Fuzzy Logic

Fuzzy Logic is successfully used in today's process control systems. Fuzzy logic addresses such applications perfectly as it resembles human decision making with an ability to generate precise solutions from uncertain or approximate information. It fills an important gap in engineering design methods left by mathematical and logic-based approaches.

While other approaches require accurate equations to model real-world behaviors, fuzzy design can accommodate the ambiguities of human languages and logics. It provides both an intuitive method for describing systems in human terms and automates the conversion of those system specifications into effective models.

Natural language is full with vague and imprecise concepts such as "Peter is tall," or "It is very hot today." Such statements are difficult to translate into more precise language without losing some of their semantic value: for example, the statement "Peter's height is 175cm" does not explicitly state that he is tall, and the statement "Peter's height is 1.4 standard deviations about the mean height for men of his age in his country" is fraught with difficulties: would a man 1.3999999 standard deviations above the mean be tall? Which country does Peter belong to, and how is membership in it defined?

While it might be argued that such vagueness is an obstacle to clarity of meaning, only the most staunch traditionalists would hold that there is no loss of richness of meaning when statements such as "Peter is tall" are discarded from a language. Yet this is just what happens when one tries to translate human language into classic logic. Such a loss is not noticed in the development of a financial program,

but when one wants to allow for natural language queries, or "knowledge representation" in expert systems, the meanings lost are often those being searched for.

The notion central to fuzzy systems is that truth values (in fuzzy logic) or membership values (in fuzzy sets) are indicated by a value on the range 0 to 1, with 0 representing absolute Falseness and 1 representing absolute Truth.

While other approaches require accurate equations to model real-world behaviors, fuzzy design can accommodate the ambiguities of human languages and logics. It provides both an intuitive method for describing systems in human terms and automates the conversion of those system specifications into effective models.

Natural language is full with vague and imprecise concepts such as "Peter is tall," or "It is very hot today." Such statements are difficult to translate into more precise language without losing some of their semantic value: for example, the statement "Peter's height is 175cm" does not explicitly state that he is tall, and the statement "Peter's height is 1.4 standard deviations about the mean height for men of his age in his country" is fraught with difficulties: would a man 1.3999999 standard deviations above the mean be tall? Which country does Peter belong to, and how is membership in it defined?

While it might be argued that such vagueness is an obstacle to clarity of meaning, only the most staunch traditionalists would hold that there is no loss of richness of meaning when statements such as "Peter is tall" are discarded from a language. Yet this is just what happens when one tries to translate human language into classic logic. Such a loss is not noticed in the development of a financial program,

but when one wants to allow for natural language queries, or "knowledge representation" in expert systems, the meanings lost are often those being searched for.

The notion central to fuzzy systems is that truth values (in fuzzy logic) or membership values (in fuzzy sets) are indicated by a value on the range 0 to 1, with 0 representing absolute Falseness and 1 representing absolute Truth.

A fuzzy logic controller is usually given a set of rules which look like:

- if you see A, do X

- if you see B, do Y

- if you see C, do Z.

- if you see B, do Y

- if you see C, do Z.

The fuzzy controller will look at the situation. In one case, it may see something which has a content of all A, B and C, but not 100% of each. For example it may see a situation which resembles 30% of A, 10% of B and 40% of C. It will therefore tell itself that it needs to come up with an output which is 30% of X, 10% of Y and 40% of Z. The combination of these outputs will be its final output.

Advantages of Fuzzy Logic Control

Advantages of Fuzzy Logic Control

- Useful when we are not very sure of the process model.

- Many commercial packages are available in DCS Systems

- Many commercial packages are available in DCS Systems

Disadvantages of Fuzzy Logic Control

- Prior knowledge is required. If a case is missed, the controller would not work properly.

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