# Create vector of random data between 0 and 10, lenght 50
x <- sort(10*runif(50))

# We generate data from our fonction (that we need to approximate)
y <- 2*cos(x)+4

# We generate vector of 100 valors from 0 to 10 that we will use to draw the basic function and the one approximate with neural network (multilayer perceptron)
x1 <- seq(0, 10, by=0.1)

# Basic function (to draw the curve of the basic function that we need to approximate)
f <- 2*cos(x1)+4

# Charging library nnet (to apply neural network to our model)
library(nnet)

# We apply neural network to our generated data (random points of the function we want to approximate)
# We use one hidden layer, 6 neurons and 40 iteration
nn <- nnet(x, y, size=6, maxit=40, linout=TRUE)

# We display the 50 random generated points of our function
plot(x, y)

# Original function curve (2*cos+4)
lines(x1, f)

# Approximate function curve, generated by prediction on the results of the neural network
lines(x1, predict(nn, data.frame(x=x1)), col="green")