ImNN: Neural Networks for Predicting Volume of Forest Trees

In ImNN: Neural Networks for Predicting Volume of Forest Trees

Neural Networks for Predicting Volume of Forest Trees

Description

Neural Networks for Predicting Volume of Forest Trees

Usage

ImNN(data, hidden_neurons_range)

Arguments

data	Datasets
hidden_neurons_range	Number of hidden neurons in neural network's two layers (layer 1 and layer 2)

Value

• results: Results

References

• Jeelani, M.I., Tabassum, A., Rather, K and Gul,M.2023. Neural Network Modeling of Height Diameter Relationships for Himalayan Pine through Back Propagation Approach. Journal of The Indian Society of Agricultural Statistics. 76(3): 169–178

• Tabassum, A., Jeelani, M.I., Sharma,M., Rather, K R ., Rashid, I and Gul,M.2022. Predictive Modelling of Height and Diameter Relationships of Himalayan Chir Pine . Agricultural Science Digest - A Research Journal. DOI:10.18805/ag.D-5555

Examples

library("ImNN")
data <- system.file("extdata", "data_test.csv", package = "ImNN")
data_test <- read.csv(data)
Model<-ImNN(data =data_test,hidden_neurons_range=2)

ImNN documentation built on Oct. 13, 2023, 1:09 a.m.