Aisha Iqbal (CT-084) Kanwal Hakeem (CT-098) Tehreem Mushtaq (CT-078) Talha Syed (CT-111)

Origin What is FL? General Definition FL Representation FL vs. Control Methods Fuzzy Operators

Fuzzy Logic Process Why Use FL? Role of FL in A.I Applications Conclusion

Fuzzy Logic Lotfi Zadeh, Berkely. The concept of Fuzzy Logic 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.

He 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.

Definition of fuzzy Fuzzy –“not clear; not precise; indistinct; blurred” Definition of fuzzy logic A form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts.

Superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth. Central notion of fuzzy systems is that truth values (in fuzzy logic) or membership values (in fuzzy sets) are indicated by a value on the range [0.0, 1.0], with 0.0 representing absolute Falseness and 1.0 representing absolute Truth. Deals with real world vagueness.

Not just “True” and “False.” Takes on a range of values  True  Mostly True  Half True  Kind of True False Values range from 0 to 1.  Including decimal values (0.2, 0.7, etc.) Fuzzy number can be represented on a computer as a number between 0 and 255. Slowest [0.0 – 0.25] Slow [0.25 – 0.50] Fast [0.50 – 0.75] Fastest [0.75 – 1.00]

FL incorporates a simple, rule-based IF X AND Y THEN Z approach to a solving control problem rather than attempting to model a system mathematically. The FL model is empirically-based, relying on an operator's experience rather than their technical understanding of the system.

For example, rather than dealing with temperature control in terms such as "T <1000F", or "210C <TEMP <220C", terms like "IF (process is too cool) AND (process is getting colder) THEN (add heat to the process)" or "IF (process is too hot) AND (process is heating rapidly) THEN (cool the process quickly)" are used. These terms are imprecise and yet very descriptive of what must actually happen. Consider what you do in the shower if the temperature is too cold: you will make the water comfortable very quickly with little trouble. FL is capable of mimicking this type of behavior but at very high rate.

Fuzzy Logic use 3 operators: AND OR NOT Let’s consider 2 values A & B: A B

FAND(A,B) - Fuzzy AND = min(A,B) FAND( 100, 30 ) = 30 FAND( 20, 250 ) = 20 A  B

FOR(A,B) - Fuzzy OR = max(A,B) FOR( 100, 30 ) = 100 FOR( 20, 250 ) = 250 A  B

FNOT(A) = 100% - A (100% defined as 255) FNOT( 100 ) = 155 FNOT( 250 ) = 5 FNOT( 255 ) = 0 FNOT( 0 ) = 255 AA

Fuzzification  Scales and maps input variables to fuzzy sets Inference Mechanism  Approximate reasoning  Deduces the control action Defuzzification  Convert fuzzy output values to control signals

The fuzzification comprises the process of transforming crisp values into grades of membership for linguistic terms of fuzzy sets. The membership function is used to associate a grade to each linguistic term.

The core section of a fuzzy system is this part, which combines the facts obtained from the fuzzification with the rule base and conducts the fuzzy reasoning process. This is called a fuzzy inference machine.

As a result of applying the previous steps, one obtains a fuzzy set from the reasoning process that describes, for each possible value, how reasonable it is to use this particular value. Using a fuzzy system as a controller, one wants to transform this fuzzy information into a single value that will actually be applied. This transformation from a fuzzy set to a crisp number is called a defuzzification. It is not a unique operation as different approaches are possible.

Fuzzy logic is perhaps the most promising advancement to come along in the Artificial Intelligence community in recent history. What exactly lies behind the term "fuzzy", what can fuzziness bring to the advancement of AI and what would fuzzy AI mean for the future?

Fuzzy logic in it's simplest terms expands the dicotomy of true or not true to include a range of answers in between. The usual example is say instead of being black or white, fuzziness allows for shades of gray. Since fuzzy logic allows this extra bandwidth in fuzzy answers, fuzzy rules used in programming can cover a much broader area.

A fuzzy rule such as "When it rains, you get wet"*** can cover a lot of ground. It would be able to several instantions of itself such as "when it rains a lot, you get wet a lot" or "when it rains a little, you get wet a little". Rules like this are beautiful because they are human rules. They are a much better model of how we think.

FL was conceived as a better method for sorting and handling data but has proven to be a excellent choice for many control system applications since it mimics human control logic. It can be built into anything from small, hand-held products to large computerized process control systems. It uses an imprecise but very descriptive language to deal with input data more like a human operator.

It is very robust and forgiving of operator and data input and often works when first implemented with little or no tuning. Fuzzy logic enables a computer to make decisions which care more in line with the sort of decisions which a human would make.

ABS Brakes Expert Systems Temperature Controller Bullet train between Tokyo and Osaka Video Cameras Automatic Transmissions

A simple and effective fuzzy logic controller is designed for an Anti-Lock Braking System (ABS) to improve the braking performance and directional stability of an automobile during braking, and steering-braking manoeuvres on uniform and nonuniform (µ-split) friction surfaces. The system consists of two controllers working in tandem.

Put as simply as possible, a fuzzy expert system is an expert system that uses fuzzy logic instead of Boolean logic. In other words, a fuzzy expert system is a collection of membership functions and rules that are used to reason about data.

The problem Change the speed of a heater fan, based on the room temperature and humidity. A temperature control system has four settings. Cold, Cool, Warm, and Hot Humidity can be defined by: Low, Medium, and High

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.