25-08-2017, 09:32 PM
ARTIFICIAL INTELLIGENCE tECHNIQUES In POWERSYSTEMS
[attachment=50264]
ABSTRACT
This paper reviews five artificial intelligence tools that are most applicable to engineering problems fuzzy logic, neural networks and genetic algorithms. Each of these tools will be outlined in the paper together with examples of their use in different branches of engineering.
INTRODUCTION
Artificial intelligence emerged as a computer science discipline in the mid 1950s. Since then, it has produced a number of powerful tools, many of which are of practical use in engineering to solve difficult problems normally requiring human intelligence. Three of these tools will be reviewed in this paper. They are: fuzzy logic, neural networks and genetic algorithms. All of these tools have been in existence for more than 30 years and have found applications in engineering. Recent examples of these applications will be given in the paper, which also presents some of the work at the Cardiff Knowledge-based Manufacturing center, a multi-million pound research and technology transfer center created to assist industry in the adoption of artificial intelligence in manufacturing.
INTRODUCTION
Fuzzy logic has rapidly become one of the most successful of today's technologies for developing sophisticated control systems. The reason for which is very simple. Fuzzy logic addresses such applications perfectly as it resembles human decision making with an ability to generate precise solutions from certain or approximate information. It fills an important gap in engineering design methods left vacant by purely mathematical approaches (e.g. linear control design), and purely logic-based approaches (e.g. expert systems) in system design.
While other approaches require accurate equations to model real-world behaviors, fuzzy design can accommodate the ambiguities of real-world human language and logic. It provides both an intuitive method for describing systems in human terms and automates the conversion of those system specifications into effective models.
As the complexity of a system increases, it becomes more difficult and eventually impossible to make a precise statement about its behavior, eventually arriving at a point of complexity where the fuzzy logic method born in humans is the only way to get at the problem.
(Originally identified and set forth by Lotfi A. Zadeh, Ph.D., University of California, Berkeley)
Fuzzy logic is used in system control and analysis design, because it shortens the time for engineering development and sometimes, in the case of highly complex systems, is the only way to solve the problem.
The first applications of fuzzy theory were primarily industrial, such as process control for cement kilns. However, as the technology was further embraced, fuzzy logic was used in more useful applications. In 1987, the first fuzzy logic-controlled subway was opened in Sendai in northern Japan. Here, fuzzy-logic controllers make subway journeys more comfortable with smooth braking and acceleration. Best of all, all the driver has to do is push the start button! Fuzzy logic was also put to work in elevators to reduce waiting time. Since then the applications of Fuzzy Logic technology have virtually exploded, affecting things we use everyday.
HISTORY
The term "fuzzy" was first used by Dr. Lotfi Zadeh in the engineering journal, "Proceedings of the IRE," a leading engineering journal, in 1962. Dr. Zadeh became, in 1963, the Chairman of the Electrical Engineering department of the University of California at Berkeley.
The theory of fuzzy logic was discovered. Lotfi A. Zadeh, a professor of UC Berkeley in California, soon to be known as the founder of fuzzy logic observed that conventional computer logic was incapable of manipulating data representing subjective or vague human ideas such as "an attractive person" or "pretty hot". Fuzzy logic hence was designed to allow computers to determine the distinctions among data with shades of gray, similar to the process of human reasoning. In 1965, Zadeh published his seminal work "Fuzzy Sets" which described the mathematics of fuzzy set theory, and by extension fuzzy logic. This theory proposed making the membership function (or the values False and True) operate over the range of real numbers [0.0, 1.0]. Fuzzy logic was now introduced to the world.
Although, the technology was introduced in the United States, the scientist and researchers there ignored it mainly because of its unconventional name. They refused to take something, which sounded so child-like seriously. Some mathematicians argued that fuzzy logic was merely probability in disguise. Only stubborn scientists or ones who worked in discrete continued researching it.
While the US and certain parts of Europe ignored it, fuzzy logic was accepted with open arms in Japan, China and most Oriental countries. It may be surprising to some that the world's largest number of fuzzy researchers is in China with over 10,000 scientists. Japan, though currently positioned at the leading edge of fuzzy studies falls second in manpower, followed by Europe and the USA. Hence, it can be said that the popularity of fuzzy logic in the Orient reflects the fact that Oriental thinking more easily accepts the concept of "fuzziness". And because of this, the US, by some estimates, trail Japan by at least ten years in this forefront of modern technology.
UNDERSTANDING FUZZY LOGIC
Fuzzy logic is the way the human brain works, and we can mimic this in machines so they will perform somewhat like humans (not to be confused with Artificial Intelligence, where the goal is for machines to perform EXACTLY like humans). Fuzzy logic control and analysis systems may be electro-mechanical in nature, or concerned only with data, for example economic data, in all cases guided by "If-Then rules" stated in human language.
The Fuzzy Logic Method
The fuzzy logic analysis and control method is, therefore:
1. Receiving of one, or a large number, of measurement or other assessment of conditions existing in some system we wish to analyze or control.
2. Processing all these inputs according to human based, fuzzy "If-Then" rules, which can be expressed in plain language words, in combination with traditional non-fuzzy processing.
3. Averaging and weighting the resulting outputs from all the individual rules into one single output decision or signal which decides what to do or tells a controlled system what to do. The output signal eventually arrived at is a precise appearing, defuzzified, "crisp" value.
Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth- truth-values between "completely true" and "completely false". As its name suggests, it is the logic underlying modes of reasoning which are approximate rather than exact. The importance of fuzzy logic derives from the fact that most modes of human reasoning and especially common sense reasoning are approximate in nature.
The essential characteristics of fuzzy logic as founded by Zadeh Lotfi 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.
The third statement hence, defines Boolean logic as a subset of Fuzzy logic.
Professor Lofti Zadeh at the University of California formalized fuzzy Set Theory in 1965. What Zadeh proposed is very much a paradigm shift that first gained acceptance in the Far East and its successful application has ensured its adoption around the world.
A paradigm is a set of rules and regulations, which defines boundaries and tells us what to do to be successful in solving problems within these boundaries. For example the use of transistors instead of vacuum tubes is a paradigm shift - likewise the development of Fuzzy Set Theory from conventional bivalent set theory is a paradigm shift.
Bivalent Set Theory can be somewhat limiting if we wish to describe a 'humanistic' problem mathematically.
The whole concept can be illustrated with this example. Let's talk about people and "youthness". In this case the set S (the universe of discourse) is the set of people. A fuzzy subset YOUNG is also defined, which answers the question "to what degree is person x young?" To each person in the universe of discourse, we have to assign a degree of membership in the fuzzy subset YOUNG. The easiest way to do this is with a membership function based on the person's age.
Young (x) = {1, if age (x) <= 20,
(30-age (x))/10, if 20 < age (x) <= 30,
0, if age (x) > 30}
a graph of this looks like:
Given this definition, here are some example values:
Person Age degree of youth
[attachment=50264]
ABSTRACT
This paper reviews five artificial intelligence tools that are most applicable to engineering problems fuzzy logic, neural networks and genetic algorithms. Each of these tools will be outlined in the paper together with examples of their use in different branches of engineering.
INTRODUCTION
Artificial intelligence emerged as a computer science discipline in the mid 1950s. Since then, it has produced a number of powerful tools, many of which are of practical use in engineering to solve difficult problems normally requiring human intelligence. Three of these tools will be reviewed in this paper. They are: fuzzy logic, neural networks and genetic algorithms. All of these tools have been in existence for more than 30 years and have found applications in engineering. Recent examples of these applications will be given in the paper, which also presents some of the work at the Cardiff Knowledge-based Manufacturing center, a multi-million pound research and technology transfer center created to assist industry in the adoption of artificial intelligence in manufacturing.
INTRODUCTION
Fuzzy logic has rapidly become one of the most successful of today's technologies for developing sophisticated control systems. The reason for which is very simple. Fuzzy logic addresses such applications perfectly as it resembles human decision making with an ability to generate precise solutions from certain or approximate information. It fills an important gap in engineering design methods left vacant by purely mathematical approaches (e.g. linear control design), and purely logic-based approaches (e.g. expert systems) in system design.
While other approaches require accurate equations to model real-world behaviors, fuzzy design can accommodate the ambiguities of real-world human language and logic. It provides both an intuitive method for describing systems in human terms and automates the conversion of those system specifications into effective models.
As the complexity of a system increases, it becomes more difficult and eventually impossible to make a precise statement about its behavior, eventually arriving at a point of complexity where the fuzzy logic method born in humans is the only way to get at the problem.
(Originally identified and set forth by Lotfi A. Zadeh, Ph.D., University of California, Berkeley)
Fuzzy logic is used in system control and analysis design, because it shortens the time for engineering development and sometimes, in the case of highly complex systems, is the only way to solve the problem.
The first applications of fuzzy theory were primarily industrial, such as process control for cement kilns. However, as the technology was further embraced, fuzzy logic was used in more useful applications. In 1987, the first fuzzy logic-controlled subway was opened in Sendai in northern Japan. Here, fuzzy-logic controllers make subway journeys more comfortable with smooth braking and acceleration. Best of all, all the driver has to do is push the start button! Fuzzy logic was also put to work in elevators to reduce waiting time. Since then the applications of Fuzzy Logic technology have virtually exploded, affecting things we use everyday.
HISTORY
The term "fuzzy" was first used by Dr. Lotfi Zadeh in the engineering journal, "Proceedings of the IRE," a leading engineering journal, in 1962. Dr. Zadeh became, in 1963, the Chairman of the Electrical Engineering department of the University of California at Berkeley.
The theory of fuzzy logic was discovered. Lotfi A. Zadeh, a professor of UC Berkeley in California, soon to be known as the founder of fuzzy logic observed that conventional computer logic was incapable of manipulating data representing subjective or vague human ideas such as "an attractive person" or "pretty hot". Fuzzy logic hence was designed to allow computers to determine the distinctions among data with shades of gray, similar to the process of human reasoning. In 1965, Zadeh published his seminal work "Fuzzy Sets" which described the mathematics of fuzzy set theory, and by extension fuzzy logic. This theory proposed making the membership function (or the values False and True) operate over the range of real numbers [0.0, 1.0]. Fuzzy logic was now introduced to the world.
Although, the technology was introduced in the United States, the scientist and researchers there ignored it mainly because of its unconventional name. They refused to take something, which sounded so child-like seriously. Some mathematicians argued that fuzzy logic was merely probability in disguise. Only stubborn scientists or ones who worked in discrete continued researching it.
While the US and certain parts of Europe ignored it, fuzzy logic was accepted with open arms in Japan, China and most Oriental countries. It may be surprising to some that the world's largest number of fuzzy researchers is in China with over 10,000 scientists. Japan, though currently positioned at the leading edge of fuzzy studies falls second in manpower, followed by Europe and the USA. Hence, it can be said that the popularity of fuzzy logic in the Orient reflects the fact that Oriental thinking more easily accepts the concept of "fuzziness". And because of this, the US, by some estimates, trail Japan by at least ten years in this forefront of modern technology.
UNDERSTANDING FUZZY LOGIC
Fuzzy logic is the way the human brain works, and we can mimic this in machines so they will perform somewhat like humans (not to be confused with Artificial Intelligence, where the goal is for machines to perform EXACTLY like humans). Fuzzy logic control and analysis systems may be electro-mechanical in nature, or concerned only with data, for example economic data, in all cases guided by "If-Then rules" stated in human language.
The Fuzzy Logic Method
The fuzzy logic analysis and control method is, therefore:
1. Receiving of one, or a large number, of measurement or other assessment of conditions existing in some system we wish to analyze or control.
2. Processing all these inputs according to human based, fuzzy "If-Then" rules, which can be expressed in plain language words, in combination with traditional non-fuzzy processing.
3. Averaging and weighting the resulting outputs from all the individual rules into one single output decision or signal which decides what to do or tells a controlled system what to do. The output signal eventually arrived at is a precise appearing, defuzzified, "crisp" value.
Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth- truth-values between "completely true" and "completely false". As its name suggests, it is the logic underlying modes of reasoning which are approximate rather than exact. The importance of fuzzy logic derives from the fact that most modes of human reasoning and especially common sense reasoning are approximate in nature.
The essential characteristics of fuzzy logic as founded by Zadeh Lotfi 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.
The third statement hence, defines Boolean logic as a subset of Fuzzy logic.
Professor Lofti Zadeh at the University of California formalized fuzzy Set Theory in 1965. What Zadeh proposed is very much a paradigm shift that first gained acceptance in the Far East and its successful application has ensured its adoption around the world.
A paradigm is a set of rules and regulations, which defines boundaries and tells us what to do to be successful in solving problems within these boundaries. For example the use of transistors instead of vacuum tubes is a paradigm shift - likewise the development of Fuzzy Set Theory from conventional bivalent set theory is a paradigm shift.
Bivalent Set Theory can be somewhat limiting if we wish to describe a 'humanistic' problem mathematically.
The whole concept can be illustrated with this example. Let's talk about people and "youthness". In this case the set S (the universe of discourse) is the set of people. A fuzzy subset YOUNG is also defined, which answers the question "to what degree is person x young?" To each person in the universe of discourse, we have to assign a degree of membership in the fuzzy subset YOUNG. The easiest way to do this is with a membership function based on the person's age.
Young (x) = {1, if age (x) <= 20,
(30-age (x))/10, if 20 < age (x) <= 30,
0, if age (x) > 30}
a graph of this looks like:
Given this definition, here are some example values:
Person Age degree of youth