Simple perceptron just as I wrote it in node JS.

perceptron.js

// Sigmoid function
function S(num){
  var y = 1 / (1 + Math.exp(num));  // y = 1/(1+e^x)
  return(y)
}

// Training function
function process(arr) {
  var w = []
  var bias = 0.5
  var neta = 0.5
  var epoch = 2500
  var temp = ""

  w[0] = Math.random()
  w[1] = Math.random()
  w[2] = Math.random()

  for (var ip = 0; ip < epoch; ip++) {

    for (var i = 0; i < arr.length; i++) {
      var x = arr[i].x
      var y = arr[i].y
      var tar = arr[i].tar
      var out = S(x*w[0] + y*w[1] + bias*w[2])

      var diff = tar-out

      var E = 0.5 * Math.pow(diff,2)

      var delta =  (out-tar)*(out*(1-out))
      w[0] = w[0]+(neta*delta*x)
      w[1] = w[1]+(neta*delta*y)
      w[2] = w[2]+(neta*delta*bias)

      var medium = (ip*4+i) + "," + E + "\n"
      temp = temp + medium
    }

    if (isNaN(E)) {
      break
    }
    //
    // if (E < 0.00001) {
    //   console.log('true');
    //   console.log(E);
    //   console.log(w);
    //   break
    // }
    // else {
    //   console.log(E);
    //   console.log('false');
    // }
  }

  var fs = require('fs');
  fs.writeFile('error.csv', temp, function (err) {
      if (err)
          return console.log(err);
      console.log(w);
  });
}

// Testing function
function run(arr) {
  // OR weights
  // var wx =   -6.145714143824315
  // var wy = -6.14594481839442
  // var wbias =  5.393654370020651

  // AND weights
  // var wx =  -4.6549735181828
  // var wy =  -4.654060764413737
  // var wbias = 14.031695517058715

  //xor weights
  var wx = 0.0796488512108033
  var wy = 0.0637190809265625
  var wbias = -0.07964885084210521

  var bias = 0.5

  for (var i = 0; i < arr.length; i++) {
    var x = arr[i].x
    var y = arr[i].y
    var tar = arr[i].tar

    var out = S(x*wx + y*wy + bias*wbias)
    console.log('Target - ' + tar);
    console.log('Output - ' + out);

  }
}

var arr = [
  {
    "x" : 0, "y": 0, "tar": 0
  },
  {
    "x" : 0, "y": 1, "tar": 1
  },
  {
    "x" : 1, "y": 0, "tar": 1
  },
  {
    "x" : 1, "y": 1, "tar": 0
  }
]


// process(arr)
// run(arr)

//read the detailed blog post : https://justruky.blogspot.com/2016/07/from-biological-neuron-to-perceptron.html