Fuzzy logic, invented by a man named Lotfi Zadeh in the mid-sixties, enables a computer to reason about linguistic terms and rules in a way similar to humans. Concepts like "far" or "slightly" are not represented by discrete intervals, but by fuzzy sets, enabling values to be assigned to sets to a matter of a degree - a process called fuzzification. Using fuzzified values computers are able to interpret linguistic rules and produce an output that may remain fuzzy or - more commonly, especially in videogames - can be defuzzified to provide a crisp value. This is known as fuzzy rule-based inference, and is one of the most popular uses of fuzzylogic. In this library is used the Combs Method. William Combs in 1997 Combs proposed a system that enables the number of rules to grow linearly with the number of member sets instead of exponentially.
var ai = new FuzzyLogic();
The only method you need to use to process the output is:
ai.getResult(object);
You need an array of input variables called variables_input and an array of input value called crisp_input: a crisp value for each variable. Every variable is composed by a set of function. In this library are used only trapezoidal function to achieve a goal: remain in a linear environment and get good performance. A trapezoidal function is composed by four numbers corresponding the four x coordinates.
The inferences, permit for every set of every input variable to define the corresponding set of the output variable called variable_output.
{
crisp_input: [NUMBER, ...],
variables_input: [
{
name: STRING,
setsName: [STRING, STRING, STRING, ...],
sets: [
[NUMBER, NUMBER, NUMBER, NUMBER],
[NUMBER, NUMBER, NUMBER, NUMBER],
[NUMBER, NUMBER, NUMBER, NUMBER],
...
]
},
...
],
variable_output: {
name: STRING,
setsName: [STRING, STRING, ...],
sets: [
[NUMBER, NUMBER, NUMBER, NUMBER],
[NUMBER, NUMBER, NUMBER, NUMBER],
...
]
},
inferences: [
[id_Ref_Output_Set, id_Ref_Output_Set, id_Ref_Output_Set, ...],
...
]
}
See the examples for more details.