Fuzzy logic - Revision history

Designing Buildings at 07:44, 27 November 2020

2020-11-27T07:44:27Z

← Older revision		Revision as of 07:44, 27 November 2020
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	* Risk management.		* Risk management.

−	[[Category:Theory]]	+	[[Category:DCN_Definition]] [[Category:DCN_Guidance]] [[Category:DCN_Research,_Development_and_Innovation]] [[Category:Theory]]

Designing Buildings at 09:48, 19 March 2019

2019-03-19T09:48:23Z

← Older revision		Revision as of 09:48, 19 March 2019
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	--[[User:CIOB\|CIOB]]		--[[User:CIOB\|CIOB]]

−	~~= Find out more =~~	+	= Related articles on Designing Buildings Wiki =
−		+
−	=== Related articles on Designing Buildings Wiki ===	+

	* Biomimicry.		* Biomimicry.
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	* Contingency theory.		* Contingency theory.
	* Game theory.		* Game theory.
		+	* Logic links.
	* Risk management.		* Risk management.
−
−	~~=== External references ===~~
−
−	* Zjawin, G.: Determinanty i sposób zmiany systemu informacyjnego zarzdzania Rachunkowo zarzdcza. Teoria i praktyka. Wyd. Akademia Rolnicza Szczecin 1999.

	[[Category:Theory]]		[[Category:Theory]]

Editor at 16:45, 19 March 2018

2018-03-19T16:45:06Z

← Older revision		Revision as of 16:45, 19 March 2018
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	* Complexity theory.		* Complexity theory.
	* Construction organisation design.		* Construction organisation design.
		+	* Contingency theory.
	* Game theory.		* Game theory.
	* Risk management.		* Risk management.

Editor at 11:24, 28 December 2016

2016-12-28T11:24:50Z

← Older revision		Revision as of 11:24, 28 December 2016
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	* Complexity theory.		* Complexity theory.
	* Construction organisation design.		* Construction organisation design.
		+	* Game theory.
	* Risk management.		* Risk management.

Designing Buildings at 15:16, 9 September 2016

2016-09-09T15:16:12Z

← Older revision		Revision as of 15:16, 9 September 2016
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−
	The roots of Fuzzy logic stretch back 2500 years, when Aristotle asserted that there were various degrees of true and false. However, it is only in the last 30 years, following the work carried out by Lotfi Zadeh, that this concept has been labelled fuzzy logic.		The roots of Fuzzy logic stretch back 2500 years, when Aristotle asserted that there were various degrees of true and false. However, it is only in the last 30 years, following the work carried out by Lotfi Zadeh, that this concept has been labelled fuzzy logic.

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	Perhaps the biggest drawback is that humans are culturally biased towards the avoidance of uncertainty (Hofstede, 1980) which fuzzy logic can generate, whereas, mathematically precise systems produce linear control models which minimize risk and are culturally more acceptable.		Perhaps the biggest drawback is that humans are culturally biased towards the avoidance of uncertainty (Hofstede, 1980) which fuzzy logic can generate, whereas, mathematically precise systems produce linear control models which minimize risk and are culturally more acceptable.

−	----	+	-----
−		+
	The text in this article is based on ‘Business Management in Construction Enterprise’ by David Eaton and Roman Kotapski. The original manual was published in 2008. It was developed within the scope of the LdV program, project number: 2009-1-PL1-LEO05-05016 entitled “Common Learning Outcomes for European Managers in Construction”. It is reproduced here in a slightly modified form with the kind permission of the Chartered Institute of Building.		The text in this article is based on ‘Business Management in Construction Enterprise’ by David Eaton and Roman Kotapski. The original manual was published in 2008. It was developed within the scope of the LdV program, project number: 2009-1-PL1-LEO05-05016 entitled “Common Learning Outcomes for European Managers in Construction”. It is reproduced here in a slightly modified form with the kind permission of the Chartered Institute of Building.
		+
		+	--[[User:CIOB\|CIOB]]

	= Find out more =		= Find out more =

	=== Related articles on Designing Buildings Wiki ===		=== Related articles on Designing Buildings Wiki ===
−	*Biomimicry.	+
−	*Chaos theory.	+	* Biomimicry.
−	*Complexity theory.	+	* Chaos theory.
−	*Construction organisation design.	+	* Complexity theory.
−	*Risk management.	+	* Construction organisation design.
		+	* Risk management.

	=== External references ===		=== External references ===
−	*Zjawin, G.: Determinanty i sposób zmiany systemu informacyjnego ~~zarządzania Rachunkowość zarządcza~~. Teoria i praktyka. Wyd. Akademia Rolnicza Szczecin 1999.	+
		+	* Zjawin, G.: Determinanty i sposób zmiany systemu informacyjnego zarzdzania Rachunkowo zarzdcza. Teoria i praktyka. Wyd. Akademia Rolnicza Szczecin 1999.

	[[Category:Theory]]		[[Category:Theory]]

Designing Buildings at 13:41, 28 March 2014

2014-03-28T13:41:49Z

← Older revision		Revision as of 13:41, 28 March 2014
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	*Biomimicry.		*Biomimicry.
	*Chaos theory.		*Chaos theory.
		+	*Complexity theory.
	*Construction organisation design.		*Construction organisation design.
−	*Fuzzy logic.
	*Risk management.		*Risk management.

Designing Buildings at 13:41, 28 March 2014

2014-03-28T13:41:19Z

← Older revision		Revision as of 13:41, 28 March 2014
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	=== Related articles on Designing Buildings Wiki ===		=== Related articles on Designing Buildings Wiki ===
		+	*Biomimicry.
	*Chaos theory.		*Chaos theory.
	*Construction organisation design.		*Construction organisation design.
		+	*Fuzzy logic.
	*Risk management.		*Risk management.

109.155.232.106 at 13:55, 27 March 2014

2014-03-27T13:55:24Z

← Older revision		Revision as of 13:55, 27 March 2014
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−	The roots of Fuzzy logic stretch back 2500 years, when Aristotle asserted that there were various degrees of true and false. However, it is only in the last 30 years, following the work carried out by Lotfi Zadeh, that this concept has been labelled fuzzy logic.	+	The roots of Fuzzy logic stretch back 2500 years, when Aristotle asserted that there were various degrees of true and false. However, it is only in the last 30 years, following the work carried out by Lotfi Zadeh, that this concept has been labelled fuzzy logic.

	The theoretical basis of fuzzy logic is that mathematics can be used to link language and human intelligence (Zjawin, 1999). Fuzzy theory has evolved to represent data or activities that cannot be clearly determined within boundaries. It is the opposite of binary data which is always a Yes/No or 1 and 0 outcome. An extreme fuzzy logic set known as a crisp set only has 2 possibilities, but this is an example of a special set and is not representative of typical fuzzy sets.		The theoretical basis of fuzzy logic is that mathematics can be used to link language and human intelligence (Zjawin, 1999). Fuzzy theory has evolved to represent data or activities that cannot be clearly determined within boundaries. It is the opposite of binary data which is always a Yes/No or 1 and 0 outcome. An extreme fuzzy logic set known as a crisp set only has 2 possibilities, but this is an example of a special set and is not representative of typical fuzzy sets.
Line 23:		Line 23:

	=== Related articles on Designing Buildings Wiki ===		=== Related articles on Designing Buildings Wiki ===
−	*~~Risk management~~.	+	*Chaos theory.
	*Construction organisation design.		*Construction organisation design.
		+	*Risk management.

	=== External references ===		=== External references ===

Designing Buildings at 13:16, 27 March 2014

2014-03-27T13:16:40Z

← Older revision		Revision as of 13:16, 27 March 2014
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−	Fuzzy logic ~~is not a new science, its roots~~ stretch back 2500 years. Aristotle asserted that there were various degrees of true and false. It is only in the last 25 years ~~that this concept has been labelled fuzzy logic~~, ~~after~~ the work carried out by Lotfi Zadeh~~. The theoretical basis of~~ fuzzy logic ~~is that mathematics can be used to link language and human intelligence (Zjawin, 1999)~~.	+	The roots of Fuzzy logic stretch back 2500 years, when Aristotle asserted that there were various degrees of true and false. However, it is only in the last 30 years, following the work carried out by Lotfi Zadeh, that this concept has been labelled fuzzy logic.

−	Fuzzy theory has evolved to represent data or activities that cannot be clearly determined within boundaries. It is the opposite of binary data which is always a Yes/No or 1 and 0 outcome. An extreme fuzzy logic set known as a crisp set only has 2 possibilities, but this is an example of a special set and is not representative of typical fuzzy sets.	+	The theoretical basis of fuzzy logic is that mathematics can be used to link language and human intelligence (Zjawin, 1999). Fuzzy theory has evolved to represent data or activities that cannot be clearly determined within boundaries. It is the opposite of binary data which is always a Yes/No or 1 and 0 outcome. An extreme fuzzy logic set known as a crisp set only has 2 possibilities, but this is an example of a special set and is not representative of typical fuzzy sets.

−	The expression which defines fuzzy theory is TV \|\|0-1\|\|~~. When simplified into terms, the~~ meaning is the truth-value lies between zero and one~~, the two extremes~~. (Abe 1995) As fuzzy logic uses approximate reasoning, the outcomes that are generated are neither exact nor inexact and when dealing with complex data, fuzzy theory can be used to simplify the results. The process also makes very complex results easier to prove due to the grouping of the data~~, this is called~~ the rules of inference. (McNeill 1994)	+	The expression which defines fuzzy theory is TV \|\|0-1\|\| meaning that the truth-value lies between zero and one. (Abe 1995) As fuzzy logic uses approximate reasoning, the outcomes that are generated are neither exact nor inexact and when dealing with complex data, fuzzy theory can be used to simplify the results. The process also makes very complex results easier to prove due to the grouping of the data (the rules of inference). (McNeill 1994)

−	The first practical application of fuzzy logic ~~came about~~ in the 1970's when a British engineer Ebrahim Mamdani ~~discovered it by accident. He~~ was trying to develop an automated control system for a steam engine~~, which was being operated by a skilled operative~~. The machine had to adjust the throttle to maintain the steam ~~engines~~ speed and boiler pressure but ~~by using~~ a mathematical formula (intelligent algorithm) the results were poor (Sanchez 1997). However, when he tried an artificial intelligence method, a 'rule based expert system' (a type of fuzzy logic), which ~~combines~~ human expertise with a series of logic rules, the results were ~~even~~ better than expected.	+	The first practical application of fuzzy logic was in the 1970's when a British engineer Ebrahim Mamdani was trying to develop an automated control system for a steam engine. The machine had to adjust the throttle to maintain the steam engine's speed and boiler pressure, but if a mathematical formula (intelligent algorithm) was used the results were poor (Sanchez 1997). However, when he tried an artificial intelligence method (a 'rule based expert system' - a type of fuzzy logic), which combined human expertise with a series of logic rules, the results were better than expected.

−	Fuzzy systems have increasingly becoming part of our lives and now have many uses. The idea has developed rapidly in Japan as many of the leading companies such as Nissan and Matsusisha have incorporated fuzzy logic into their products (Klir 1995). For example, Matsusisha have developed a fuzzy logic washing machine. At the beginning of the washing cycle the machine carries out a pre-wash and assesses the dirtiness of the water and so applies a certain measure of detergent to the cycle. The concept is that it maximizes the efficiency of the cycle and does not bleach the clothes with too much detergent. ~~Examples of other~~ uses of fuzzy logic include; traffic light systems, video and television tuning automation and anti-lock brakes on motor vehicles.	+	Fuzzy systems have increasingly becoming part of our lives and now have many uses. The idea has developed rapidly in Japan as many of the leading companies such as Nissan and Matsusisha have incorporated fuzzy logic into their products (Klir 1995). For example, Matsusisha have developed a fuzzy logic washing machine. At the beginning of the washing cycle the machine carries out a pre-wash and assesses the dirtiness of the water and so applies a certain measure of detergent to the cycle. The concept is that it maximizes the efficiency of the cycle and does not bleach the clothes with too much detergent. Other uses of fuzzy logic include; traffic light systems, video and television tuning automation and anti-lock brakes on motor vehicles.

−	The advantages of employing fuzzy logic to situations ~~(in particular construction)~~ with multiple complex solutions are that linguistic, not numerical, variables are used, making it similar to the way humans think (McNeill 1994). Also rapid prototypes or budget costs can be created, as the information produced is not detailed. Other advantages are that fuzzy logic is cheaper to develop than other intelligent systems, simplifies knowledge acquisition and representation and can accommodate rules on complexity.	+	The advantages of employing fuzzy logic to situations with multiple complex solutions, such as construction, are that linguistic, not numerical, variables are used, making it similar to the way humans think (McNeill 1994). Also rapid prototypes or budget costs can be created, as the information produced is not detailed. Other advantages are that fuzzy logic is cheaper to develop than other intelligent systems, it simplifies knowledge acquisition and representation and can accommodate rules on complexity.

−	The drawbacks of a fuzzy logic system are that it is very hard to develop a model from the outcome, as it is so broad and imprecise. Also compared to other control systems, fuzzy logic relies on detailed inputs and requires fine tuning before it can become operational. As construction projects are usually one off designs there is little room for trial and error. Perhaps the biggest drawback is that humans are culturally biased towards an avoidance of uncertainty (Hofstede, 1980) which fuzzy logic can generate, whereas, mathematically precise ~~or crisp~~ systems produce linear control models which minimize risk and are culturally more acceptable.	+	The drawbacks of a fuzzy logic system are that it is very hard to develop a model from the outcome, as it is so broad and imprecise. Also compared to other control systems, fuzzy logic relies on detailed inputs and requires fine tuning before it can become operational. As construction projects are usually one-off designs there is little room for trial and error.
		+
		+	Perhaps the biggest drawback is that humans are culturally biased towards the avoidance of uncertainty (Hofstede, 1980) which fuzzy logic can generate, whereas, mathematically precise systems produce linear control models which minimize risk and are culturally more acceptable.

	----		----

Designing Buildings: Created page with " Fuzzy logic is not a new science, its roots stretch back 2500 years. Aristotle asserted that there were various degrees of true and false. It is only in the last 25 years that t..."

2014-03-27T13:00:38Z

Created page with " Fuzzy logic is not a new science, its roots stretch back 2500 years. Aristotle asserted that there were various degrees of true and false. It is only in the last 25 years that t..."

New page

Fuzzy logic is not a new science, its roots stretch back 2500 years. Aristotle asserted that there were various degrees of true and false. It is only in the last 25 years that this concept has been labelled fuzzy logic, after the work carried out by Lotfi Zadeh. The theoretical basis of fuzzy logic is that mathematics can be used to link language and human intelligence (Zjawin, 1999).

Fuzzy theory has evolved to represent data or activities that cannot be clearly determined within boundaries. It is the opposite of binary data which is always a Yes/No or 1 and 0 outcome. An extreme fuzzy logic set known as a crisp set only has 2 possibilities, but this is an example of a special set and is not representative of typical fuzzy sets.

The expression which defines fuzzy theory is TV ||0-1||. When simplified into terms, the meaning is the truth-value lies between zero and one, the two extremes. (Abe 1995) As fuzzy logic uses approximate reasoning, the outcomes that are generated are neither exact nor inexact and when dealing with complex data, fuzzy theory can be used to simplify the results. The process also makes very complex results easier to prove due to the grouping of the data, this is called the rules of inference. (McNeill 1994)

The first practical application of fuzzy logic came about in the 1970's when a British engineer Ebrahim Mamdani discovered it by accident. He was trying to develop an automated control system for a steam engine, which was being operated by a skilled operative. The machine had to adjust the throttle to maintain the steam engines speed and boiler pressure but by using a mathematical formula (intelligent algorithm) the results were poor (Sanchez 1997). However, when he tried an artificial intelligence method, a 'rule based expert system' (a type of fuzzy logic), which combines human expertise with a series of logic rules, the results were even better than expected.

Fuzzy systems have increasingly becoming part of our lives and now have many uses. The idea has developed rapidly in Japan as many of the leading companies such as Nissan and Matsusisha have incorporated fuzzy logic into their products (Klir 1995). For example, Matsusisha have developed a fuzzy logic washing machine. At the beginning of the washing cycle the machine carries out a pre-wash and assesses the dirtiness of the water and so applies a certain measure of detergent to the cycle. The concept is that it maximizes the efficiency of the cycle and does not bleach the clothes with too much detergent. Examples of other uses of fuzzy logic include; traffic light systems, video and television tuning automation and anti-lock brakes on motor vehicles.

The advantages of employing fuzzy logic to situations (in particular construction) with multiple complex solutions are that linguistic, not numerical, variables are used, making it similar to the way humans think (McNeill 1994). Also rapid prototypes or budget costs can be created, as the information produced is not detailed. Other advantages are that fuzzy logic is cheaper to develop than other intelligent systems, simplifies knowledge acquisition and representation and can accommodate rules on complexity.

The drawbacks of a fuzzy logic system are that it is very hard to develop a model from the outcome, as it is so broad and imprecise. Also compared to other control systems, fuzzy logic relies on detailed inputs and requires fine tuning before it can become operational. As construction projects are usually one off designs there is little room for trial and error. Perhaps the biggest drawback is that humans are culturally biased towards an avoidance of uncertainty (Hofstede, 1980) which fuzzy logic can generate, whereas, mathematically precise or crisp systems produce linear control models which minimize risk and are culturally more acceptable.

----

The text in this article is based on ‘Business Management in Construction Enterprise’ by David Eaton and Roman Kotapski. The original manual was published in 2008. It was developed within the scope of the LdV program, project number: 2009-1-PL1-LEO05-05016 entitled “Common Learning Outcomes for European Managers in Construction”. It is reproduced here in a slightly modified form with the kind permission of the Chartered Institute of Building.

= Find out more =

=== Related articles on Designing Buildings Wiki ===
*Risk management.
*Construction organisation design.

=== External references ===
*Zjawin, G.: Determinanty i sposób zmiany systemu informacyjnego zarządzania Rachunkowość zarządcza. Teoria i praktyka. Wyd. Akademia Rolnicza Szczecin 1999.

[[Category:Theory]]