Jairo LLC...
 Jairo LLC
Jairo LLC... Jairo LLC
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Jairo LLC
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Crypto Bot Station
Innovation project developed to allow your customers to automate their operations with digital assets simply and quickly.
It combines Fuzzy logic with artificial intelligence to provide high performance and effectiveness.
Provides a diverse ecosystem of solutions for your subscribers.
“Offer safe, innovative and personalized solutions; generating value and simplifying the financial operations of our customers.”
“Our view is to be a reference in the investment market where our customers use our solutions, obtaining real gains at less risk.”
- Ethic;
- Commitment to customers;
- Continuous learning;
- Diversity.
“Use technology to assist our customers personalized in their investment market operations bringing automation, safety and education to generate value.”
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Interesting initiatives will occur here. We will make content improvements and relate them to their interests.

We have steps to fulfill before setting the best content for you. Our intention and that in the next semester we will implement improvements.

IMPORTANT: The CryptoBotStation Project is scheduled for 2024.

Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false.[1] By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.

The term fuzzy logic was introduced with the 1965 proposal of fuzzy set theory by Iranian Azerbaijani mathematician Lotfi Zadeh.[2][3] Fuzzy logic had, however, been studied since the 1920s, as infinite-valued logic—notably by Łukasiewicz and Tarski.[4]

Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy). These models have the capability of recognising, representing, manipulating, interpreting, and using data and information that are vague and lack certainty.[5][6]

Fuzzy logic has been applied to many fields, from control theory to artificial intelligence.

A basic application might characterize various sub-ranges of a continuous variable. For instance, a temperature measurement for anti-lock brakes might have several separate membership functions defining particular temperature ranges needed to control the brakes properly. Each function maps the same temperature value to a truth value in the 0 to 1 range. These truth values can then be used to determine how the brakes should be controlled.[9] Fuzzy set theory provides a means for representing uncertainty.

In fuzzy logic applications, non-numeric values are often used to facilitate the expression of rules and facts.[10]

A linguistic variable such as age may accept values such as young and its antonym old. Because natural languages do not always contain enough value terms to express a fuzzy value scale, it is common practice to modify linguistic values with adjectives or adverbs. For example, we can use the hedges rather and somewhat to construct the additional values rather old or somewhat young.

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