Talk:Fuzzy logic

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[edit] older comments

I would also strongly support the point of view that fuzzy logic must not be separated from prob theory. This is due to the fact that conditional on some covariate (e.g. position in the house) membership fractions add to one, which simply can be interpreted as a multinomial distribution. The point of confusion might be that this is a conditional distribution and no distribution of the covariate is usually assumed. The question is just whether inference differs in fundamental way. I cannot see this either, but maybe I am missing something. Any functional depending on membership fractions/probalities should be interpretable in a statistical sense (predictions, moments, moments of functions, etc.). I think this needs to be clarified. Sboehringer 10:40, 31 December 2006 (UTC)


Some of those examples were misleading. So I changed them. I don't think they're ideally placed at the moment, though. There's no need to start discussing the probability theory stuff in the beginning of the article. That should only contain information about what fuzzy logic is! I am not going to bother any more with this article, actually. The possibility theory article is much much better. Perhaps Fuzzy Logic should be just a short article talking about the history of fuzzy logic, etc?? All the controversial stuff is explained very nicely in the possibility theory part. My overall opinion, this article needs to be severely shortened. --Olethros 13:18, 25 May 2007 (UTC)


[edit] Older discussion

Much as I respect the important contributions of Dr. Lofti Zadeh in the '60s, particularly in the reduction of an idea to engineering practice, the roots of fuzzy logic lie in the concept of "vagueness." My father Max Black published one of the seminal papers on this concept before WWII - cit.: Philosophy of Science 4, 427-455, Oct. 1937. I have brought this to Dr. Zadeh's attention and he recognizes its precedence. adamsmithusa

then you should add this information to the article, perhaps in a section on the history of the concept.--Scriber 02:55, 15 August 2005 (UTC)

Note this whole argument for and against fuzzy logic is mute and irrelavent. Many decision / classification approaches overlap and in fact can be proven to be the same. Take for instance Tagaki-Sugeno Fuzzy inference. They basically modified the inference system, using fuzzy nomencluture to implement a type of neural network. You can call all of these anything you like, but the names indicate the original motive or application area where this idea cam from. I can even prove some neural networks, baysian networks and fuzzy logic based inference systems are identicle!

Fuzzy logic describes a specific type of multi-valued logic which has gained considerable application in engineering. It warrants an article on its own, IMHO. --Robert Merkel

Fuzzy logic is used to control household appliances (such as washing machines which sense load size and detergent concentration and auto-adjust their wash cycles accordingly; and refrigerators)

I'm not sure about the washing machines. It's not logic - it's just using the load size to calculate the detergent concentration. There is no predicate in that. The system won't be working on "how true is it that the load is heavy?". CGS 01:23, 14 Nov 2003 (UTC).

What is this supposed to mean: "Al St.John (1893 - 1963) successfully incorporated the bearded "Fuzzy" in a series of Cowboy B-movies. See also: Westerns."? Is it a caharcter that just has the name "Fuzzy"? Or do the b-weterns by St.John somehow exemplify fuzzy logic? If that is the case the sentence should be rewritten.

I've been adding new fuzzy logic articles: fuzzy associative matrix, Combs method, and most recently defuzzification. I decided to create these in separate pages because they can be treated in depth in their own right, although there is not much depth at these pages yet. - Furrykef 06:31, 3 Oct 2004 (UTC)

Whether or not a statement has a certain determinate truth-value is different from whether or not we are able to know or ascertain the truth-value of a statement. Fuzzy logic is used to deal with vague concepts and predication - it is not an epistemic or doxastic modal logic used for capturing notions like degrees of certainty. Hence the deletion of the section involving the (confusion about the) "controversy" over fuzzy logic. Nortexoid 04:41, 7 Nov 2004 (UTC)

As a statistician it is quite irritating to be told that FL is

"...generally rejected by mathematicians and statisticians because it seems to contradict the

principle of bivalence."

The idea that mathematicians, who invented undecidibility, would reject a form of logic because it involved a form of undecidibility is stupid.

FL is controversial and the critics, like myself, should be acknowledged.

The chief arguments against FL in my view are

a) Exaggerated claims are made for it. The claim that it is a generalisation of set theory is simply false, as membership functions are functions, and functions are defined in terms of sets. Thus FL is built on set theory, and is so not a generalisation of it.

b) FL is used for both deterministic purposes and decision-making under uncertainty. For deterministic purposes it does not offer much of an advantage over simple percentages. For decision-making under uncertainty it should give the same answers as decision theory or there should a good reason why not. It does not give the same answers as decision theory. The reason is that the solutions it provides are, in decision theory terms 'inadmissible' (i.e. non-optimal). FL is simply a 'quick and dirty' ad hoc technique. There is a place for 'quick and dirty' techniques in engineering, as long as one knows that that is what one is using. However, I suspect that many people using FL think they using a rigorous technique.

c) Conventional Popperian philosophy of science lays emphasis on statements which empirically falsifiable. The FL set membership functions are not empirically falsifiable, whereas probability statements (even Bayesian subjective probabilities) are capable of refutation with probability 1 - epsilon, for any positive epsilon. Blaise 19:54, 25 Apr 2005 (UTC)

Well, if you think you can improve the article, by all means do so. Just be sure to keep it NPOV. - furrykef (Talk at me) 20:50, 25 Apr 2005 (UTC)
To add my own opinion, I don't think the implementation of fuzzy logic really accomplishes anything that can't be done with other math. I think where fuzzy logic wins is the way you look at a problem. Sometimes a more linguistic approach is more appropriate, and in my opinion, proper fuzzy logic (as opposed to the way it is often applied) is all about being able to phrase a problem and its solution in linguistic terms. Then the solution becomes obvious, and should be easy to implement.
Also, I've been wanting to speak to an "antifuzzy" person for a long time; now that I've met one, I must ask: why was it that the Sendai Subway was (is?) the smoothest subway ride in the world if its use of fuzzy logic is easily replaced by conventional logic? :) - furrykef (Talk at me) 22:34, 25 Apr 2005 (UTC)

>I think where fuzzy logic wins is the way you look at a problem. Sometimes a more linguistic approach is more appropriate, and in my opinion, proper fuzzy logic (as opposed to the way it is often applied) is all about being able to phrase a problem and its solution in linguistic terms. Then the solution becomes obvious, and should be easy to implement.

I can go along with that.

Blaise 11:37, 28 Apr 2005 (UTC)

>why was it that the Sendai Subway was (is?) the smoothest subway ride in the world if its use of fuzzy logic is easily replaced by conventional logic?

I don't think I've ever been that particular subway but, with respect, I'd remind you of the logical fallacy of 'Post Hoc Ergo Propter Hoc' (literally, 'after therefore because) in which one assumes that because event B follows event A one assumes that event A caused event B. In the 1970s an entire issue of Technometrics was devoted to FL. Peter Cheeseman of NASA Ames wrote some good 'antifuzzy' articles. In one, I seem to remember, he showed how you can take any fuzzy controller and replace it with an equivalent probabilistic controller.

Blaise 11:37, 28 Apr 2005 (UTC)

[edit] Common misconceptions

This section in particular is the most unacceptably POV piece of promotion in an article that reads rather like a sales pitch. I've not got time in the next few weeks to rewrite it, but I'll put the task on my to-do list. ---- Charles Stewart 04:15, 7 Dec 2004 (UTC)

I agree -- and I'm the one who wrote it! It was kind of meant to be a draft, but, as ends up happening too often, I didn't come back to it. I do think it is true that fuzzy logic is misunderstood and this needs to be noted, but a better job needs to be done of it, yes. - furrykef (Talk at me) 20:02, 7 Dec 2004 (UTC)
I think Blaise's revisions handle the issue well now. - furrykef (Talk at me) 01:59, 29 Apr 2005 (UTC)
Maybe the name of the section should be changed to something like "Debate over fuzzy logic" Stupidone0 05:48, 31 May 2007 (UTC)

[edit] This introductory sentence does not make sense.

"Degrees of truth are often confused with probabilities, although they are conceptually distinct, because they need not add up to 100%. "

Totally absent from this sentence is any idea why fuzzy logic might be identified with probability and why they are in actuality different.

A prototype replacement sentence might be: "Degrees of truth are often confused with probabilities: while both deal with "maybes", probability theory deals with the statistical likelihood of the occurrance of an event (hence all probability weightings add up to 100%) whereas degress of truth ..." {fill in the ellipsis at your leisure}.

I'm not an expert in either although I have a fair grounding in probability, so I am reticent to change the article myself. (HTM 2005.04.26 23:50GMT)

For me, fuzzy logic is actually equivalent to probabilistic statements. The basic membership in fuzzy logic, such as 'X being Big' would have some kind of membership function f(x) taking values in [0,1]. Let's say for the moment that this is some kind of triangle with support on [a,b], f(a)=f(b)=0 and f((a+b)/2)=1.

This is exactly equivalent to defining the conditional probability function for X given that X is big i.e. p(x|X_is_big), having the above shape and normalised such that \int p(x|X_is_big) dx = 1

Then you can go on and use standard probability theory and 'fuzzy logic' becomes 'probabilistic inference'. So, what is the advantage in introducing yet another nomenclature? Maybe there is something inherently interesting about fuzzy logic, but it just looks like clumsy probabilities to me. Please correct me if I hold misconceptions, though. --Olethros 23:55, 21 December 2005 (UTC)

The sentence "fuzzy truth represents membership in vaguely defined sets,..." does not make sense. I think the sets are precisely defined, no matter what form they are in, triagular, guassian, etc. If not, how could, for example, Fuzzy Controllers work? --JustAnotherJoe 03:23, 27 December 2005 (UTC)

I agree with the above. The sentence "Degrees of truth are often confused with probabilities, although they are conceptually distinct, because they need not add up to 100%. " is a bit silly, since 'likelihoods', i.e. densities of different things do not necessarily add up to 100%. For example the distribution of heights for men, and the distribution of heights for women. If you take a particular height range, then the numbers of and women won't add up to a constant value obviously. Similarly, for set membership.
A better example: Let's say that we ask people whether they think that a particular temperature is 'hot', 'comfortable' or 'cold'. Then we get three densities for those categories of temperatures. From the three densities you could get the probability that someone will say 'hot' for a particular temperature simply by normalising using the definition of the conditional density (i.e. Bayes formula).
Not only that, but probability theory will also allow you to add prior knowledge as to how probable someone is to say that 20C is hot, if he thinks that 19C is hot. This prior knowledge can be integrated with the data to obtain a posterior more accurate representation of people's perception of temperature. So, in this case, probability seems more general than fuzzy logic. How can fuzzy logic alter set membership based on observations? What would be the meaning of the alteration? What would be the equivalent of prior and posterior/conditional distributions?
Of course, the converse could also be true. Perhaps there is no perfect containment of one construction into the other. However this should be made clear in the article. Any statements claiming more generality of one concept relative to another should be accompanied by a clear counter-example, or a direct reference to one.
--Olethros 08:25, 22 May 2007 (UTC)

[edit] Possible bad example of a non-probability truth degree?

With only his little toe in the dining room, we might say Bob is 0.99 "in the kitchen", for instance. No event (like a coin toss) will resolve Bob to being completely "in the kitchen" or "not in the kitchen", as long as he's standing in that doorway.

Wouldn't this 99% degree of truth correspond easily to a probability, namely that the center of a randomly selected particle of Bob's body is within the kitchen? --Damian Yerrick 02:41, 29 Apr 2005 (UTC)

While I don't think that example is particularly good, and your statement is correct, I'd say that example is contrived. Of course nobody would actually think of the problem as, "what percent chance is there of a random particle of Bob's body being within the kitchen"? You could really phrase many if not all statements of fuzzy membership that way. For instance instead of asking if the apple is half-eaten, you might ask, "what percent chance is there of a randomly chosen particle that once made up this apple has passed through somebody's digestive system"? - furrykef (Talk at me) 04:42, 29 Apr 2005 (UTC)

I disagree. This probability-oriented interpretation of fuzzy set membership assumes one particular membership function. Sigmoid fuzzy membership functions, for example, would not fit such an interpretation. -Predictor

I was objecting to that paragraph's implication of a bright line between probability theory and fuzzy logic, a bias toward Dr. Zadeh's point of view and against Dr. Kosko's. Like Dr. Kosko, I see some overlap, and contrived corner cases are useful for pointing out this overlap. Fuzzy metalogic anyone? --Damian Yerrick 17:36, 7 May 2005 (UTC)
A sigmoid fuzzy membership function is a perfectly valid density, though, in the same way as the uniform distribution, P(a < X < b) by itself does not tell you much since it's infinitesimal. On the other hand, you could just write the density p(x) = Z / (1 + exp( − x)), where Z is some normalisation constant, which happens to be the same as that of the uniform distribution. So, if your sigmoid model is the conditional density p(x | C), where C is some category, you can discover the probability that some data X belongs in the category by doing P(C|X) = \frac{p(X|C)P(C)}{p(X)} where P(C) is the prior probability of the category, p(x) is a prior density - note that this must have a support that covers p(x|C), so even if the normalisation constants are infinitely small they will be of the same order of infinity so everything is certainly computable.--Olethros 00:24, 22 December 2005 (UTC)

On a related point, shouldn't it be his big toe? Quite difficult to only enter one's little toe into a room. 81.179.227.183 09:44, 25 July 2006 (UTC)

On the basis that no one's objected, I have changed this :) 81.179.68.182 10:41, 29 August 2006 (UTC)

[edit] Separate from probability

Here is an example that I came up with of how conventional logic differs from FL. It also shows that FL has its niche apart from probability:

Tim and Carl have to unload and clean a truck. Tim is stronger than Carl, and Carl is better at cleaning than Tim. Therefore Tim should unload the truck, and Carl should do the cleaning.

This is a logical approach; we’ve assigned a function to each of the workers and delegated accordingly. But what have we done with Tim’s ability to clean and Carl’s strength? Are they both void? Does Carl have no strength and is Tim a complete slob who is unable to clean anything? It seems that in the process of making a clear cut decision we’ve neglected some abilities. It the industrial and commercial realm, we have failed to use all of our resources. In the above example, we would like to see both workers exercise their greatest abilities, but also be able to utilize the lesser skills they still obtain. This is the foundational concept of Fuzzy Logic; waste can be minimized by reducing the impulse to conclude a black and white solution to a complex problem.

If Tim is stronger than Carl, and Carl is better at cleaning, then Tim should do more of the unloading, and Carl should do more of the cleaning. But both of them should do both tasks.

Now we have a situation where Tim has help carrying the heavy couch, and Carl has someone to help him sweep. Not to mention how much faster both jobs will get done!

If you wanted to place number values on the situation, if Tim is 25% stronger than Carl, then Tim should do 25% more lifting. This is my understanding of FL, unless it is a different logic altogether...

Actually you can't infer anything like that without more information. What if the cleaning is really easy while the unloading is hard work. Then both should spend most of their time unloading the truck... ...unless only one can unload at once etc. 194.237.142.21 14:13, 9 August 2005 (UTC)

A more sophisticated practical example is the use of fuzzy logic in high-performance error correction to improve information reception over a limited-bandwidth communication link affected by data-corrupting noise using turbo codes. The front-end of a decoder produces a likelihood measure for the value intended by the sender (0 or 1) for each bit in the data stream. The likelihood measures might use a scale of 256 values between extremes of "certainly 0" and "certainly 1".

To me this sounds like a textbook example of when to use Bayes' theorem. In other words, it is (should be) an application of probability rather than fuzzy logic. Earlier the article states that

because fuzzy truth represents membership in vaguely defined sets, not likelihood of some event or condition.

and we're definately dealing with the latter in this case. 194.237.142.21 14:13, 9 August 2005 (UTC)

Yeah, I cannot think of a reason why a probability measure over a set does not construe a 'vaguely defined set'.
"If Tim is stronger than Carl, and Carl is better at cleaning, then Tim should do more of the unloading, and Carl should do more of the cleaning. But both of them should do both tasks. ... how much faster both jobs will get done!"
-- As I understand Ricardo's Law / Law of Comparative Advantage, economic theory says that this isn't true at all. Instead, each person should stick to doing whatever he does best, and all the work will get done most efficiently this way. -- 201.78.233.162 02:11, 30 June 2006 (UTC)
But does Ricardo's Law encompass team synergy? In more than a few cases, many hands make light work. If two people can move a piece of furniture more than twice as efficiently than one can, then it maximizes overall efficiency when both people work on it. --Damian Yerrick (☎) 03:25, 5 July 2006 (UTC)

[edit] Proof of DeMorgan's Theorem, excluded middle, Fuzzy Logic

I can't provide a reference, since this is something I figured out myself (although I'm sure others have figured out the same), so I won't add it directly to the article. But as I recall, part of the proof of the DeMorgan's Theorem relies on the law of the excluded middle. Since the excluded middle doesn't exist in Fuzzy Logic, the proof is no longer valid. But you still need DeMorgan's, so it must be adopted implicitly as an axiom. I've never seen anyone else mention this, however.

[edit] "something cannot be 'cold' at N degrees but 'not cold' at N+1 degrees"

Isn't "not-cold at N-1" meant? Somebody confirm this and correct if appropriate.

No, it is saying what is intended, although it may not be saying it well. The point is that in common usage, one would not use a precise cut-off point for "coldness", e.g. saying that 12 degrees is definitely cold, but 13 degrees is definitely not cold. -R. S. Shaw 23:16, 4 December 2005 (UTC)
Somebody else kept changing "cannot" to "can". I really don't understand why this sentence seems to be so hard to understand, considering surrounding context should make it clear, but I finally reworded it... - furrykef (Talk at me) 03:38, 29 December 2006 (UTC)

[edit] Needs a section on logical operations and more

This encyclopedic entry desperately needs mention of the families of ways to represent conjunction, disjunction, negation, and inference. One could also go on to describe the necessary relationships between these using DeMorgan triples. At the moment all this entry has is a brief mention of fuzzy sets. This paucity is then promptly overwhelmed by nay saying.

I would also argue that all the "controversial" labelling should be removed because theoretical paradigms are by definition controversial. That is the Carl Popperian basis of falsifiability. If the paradigm isn't controversial, it's dogma (or incontrovertible fact) and has no place in this discussion. Thus, labelling things "controversial" serves only to advance a personal preference. See also Kosko "claims" to have derived Bay's Theorem. What was the claim? We don't know. The result is incomplete discussions and ignorance.

I would write these myself but I'm busy writing several other papers at the moment. Apologies but its difficult to write something that may be editted by personal preference when for the same effort what I write can be evaluated by a knowledgable editorial review board that already accepts "controversial" presuppositions. I realize this is an excuse so...dare I invoke Schwarzenegger...owl be bock.

[edit] Gianni Bellocchi

I am looking for an expert in the field of fuzzy logic to review the claims on the autobiographical article Gianni Bellocchi. The author (who is also the topic) has made many claims to notability that I am not qualified to evaluate. Thank you. -Harmil 16:08, 24 March 2006 (UTC)

[edit] Applications

This article is pretty good! Some of the other technolgoy articles on Wiki are utter rubbish. Concerning applications - It might be worth mentioning that Fuzzy L is commonly used to control robot navigation and other computer-driven vehicles operating in the real world because of its ability to quickly interpolate logical outputs (for motors and things) with the centre of gravity function. (Also Fuzzy is more and more being used for machine vision algorithms.)

[edit] What is the diffrence between fuzzy logic and binary boolean logic??

Can anyone help?

Basically, fuzzy logic allows a continuous range of truth values instead of just true and false. But as you ask, it really seems that introduction to this article may be a bit confusing for newcomers. Samohyl Jan 20:17, 18 October 2006 (UTC)

[edit] Link to ja

The Japanese wikilink to ja:ファジー has been removed twice now. Can anybody explain why? The article there looks relevant to me. It does link to an article about fuzzy sets rather than fuzzy logic proper, but I think that's better than having no link at all, since fuzzy sets are the most important part of the concept of fuzzy logic anyway. - furrykef (Talk at me) 12:50, 24 May 2007 (UTC)

Somebody removed the link again on the grounds that it redirects to the Japanese article on fuzzy sets. I thought about reinstating the link again, but I noticed that it really doesn't discuss fuzzy logic in any broad detail at all. The ideal solution would be to write a Japanese article on fuzzy logic in general, but I don't know enough Japanese to even make a rough stab at it. What should we do? - furrykef (Talk at me) 18:29, 20 June 2007 (UTC)

[edit] Optimal decisions

What is the theory for optimal decision making under uncertainty in fuzzy logic systems? Does one even exist? The classical utility theory integrates just fine with either probabilities or with classical logic. But fuzzy logic?

For example, let's consider some set of possible universes W, composed of the disjoint possibilities w_1, w_2, ..., w_n. Let's say that in all universes you can to things: a_2 or a_2. Let us say that for each universe w there is a known reward funcion R(A;W). If you know that you are in some universe w, then classical logic and utility theory says

If W == w then a = argmax_{a'} R(A=a';W=w).

In fuzzy logic you'd probably have to 'crispify' your membership to decide which universe you are in. Let's call the membership function M(W). Then

w = argmax_{w} M(W=w')

a = argmax_{a'} R(A=a'; W=w).

However, consider the case when there is a w_1, w_2, which both maximise M(.) and for which the following holds:

R(A=a ; W=w_1) = - R(A=a; W=w_2).

In that case, we have no way of choosing an action.


On the other hand, probability theory and utility theory say:

a = argmax_{a} E[R | A=a] = argmax_{a}[sum_w R(A=a';W=w) P(W=w)]

This works because you can marginalise over W to get the expected reward. Again, W is not necessarily 'random', it may just express an uncertain belief. Or it could be both uncertain and random - it depends on how P(W) is defined.

In any case, the question is, how would the choice be made in Fuzzy Logic? It seems that for the actual decision, it'd be something like

If w_1 then a_1

If w_2 then a_2

but the reward does not go anywhere... is something missing here? --Olethros 13:40, 25 May 2007 (UTC)

I'm not sure if I'm smart enough to answer, but here is an attempt. The fuzzy membership function is defined as a whole class of functions. If you pick a particular way of defining it, you can integrate in a similar way to the classical method with that as your measure. An example is using a capacity as your fuzzy measure and integrating with a Choquet integral. I've been meaning to write up some good articles on this stuff, but it hasn't happened yet. I can look up some references if this sounds like the type of answer you want to learn more about, but I'm worried I might be less help than harm. Smmurphy(Talk) 17:03, 25 May 2007 (UTC)
Good, this seems to start making some sense. If you have a capacity v for some set S, you just set S(x)={s|f(s)>x} and integrate.. to get int v(S(x)) dx. Now I can't see how the capacity relates to the fuzzy membership function. If you take any value s in S, then you get the membership fnuction m(s). But the capacity function takes as input sets in S, not single values in S. So, hm.. I would define the capacity as the integral over a subset Q: v(Q) = int_Q m(s) ds. Note that then m(s) would not have to be [0,1] anymore! I think there's a hole here. Probably this is supposed to only work if you set your membership function to be some capacity function.
Which brings me to my problem with this article and with fuzzy logic in general: a lack of good definitions. There apparently many different formalizations of the concept of fuzzy logic. The article needs to reflect the fact that fuzzy logic is only a general concept and nothing precise. First give some idea of what it is like, then write up the list of formalizations. A comparison with probability/whatever makes no sense unless you are speaking about a specific FL sub-field.

--Olethros 18:30, 25 May 2007 (UTC)

[edit] "Perhaps a better question is what technologies do not employ fuzzy logic"

I removed this from the article, at the end of Examples where fuzzy logic is used:

Perhaps a better question is what technologies do not employ fuzzy logic? Most software and mechanical devices and indeed most technologies that work in bounded conditions must, but clearly, since this line will be deleted, this is not popular opinion. Instead of just deleting this line, how about addressing why it's proposition is not accurate?

I can't answer your question because I'm not sure you're trying to say. "Most technologies that work in bounded conditions must?" What do you mean by a "bounded condition", and what makes this statement true?

Also, the line was deleted before not because it was unpopular, but because it was unencyclopedic. It isn't the sort of sentence you're likely to find in an encyclopedia. - furrykef (Talk at me) 15:12, 2 August 2007 (UTC)

Granted it's not appropriate for inclusion in the article but if the same line was posted multiple times this guy probably wants an answer and isn't satisfied with you throwing petty semantics in his face. I think you can answer the question simply by providing an example of a technology that does not in any arguable way utilize fuzzy logic, which would be an appropriate inclusion in the article. Paperflight 02:36, 3 August 2007 (UTC)

[edit] Fuzzy logic - the same as saying quantative logic?

A thought i had triggered by a discussion elsewhere - fuzzy logic relies on a set of defined rules to be determined as being true or false. It relies on creating quantitive measurements of already pre-defined binary states.

An example, a light with a dimmer switch. I've seen people use this as an example of Fuzzy logic, where it becomes a game of defining the state, "where is the light at 50% brightness in relation to 100% brightness" would be the same as saying "For this range of cases tell me where this case = true"? - if, then, else?

In the light example, if you were to look at it further on the cause of it being on at 50% would it not be true enough to say that since electrons are present that the object is in a state of on, and that the 50% is more a measure of the amount of electrons passing through the light resulting in a measurable effect as apposed to a definition of what could be or isnt?

A computer component for example works off 1's and 0's, which is determined by 'how on' a certain object is by the measurable quantity of voltage through it. 0v = off, .5v (or what ever it is) = on, for the states inbetween it is 'unknown', this limitation is set by the designer due to external forces, and if perfect components were to be able to be used as little as 1 electron could be used to define a state, so either the electron is there or it isnt there is no grey, the grey exists cause we choose to define it as a measurable quantive state.

If we are to look at 'experience' as being a modifier for where our value lies in the 'grey' it would be another case of 1's or 0's, you go through the logic, "have i done this before?" yes, 1, the process recalls the information in the objects memory (which is 1's or 0's) and applies it to the current representation of what it has. Binary multiplication, 1's and 0's aside, result - 1 or 0 to proceed. Now i may be over simplifying this, but there is no grey... it either happens or it doesnt.

So to tie it together with another example, a glass full of hot water. We know its hot, our brain interprets the signals sent by our hands to say it is hot. The state of being hot or cold is a binary one. How hot it is per fuzzy logic would indicate that we measure the hotness as a percentage, 0 being stone cold, or 1 being some immesurable heat (for water probably more likely to be 100 degrees where it would no longer be water but vapour). I propose that if the water has temperature from any measurable base state that the value for heat reads as a 1, how hot it is, being a measure of how many times that 1 has been applied to it. Seriphis 09:43, 21 September 2007 (UTC)


Yes. Fuzzy logic is indeed a purely quantitative system and not the qualitative system many seem to assume is its key benefit.

The input condition boundaries are precisely defined using numbers and the output results are also precisely defined using numbers. Once the input and output ranges have been pre-defined then the math used by FL to translate real inputs into real outputs is trivial and deterministic. Every FL system could be replaced by an equivalent numeric lookup map that will give identical results.

FL however does seem a useful way of viewing and mapping some complex algebraic numerical problems as simpler geometric problems and in practical computing terms the use of FL as a high level language could sometimes have undoubted time saving elements in system production.

I believe all the above to be demonstrable and in my own opinion FL is just a novel numerical method rather than the root update in logic that some claim.


Paul J. Weighell 78.146.98.63 (talk) 13:44, 11 December 2007 (UTC)

[edit] Diagram too confusing?

This is regarding the part with the diagram near the top of the article.

In this image, cold, warm, and hot are functions mapping a temperature scale. A point on that scale has three "truth values" — one for each of the three functions. For the particular temperature illustrated with the vertical line, the three truth values could be interpreted as describing the temperature as, say, "fairly cold" (blue arrow), "slightly warm" (yellow arrow), and "not hot" (red arrow).

The "not hot" at the end has been changed to "hot" several times now. This clearly indicates a lack of comprehension of what this is saying. I don't know if it's because they're only skimming the text and not really reading it, or they're reading it but not understanding it. In any case, I wonder if this could be worded better or something to prevent the confusion. - furrykef (Talk at me) 09:33, 30 January 2008 (UTC)

I've made a stab at revising the wording to make it clearer. I only saw two recent changes which deleted the "not", and it's not clear it wasn't the same person each time. Also unclear is whether it was an attempt at correction or simply vandalism. -R. S. Shaw (talk) 05:44, 31 January 2008 (UTC)

It is a combination of lack of comprehension and skimming. I stumbled upon this article and sure enough I just skimmed through until I hit the "Not Hot" and figured it was an error (until I read the article more thoroughly and read this talk page). But, at the same time, it is also a lack of comprehension since I had to read the article several times before fully understanding why it says "not hot". T3hZ10n (talk) 02:58, 13 May 2008 (UTC)

[edit] Examples where fuzzy logic is used

This section does not add to understanding of fuzzy logic. Maybe if explanation was given, otherwise the reader (at least this one) is left thinking "Rice cookers? Huh?" 81.174.226.229 (talk) 12:51, 6 February 2008 (UTC)

[edit] Article quality

This article is not really even start class, for it is chaotic, disorganized and contains errors, unjustified statements and haphazard assertions. Please do not rely on it. I will try to rewrite it over time. History2007 (talk) 11:16, 14 May 2008 (UTC)