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A SHORT PRESENTATION ON:

APPLICATIONS OF FUZZY

LOGIC IN VARIOUS FIELDS,

ESPECIALLY IN THE FIELD

OF WATER RESOURCE

MANAGEMENTPresented By :- Maikel Das,

School of Hydro-Informatics Engineering,NIT Agartala.

overview

INTRODUCTION

BACKGROUND

APPLICATION

A BRIEF HISTORY ON THE CONCEPT OF LOGICS AND THE ADVENT OF FUZZY LOGIC IN THE MODERN DAY APPLICATIONS

384-322 BC 1917 599–527 BC

Anekāntavāda: This principle is stated by observing that objects are infinite in their qualities and modes of existence, so they cannot be completely grasped in all aspects and manifestations by finite human perception. The origins of Anekāntavāda can be traced back to the teachings of Mahāvīra.

Polish logician and philosopher Jan Lukasiewicz proposed the three-valued logic : True, False and Possible. This principle is known as the “law of excluded middle”.Classical logic of Aristotle: Law of Non-Contradiction: “Every proposition is either True or False(no middle)”

1973 19851965

The concept of Fuzzy Logic (FL) was conceived by Lotfi Zadeh, a professor at the University of California at Berkley, and presented not as a control methodology, but as a way of processing data by allowing partial set membership rather than crisp set membership or non-membership.

The Fuzzy approach to set theory applied to control systems. This was not possible until the 70's due to insufficient small-computer capability prior to that time. Professor Zadeh reasoned that people do not require precise, numerical information input, and yet they are capable of highly adaptive control. If feedback controllers could be programmed to accept noisy, imprecise input, they would be much more effective and perhaps easier to implement.

Multiple Criteria Decision Making (MCDM) was developed by Zimmerman. MCDM is the optimal choice with different type depended on decision makers’ preference, sorted of Multiple Objective Decision Making and Multiple Attribute Decision Making (MADM). Hwang and Yoon provided that MCDM is a possible evaluation scale for many characters or quantities of decision makers’ evaluation. It could be determined by advantage or ranking.

SINCE THEN – TODAY AND BEYOND1987

Today, almost every intelligent machine has fuzzy logic technology inside it. But fuzzy logic doesn't only help boast machine IQs. It also plays an important role in numerous other fields of applications, including Water Resources Management, GIS and lots more. The concept that everything is not just ‘good’ or ‘bad’ also helps us to see the good things in other people or things.

The first subway system was built which worked with a fuzzy logic-based automatic train operation control system in Japan. It was a big success and resulted in a fuzzy boom.

INTRODUCTION

What is Fuzzy Logic?

Fuzzy logic provides a method to formalize reasoning when dealing with vague terms. Traditional computing requires finite precision which is not always possible in real world scenarios. Not every decision is either true or false, or as with Boolean logic either 0 or 1. Fuzzy logic allows for membership functions, or degrees of truthfulness and falsehoods. Or as with Boolean logic, not only 0 and 1 but all the numbers that fall in between.

What are Fuzzy Sets?

In classical set theory, a set is defined by a characteristic (membership) function that assigns each element a degree of membership: either 0 (the element is not member of the set) or 1 (the element is member of the set). Fuzzy sets generalize classical (crisp) sets and the degree of membership to a fuzzy set can take any value in the real unit interval [0, 1].

The essential characteristics of fuzzy logic as founded by Lotfi Zadeh

are as follows:

In fuzzy logic, exact reasoning is viewed as a limiting case of approximate reasoning

In fuzzy logic everything is a matter of degree

Any logical system can be fuzzified

In fuzzy logic, knowledge is interpreted as a collection of elastic or equivalently , fuzzy constraint on a collection of variables

Inference is viewed as a process of propagation of elastic constraints

MAIKEL

The third statement hence, define Boolean logic as a subset of Fuzzy logic.

Fuzzy Linguistic Variables

Fuzzy Linguistic Variables are used to represent qualities spanning a particular spectrum

Temp: {Freezing, Cool, Warm, Hot}

50 70 90 1103010

Temp. (F°)

Freezing Cool Warm Hot

0

1

FUZZY APPLICATIONS

APPLICATIONS OF FUZZY LOGIC

Drainage management problems are usually very hard to simulate due to the uncertainty of the hydraulic parameters involved. Fuzzy analysis is one of the available tools that can be used for such problems, involving uncertain data. A fuzzy analysis approach usually involves the consideration of several α-level cuts and an analytical approach or an explicit scheme approach for the PDE's discretization.


Neural network and fuzzy logic have been successfully applied to a wide range of problems covering a variety of sectors. Their practical applications, especially of neural networks expanded enormously starting from mid 80s till 90s partly due to a spectacular increase in computing power. During the last decade ANN evolved from being only a research tool into a tool that is applied to many real world problems: physical system control, various engineering problems, statistics, medical and biological fields. Consequently they are applied more and more in water management field as well.


Fuzzy-based methods are applied successfully for identifying optimal control actions of wastewater treatment plant, determining optimal dosage thereof and determining leakage. They often are used in combination with the expert knowledge. Fuzzy rule-based systems (FRBS) (capable of building rules automatically) have been applied for drought prediction,determining optimal control action of polder pumping station and filling in gaps in the measured data.


Database systems are one of the mostly used sources of information that keep the vast amount of information. We have to be able to process and convert it into a form that meets the specific information needs. And this is a problem because the current search techniques use "real values” questions. The result is short or long list of objects which satisfy the conditions. This list is to be re-evaluated unless is worthless. One of the possible solutions is to use fuzzy logic in searching databases.


Mpimpas (1998) worked on arithmetic investigation of polluting elements dispersion using fuzzy logic, Ganoulis (2000) solved an aquifer recharge problem with infiltration where the diffusion coefficient was not precisely known. Chalkidis (2005) and Chalkidis et al. (2006) worked on aquifer management problems using fuzzy logic and fuzzy linear programming. Mpallas (2007) worked on management and hydrological problems using fuzzy rules, and Tzimopoulos et al. (2004) worked on two dimensional unsteady flow using fuzzy logic. Tzimopoulos et al. (2005) presented a quasi similar and shortened version of this paper, using fuzzy logic.

I.N. Halkidis, Ch.D. Tzimopoulos1, Ch.H. Evangelides, M. Sakellarioy-Makrantonaki (2009) Soil water management problem using fuzzy arithmetic, Global NEST Journal, Vol 11, No 4, pp 556-565


In many image processing applications, expert knowledge must be used for applications such as object recognition and scene analysis. Fuzzy set theory and fuzzy logic provide powerful tools to represent and process human knowledge in form of fuzzy IF-THEN rules. Many difficulties in image processing are fuzzy in nature and for the solution of these problems, fuzzy approaches can be used.


Wind energy converters can be enhanced with a fuzzy system based on human experience to find the best compromise to the trade-off between efficiency, safety and wear of the wind energy converter.


In recent years, fuzzy logic has proven well its broad potential in industrial automation applications. Some of the most popular applications in this field are:

1. Anti-Sway Control of Cranes 2. Fire Zone Control in Waste Incineration Plants 3. Control of Tunnel Inspection Robots 4. Positioning in Presses 5. Temperature Control in Plastic Molding Machines 6. Climate Control and Building Automation 7. Fuzzy Logic Supervisory Control for Coal Power

Plant

ADVANTAGES

Mimics human control logic Tolerant of imprecise data Inherently robust Fails safely Modified and tweaked easily Flexible Conceptually easy to understand Fuzzy logic is based on natural language

DISADVANTAGES

It would be remarkable if a theory as far-reaching as fuzzy systems did not arouse some objections in the professional community. There have been generic complaints about the "fuzziness" of the process of assigning values to linguistic terms.

Some of the objections that faced fuzzy logic in its early days are shown below.

"Fuzzy theory is wrong, wrong, and pernicious. What we need is more logical thinking, not less. The danger of fuzzy logic is that it will encourage the sort of imprecise thinking that has brought us so much trouble. Fuzzy logic is the cocaine of science."

-Professor William Kahan, (UC Berkeley)

"’Fuzzification is a kind of scientific permissiveness. It tends to result in socially appealing slogans unaccompanied by the discipline of hard scientific work and patient observation." -Professor Rudolf Kalman, University of Florida

"Fuzziness is probability in disguise. I can design a controller with probability that could do the same thing that you could do with fuzzy logic."

-Professor Myron Tribus, on hearing of the fuzzy-logic control of the Sendai subway system IEEE Institute, may 1988.

Some other inherent drawbacks of the Fuzzy Logic approach are listed below:

Fuzzy logic is not always accurate. The results are perceived as a guess, so it may not be as widely trusted .

Requires tuning of membership functions which is difficult to estimate.

Fuzzy Logic control may not scale well to large or complex problems

Fuzzy logic can be easily confused with probability theory, and the terms used interchangeably. While they are similar concepts, they do not say the same things.

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CONCLUSION


Fuzzy Logic provides way to calculate with imprecision and vagueness.


Fuzzy logic provides an

alternative way to represent

linguistic and subjective

attributes of the real world in

computing. It is able to be

applied to control systems and

other applications in order to

improve the efficiency and

simplicity of the design

process.


The control stability, reliability, efficiency, and durability of fuzzy logic makes it popular. The speed and complexity of application production would not be possible without systems like fuzzy logic.


Fuzzy logic is not the answer to everything. It may be overkill in many places however it simplifies the design of many more complicated cases. If a simple closed loop or PID controller works fine then there is no need for a fuzzy controller. There are many cases when tuning a PID controller or designing a control system for a complicated system is overwhelming, this is where fuzzy logic gets its chance to shine.


As man gets hungry in finding new ways of improving our way of life, new, smarter machines must be created.


Fuzzy logic provides a simple and efficient way to meet these demands and the future of it is limitless.

THANKING YOU ALL ! ● ● ● ●

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