fuzzyis

FuzzyIS - fuzzy inference system library

It is a JavaScript library for building fuzzy inference systems.
Build your own system right in browser or with nodejs.

Install it!

npm install fuzzyis

Browser-ready minified version available at /dist directory. Nodejs usage: const fuzzyis = require('fuzzyis');

FuzzyIS contains 4 core objects:

• LinguisticVariable - thing described by fuzzy-terms like fast / slow, tall / low, hot / cold etc...
• Term - mentioned fuzzy term itself
• Rule - thing which describes connection between input and output linguistic variables. These are conditions like: "if food is tasty AND service is great -> tip should be generous", which describes how system works.
• FIS - fuzzy inference system. It is created with input and output linguistic variables and with described rules. FIS calculates precise values for output linguistic variables from precise values of input variables referring to the rules given.

Example about the tip

Let's see how we can model simple fuzzy inference system.

const fuzzyis = require('fuzzyis');
const system = new fuzzyis.FIS('Tip system');
const LV = fuzzyis.LinguisticVariable;

We need to know how much we should leave for officiant in percentage terms. Tip is output linguistic variable. We suppose it could be 'small', 'average' or 'generous'. And in precise values it could be from 0 to 30% from bill.

let outputs = [
    system.addOutputs(new LV('service', [0, 30]);
];

We'll draw our conclusion about the tip based on the quality of service and food quality. Food and Service are input linguistic variables. Let's agree that Food could be only 'bad' or 'good'. Service could be 'poor', 'normal', 'excellent'. We'll rate them both from 0 to 10 on an imaginary scale.

let inputs = [
    new LinguisticVariable('service', [0, 10]),
    new LinguisticVariable('food', [0, 10])
];
// take some shortcuts
let TIP = outputs[0];
let SERVICE = inputs[0];
let FOOD = inputs[1];

Now we should explain our fuzzy terms to the system and create the mentioned terms. Each term is described by a function. Let's define what good and bad food mean in terms of the imaginary 0 to 10 scale.

Same thing for service terms:

And for tip:

Code it:

SERVICE.addTerm(new Term('poor', 'gauss', [2.123, 0]));
SERVICE.addTerm(new Term('normal', 'gauss', [2.123, 5]));
SERVICE.addTerm(new Term('excellent', 'gauss', [2.123, 10]));

FOOD.addTerm(new Term('bad', 'trapeze', [0, 0, 1, 3]));
FOOD.addTerm(new Term('good', 'trapeze', [7, 9, 10, 10]));

TIP.addTerm(new Term('small', 'triangle', [0, 5, 10]));
TIP.addTerm(new Term('average', 'triangle', [10, 15, 20]));
TIP.addTerm(new Term('generous', 'triangle', [20, 25, 30]));

system.inputs = inputs;
system.outputs = outputs;

Now we should explain how tip depend on food and service to our system. Let's agree that the rules are as follows:

if service is poor AND food is bad -> tip is small

if service is normal -> tip is average

if service is excellent AND food is good -> tip is generous

system.rules = [
    new Rule(
        ['poor', 'bad'],
        ['small'],
        'and'
    ),
    new Rule(
        ['normal', null],
        ['average'],
        'and'
    ),
    new Rule(
        ['excellent', 'good'],
        ['generous'],
        'and'
    )
];

Now we have described our system and can ask it to calculate tip:

system.getPreciseOutput([7.892, 7.41])

Result is [17.40000000000002]. It returns an array with values of output variables.