New article An Introduction to Fuzzy Logic has been published:
Fuzzy logic expands our boundaries of mathematical logic and set theory.
This article reveals the basic principles of fuzzy logic as well as
describes two fuzzy inference systems using Mamdani-type and Sugeno-type
models. The examples provided will describe implementation of fuzzy
models based on these two systems using the FuzzyNet library for MQL5.
The mathematical theory of fuzzy sets and fuzzy logic
itself originated back in 1965. Its founding father was a Professor
Lotfi Zadeh from the University of Berkeley, who first introduced both
concepts in his article "Fuzzy Sets" in the Information and Control
journal. This mathematical instrument allowed to introduce fuzzy
concepts, that anyone could use, to exact science like mathematics, and
laid the foundation for fundamentally new methods of problem solving on
the basis of soft computing.
All these innovations, when utilized properly, can greatly facilitate
the process of solving classification problems, creating expert systems
as well as building neural networks.
the practical application of fuzzy logic did not just stop there, in
fact, this mathematical instrument has become predominantly used in automatic control theory. This can be further linked to the emergence of another new concept — fuzzy model, which is a particular case of a mathematical model.
define a linguistic variable called "Age". By definition, "Age" is a
period, a step towards development and growth of a human, animal, plant.
The minimum value of this variable is 0, which means that a man is not
even a year old. As a maximum value 80 is set up. Depending on the
person's age we can give him the following assessment: "newborn",
"young", "middle-aged", "old", "senior" etc. This list can accommodate a
fairly large number of items. It will be a term set for our linguistic
variable and its elements will be terms.
figure below shows an example of the fuzzy variable "Age", which has
only three terms set up: "Young," "Middle-aged,","Old." Each of these
terms has its own membership function.
Author: MetaQuotes Software Corp.
As clearly stated in the title, this is an introduction to fuzzy logic, but that's very rough introduction, don't expect to fully understand it if you don't already know what is fuzzy logic.
The second part is commented code, with as usual examples not related at all to trading.
Hello, I dropt script on chart, and give me this error:
when i updated metatrader to build 2342
all of samples with fuzzy logic library
return error "incorrect casting of pointers" on MQL5\Include\Math\Fuzzy\RuleParser.mqh Line 712
please help to fix bug
Forum on trading, automated trading systems and testing trading
New MetaTrader 5 Platform Build
2340: Managing account settings in the Tester and expanded integration with Python
Sergey Golubev, 2020.03.02 12:00
thanks for your replay
real author is Dmitry Kalyuzhny. but his coded this library in .Net
But maybe someone else changed the code to mql
i see the author of this article is Дмитрий Калюжный
but I could not find Дмитрий Калюжный profile on mql5
Would you please help me to contact or fix this bug
return error "incorrect casting of pointers" on MQL5\Include\Math\Fuzzy\RuleParser.mqh Line
but I could not find Дмитрий Калюжный profile
I have a question regarding the 'linguistic Quantifiers': "slightly", "somewhat", "very", "extremely "...
What is the correct use of them in the ParseRule-function?
If I try something like this:
MamdaniFuzzyRule *rule1 = fsTips.ParseRule("if (service is poor) or (food is rancid) then (tips is very cheap)");
...I get this error: "Conclusion part of the rule should be in form: 'variable is term'"
Where would I have to place the 'very' in the string above?
Help would be much apreciated!
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