Artificial Hydrocarbon Networks in R

Quick Start

Here are some quick examples to get you started.

Create a network for a SISO sytem

The following code let’s you create the data to train an artificial hydrocarbon network.

library(ahnr)

# Create data
set.seed(12321)
x <- 2 * runif(1000) - 1;
x <- sort(x)

y <- (x < 0.1) * (0.05 * runif(100) + atan(pi*x)) + 
    (x >= 0.1 & x < 0.6) * (0.05 * runif(1000) + sin(pi*x)) + 
    (x >= 0.6) * (0.05 * runif(1000) + cos(pi*x))

plot(x, y, type = 'l')

# Create the Sigma dataset
Sigma <- list(X = data.frame(x = x), Y = data.frame(y = y))

# Create network
ahn <- AHNnD(Sigma, 5, 0.01, 500)

The trained network can be used to visualize its performance.

# Create test data
X <- data.frame(x = x)

# Simulate
ysim <- SimAHNnD(ahn, X)

plot(x, y, type = 'l')
lines(x, ysim, type = 'l', col = 'red')

A summary of the network can be obtained with the summary command.

summary(ahn)

## 
## Artificial Hydrocarbon Network trained:
## 
## Number of molecules:
##  5 
## 
## Learning factor:
##  0.01 
## 
## Overall error:
##  0.3206 
## 
## Centers of the molecules:
##                    x
## molecule1 -0.6863629
## molecule2 -0.1316634
## molecule3 -0.1305425
## molecule4 -0.4445938
## molecule5  0.2160453
## 
## Molecules:
## Molecule 1:
##          x
## C1  -0.259
## H11  2.164
## H12  1.696
## H13  0.510
## 
## Molecule 2:
##         x
## C2  0.051
## H21 3.596
## H22 3.337
## 
## Molecule 3:
##         x
## C3  0.025
## H31 3.168
## H32 1.385
## 
## Molecule 4:
##          x
## C4  -0.104
## H41  2.600
## H42  1.704
## 
## Molecule 5:
##           x
## C5   -1.020
## H51  14.852
## H52 -31.136
## H53  16.293

Finally, the network itself can be plotted with the plot command. The text of the carbon of the first molecule is in red.

plot(ahn)

Create a network for a MISO sytem

# Create data
t <- seq(0, 15, 0.01)
X <- data.frame(x1 = cos(t), x2 = t)
Y <- data.frame(y = sin(t))

# Create the Sigma dataset
Sigma <- list(X = X, Y = Y)

# Create network
ahn <- AHNnD(Sigma, 5, 0.01, 500)

# Simulate
ysim <- SimAHNnD(ahn, X)

plot(seq_along(Y$y), Y$y, type = 'l', col = 'black')
lines(seq_along(ysim), ysim, col = 'red')

summary(ahn)

## 
## Artificial Hydrocarbon Network trained:
## 
## Number of molecules:
##  5 
## 
## Learning factor:
##  0.01 
## 
## Overall error:
##  0.3523 
## 
## Centers of the molecules:
##                    x1          x2
## molecule1 -0.01279928 11.31135355
## molecule2  0.40496425 11.37478953
## molecule3 -0.53182701  0.46498680
## molecule4 -0.06948056  2.12529653
## molecule5 -0.51136596  8.93287402
## 
## Molecules:
## Molecule 1:
##         x1     x2
## C1  -0.459 -0.459
## H11 -0.872 -2.714
## H12  3.478  0.412
## H13  4.622 -0.015
## 
## Molecule 2:
##         x1     x2
## C2  -0.159 -0.159
## H21  1.056 -0.837
## H22 -0.653  0.066
## 
## Molecule 3:
##         x1     x2
## C3  -1.677 -1.677
## H31  4.262  1.013
## H32 -0.908  1.145
## 
## Molecule 4:
##         x1     x2
## C4   6.325  6.325
## H41 -4.521 -9.724
## H42 -0.931  1.448
## 
## Molecule 5:
##         x1     x2
## C5  -0.835 -0.835
## H51  0.286 -1.907
## H52 -0.270  0.578
## H53  0.039 -0.037

plot(ahn)