Problem with"Artificial Neural Network"

[se7en0803]

To build a feedforward artificial neural network model with 1 hidden layer and present the key results of the model for predicting air blast waves based on the given hypothetical data, let's first translate the variables to English:

• Lb: Length of blasting column, m
• Wct: Bottom charge burden, m
• b: Distance between blastholes, m
• Q: Maximum total explosive charge per delay, Kg
• q: Explosive density, Kg/m3
• D: Distance of air blast wave monitoring, m
• AOP: Air blast wave overpressure, dB

My code:

library(readxl)
data <- read_excel("D:/data.xlsx")
View(data)
library(neuralnet)

Split the data into training and test sets (80% for training, 20% for testing)

train_data <- data[1:80, c("Lb", "Wct", "b", "Q", "q", "D")]
train_labels <- data[1:80, "AOP"]
test_data <- data[81:nrow(data), c("Lb", "Wct", "b", "Q", "q", "D")]
test_labels <- data[81:nrow(data), "AOP"]

Normalize the data (optional but recommended)

train_data_norm <- scale(train_data)
test_data_norm <- scale(test_data)

Build the neural network model

model <- neuralnet(AOP ~ Lb + Wct + b + Q + q + D, data = train_data_norm, hidden = c(10))
Error in eval(predvars, data, env) : object 'AOP' not found

Can anyone help me complete this code? THANKS!

[technocrat]

@se7en0803

Error in eval(predvars, data, env) : object 'AOP' not found

There must be a variable by this name in train_data_norm

[se7en0803]

library(readxl)
data <- read_excel("D:/Baitap/data.xlsx")
View(data)

Load the neuralnet package

library(neuralnet)

Split the data into training and test sets (80% for training, 20% for testing)

train_data <- data[1:80, c("Lb", "Wct", "b", "Q", "q", "D", "AOP")]
train_labels <- data[1:80, "AOP"]
test_data <- data[81:100, c("Lb", "Wct", "b", "Q", "q", "D", "AOP")]
test_labels <- data[81:100, "AOP"]

Normalize the data (optional but recommended)

train_data_norm <- scale(train_data)
test_data_norm <- scale(test_data)

Build the neural network model

model <- neuralnet(AOP ~ Lb + Wct + b + Q + q + D, data = train_data_norm, hidden = c(10))

Make predictions on the test set

predictions <- compute(model, test_data_norm)$net.result

Convert test_labels to a vector

test_labels <- unlist(test_labels)

Convert predictions and test_labels to numeric

predictions <- as.numeric(predictions)
test_labels <- as.numeric(test_labels)

Calculate the mean squared error

mse <- mean((predictions - test_labels)^2, na.rm = TRUE)
#Calculate RMSE from MSE
rmse <- sqrt(mse)

Print the key results

cat("Root Mean Squared Error (RMSE):", rmse, "\n")
Root Mean Squared Error (RMSE): 110.553
predictions
[1] -0.27514569 0.60132493 0.93942980 -0.09514141 -0.57754302
[6] -0.03344658 -0.99636708 -0.53039975 0.72270901 0.69162210
[11] 0.36900769 1.66349748 -0.74943723 -0.80595008 -1.41068713
[16] -0.15363472 -1.29964997 -0.14979083 1.07483063 -0.24234422

Thank you very much, the code worked, and i hope it will be correct for the given data:

Lb	Wct	b	Q	q	D	AOP
2.9	1.6	3.9	83.9	0.75	535	102.5
2.8	3	3.4	75.6	0.6	397	110.4
3	3.4	3	71.8	0.66	398	110.2
2.5	2.9	2.9	66.8	0.84	346	106.8
3.5	2.1	4.1	65.1	0.79	476	91.5
3.2	1.8	3.6	76.6	0.78	296	116.8
3.3	3	3.8	66.1	0.57	533	93.5
2.2	1.9	4.2	73	0.84	393	105.4
3.1	2.2	3	71.8	0.75	346	110.1
...