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(123)
x <- 2 * runif(1000) - 1;
x <- sort(x)

y <- (x < 0.1) * (0.05 * runif(1000) + 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 <- fit(Sigma, 4, 0.01, 2000)

The trained network can be used to visualize its performance.

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

# Simulate
ysim <- predict(ahn, X)

plot(x, y, type = 'l')
lines(x, ysim, type = 'l', lty = 2, col = 'red')
legend(-1, 1, c('Original', 'Simulation'), col = c(1,2), lty = c(1,2), cex = 0.8)

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

summary(ahn)

## 
## Artificial Hydrocarbon Network trained:
## 
## Number of molecules:
##  4 
## 
## Learning factor:
##  0.01 
## 
## Overall error:
##  0.0615 
## 
## Centers of the molecules:
##                    x
## molecule1 -0.3900741
## molecule2  0.6661437
## molecule3  0.2555718
## molecule4  0.5338930
## 
## Molecules:
## Molecule 1:
##         x
## C1  0.035
## H11 3.527
## H12 3.709
## H13 1.466
## 
## Molecule 2:
##          x
## C2   3.089
## H21 -7.914
## H22  3.842
## 
## Molecule 3:
##          x
## C3   0.031
## H31  3.295
## H32 -2.165
## 
## Molecule 4:
##           x
## C4   -0.641
## H41   7.889
## H42 -11.506
## H43   4.771

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

visualize(ahn)

Create a network for a MISO sytem

# Create data
set.seed(12321)
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 <- fit(Sigma, 5, 0.01, 2000)

# Simulate
ysim <- predict(ahn, X)

plot(t, Y$y, type = 'l', col = 'black', xlab = 't', ylab = 'y')
lines(t, ysim, type = 'l', lty = 2, col = 'red')
legend(0, -0.5, c('Original', 'Simulation'), col = c(1,2), lty = c(1,2), cex = 0.6)

summary(ahn)

## 
## Artificial Hydrocarbon Network trained:
## 
## Number of molecules:
##  5 
## 
## Learning factor:
##  0.01 
## 
## Overall error:
##  0.0681 
## 
## Centers of the molecules:
##                   x1         x2
## molecule1  0.6711820  6.3923153
## molecule2  1.1138319  1.1193610
## molecule3  0.3259886  6.4597589
## molecule4 -0.2025691  4.1373693
## molecule5 -0.1651733 13.7288473
## 
## Molecules:
## Molecule 1:
##         x1     x2
## C1  -1.768 -1.768
## H11 -2.238 -5.561
## H12 -0.332  2.051
## H13  0.310 -0.162
## 
## Molecule 2:
##         x1     x2
## C2   0.051  0.051
## H21  0.102  1.047
## H22 -0.207 -0.302
## 
## Molecule 3:
##         x1     x2
## C3  -3.023 -3.023
## H31 -0.454  2.222
## H32 -0.362 -0.169
## 
## Molecule 4:
##        x1     x2
## C4  0.780  0.780
## H41 1.681  0.539
## H42 0.699 -0.230
## 
## Molecule 5:
##         x1      x2
## C5  -2.828  -2.828
## H51 -4.667 -18.164
## H52  1.908   2.992
## H53 -0.864  -0.118

visualize(ahn)