**Previous message:**David Olmsted: "Re: Humans think fuzzy?"**Next in thread:**Russ Heersberger: "Re: Humans think fuzzy?"**Messages sorted by:**[ date ] [ thread ] [ subject ] [ author ]

Ο Marcello Savarese <marcello.savarese@tin.it> έγραψε στο μήνυμα συζήτησης:

asUJ6.1030$2i2.24525@news1.tin.it...

*> I read with attention you response about this subject....
*

*> I'm agree with you considerations on argument, but I think that the
*

*> imprecision is the natural aspect of fuzzy logic .
*

*> I think that the uncertainty is linked whit probability concept.
*

*> If I say that an object ( eg. apple) is 0.75( or 75%) an apple I expose a
*

*> uncertainty but if I say this obj belongs 0.8 to apple membership, I
*

*> means an imprecision.
*

*> So in the probability topic I say that this obj is or isn't an apple
*

with

*> grade of probability( in ex: 75%)) ( uncertainty), in the Fuzzy topic I
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say

*> that this obj is a "wrong apple" but is an apple( imprecision).
*

*> I hope that I explained my concept in the better manner.....
*

*> I love to say that probability is a distance measure from true and the
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fuzzy

*> is a distance measure from real.
*

*>
*

*> Where my reasoning is wrong?
*

*> What do you think about it?
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*>
*

Hello,

The following reply might be useful:

*> Hi everyone,
*

*>
*

*> A neuron clearly showing the fuzzy logic INCLUSIVE OR property of passing
*

the

*> maximum value among it inputs has been found in
*

*> the auditory system of the frog brain (see the home page of my web site
*

*> http://neurocomputing.org for the data and reference). Yet neural signals
*

have

*> more than one parameter (dimension) unlike the values used in conventional
*

logic

*> and probability. Neural signals have a pulse width (number of action
*

potentials)

*> and within that pulse the action potentials have a frequency. In addition
*

the

*> frequency has a variance and often a decay. Finally, a variable latency
*

often

*> occurs before the pulse is triggered.
*

*>
*

All true. Even more, R.Penrose suggests a rather quantum neural model for

the brain cells which takes into account quantum effects in signal

propagation, pointing to a theory as complex as (maybe) hyperstrings for

physics. However, this does not mean that current implementations a

deprecated (Newton's laws still stand, even as a sub-set, but quite real and

descriptive). With neural nets, we do not want to construct an exact

formulation for the biological neurons, but simply to simulate their basic

fuctionality. Neural networks seem to have accomplished that in many cases,

whether low-level signal processing or high-level logic functions. The

problem is not whether the NN can learn to act as a logic unit, but rather

how AI can help that this unit has the congitive knowledge of what it does.

*> Certainly neural signals have a validity value in addition to a truth
*

value to

*> make it at least a 2-dimensional multivalued logic (a generalization of
*

fuzzy and

*> modal logic as pointed out by Stephen Lehmke in a posting to this
*

newsgroup). But

*> how are these 2 dimensions used and defined? How do they interact? Is
*

there a

*> relation to probability? And what about their variances?
*

The vector space of a NN can actually contain many thousands of dimensions,

this does not mean that we should try and translate them all into

multi-dimensional logic. NN try to fit an adaptive system into a set of

pre-determined constraints (not necessarily logic ones), not describe the

cognitive attributes of the environment which is implied by them (NN is not

AI in terms of inference logic).

*> Probability and logic and not independent. Consider this question about a
*

car

*> belonging to the sample set of vehicles. "What is the probability that a
*

car is

*> behind a wall?" The probability depends upon the precision of the
*

definition for

*> "car". Does "car" include a pick-up truck, a sport utility vehicle, a van?
*

I can not agree with that. Probability has nothing to do with fuzziness.

Propability applies to mutually exclusive states, while fuzziness applies

when things belong to more than one state at the same time. The proposition

"What is the probability that a car is behind a wall?" is clearly predicate

logic (with the addition of probability calculations). The same question in

terms of fuzziness should be "How much of the car is behind the wall?" - it

may sound strange but it stands quite well (with value from 0 to 1), as long

as we forget our perception that a car can not actually be in front and

behind the wall (two-valued logic => mutually exclusive states =>

propability calculations).

___________________________

Harris Georgiou

Informatics Systems Analyst (MSc)

mailto:xgeorgio@hol.gr

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