-organizing and self-tuning
capabilities.
no need of prior knowledge of relationships of data.
mimic human decision making process.
fast computation using fuzzy number operations.
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NEURO FUZZY MODEL
It is the way of applying various learning techniques
developed in the neural network literature to fuzzy
modeling or to a fuzzy inference system (FIS).
Fuzzy Interface System:-
A rulebase, which contains a selection of fuzzy rules.
A database which defines the membership functions
used in the fuzzy rules. A reasoning mechanism, which performs the inference
procedure upon the rules to derive an output.
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FIS STRUCTUREWITH CRISP OUTPUT
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NEURO FUZZY SYSTEMS
Neuro - fuzzy systems are developed based on the
concept of neural methods on fuzzysystems.
Types of Neuro Fuzzy system:-
Takagi Sugano and Kang (TSK) fuzzy mode.
Adaptive Neuro Fuzzy Interface System(ANFIS).
Fuzzy Adaptive Learning Control Network (FALCON).
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TAKAGI SUGANO AND KANG (TSK) FUZZY
MODEL
It is an effort to formalize a system approach to
generating fuzzy rules from an input-output data set.
If x is A and y is B, then z = f(x, y)where A, B are fuzzy sets in the above; z = f(x, y)
is a crisp function in the consequent.
If f(x, y) is a constant, lead to the zero-order TSK fuzzymodel.
If f(x, y) is a first-order polynomial, lead to the first-
order TSK fuzzy model.
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ANFIS MODEL
An ANFIS network maps inputs through input membership
functions and associated parameters, and then through output
membership functions and associated parameters to outputs,
can be used to interpret the input/output map.
Fig. 2 illustrates the ANFIS architecture:-
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MECHANISM OF ANFIS
Layer 1: Every node in this layer is an adaptive node with a node function
where x is the input to node i, A(i) is the linguistic label (small, large, etc.)
associated with this node function and i is the MF of A(i). Usually A(x) is
chosen to have a bell-shaped asF
ig. 2 with a maximum equal to 1 and aminimum equal to 0, i.e.,
where {a, b, c} is the premise parameters
Layer 2: Every node in this layer is a fixed node labeled P, whose output
is the product of all incoming signals:
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CONTD
Layer 3: Every node in this layer is a fixed node labeled N. The i(th) node
calculates the ratio of the i(th) rules firing strength to the sum of all rules firing
strengths:
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CONTD
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STUDY AREA
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MODEL APPLICATION
Using the first order Takagi Sugano and Kang fuzzy
model so the consequent part of fuzzy if then rules
is linear equation
T norms operations that performs algebraic fuzzyoperation AND
The type of MF used in bell function defined in above
equation.
The algorithm for update the MF parameters is back
propagation.
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RESULTS
The ANFIS model is applied to forecast the daily discharge at the gaugingstation Y17 and Y6 in the Baitarani river basin.
EI =SR/ST
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CONCLUSION
This paper presents the application of NFT in
daily flood forecasting with promising results. It was
shown that NFT has better capability and performance
compared to ANN . It indicates that NFT model withthe knowledge contain infuzzy if then rule sets
obtained from ANN is adaptive to flood forecasting
better than ANN itself. The model accuracy decreases
when the time of forecasting ahead is increased.Compared to ANN, NFT is better in term of accuracy
and computing time.
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REFERENCES
Engineering Hydrology by P.C. Nayak,*, K.P. Sudheer,
D.M. Rangan, K.S. Ramasastri,
J.S Jang, ANFIS; Adaptive Network based Fuzzy
Inference System, IEEE Transactionson Systems,Man,and Cybernetics, Vol. 23 No.3, (1993), pp 665-
684.
H. X. Li, C. L Philip Chen, Hang-Pang Huang, Fuzzy
Neural Intelligent Systems,1st edition, CRC Press,
(2001).
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