dharmu ppt 2

8/4/2019 Dharmu Ppt 2

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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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dharmu ppt 2

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