mlp_mse_fitness: MSE fitness function for MLP training
In SBOAtools: Secretary Bird Optimization for Continuous Optimization and Neural Network Training
mlp_mse_fitness R Documentation
MSE fitness function for MLP training
Description
Computes mean squared error for a single-hidden-layer MLP.
Usage
mlp_mse_fitness(params, X, Y, input_dim, hidden_dim, output_dim)
Arguments
params
Numeric parameter vector.
X
Input matrix.
Y
Output matrix.
input_dim
Number of input variables.
Number of hidden neurons.
output_dim
Number of output neurons.
Value
Mean squared error.
SBOAtools documentation built on May 3, 2026, 9:06 a.m.
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| mlp_mse_fitness | R Documentation |
MSE fitness function for MLP training
Description
Computes mean squared error for a single-hidden-layer MLP.
Usage
mlp_mse_fitness(params, X, Y, input_dim, hidden_dim, output_dim)
Arguments
params |
Numeric parameter vector. |
X |
Input matrix. |
Y |
Output matrix. |
input_dim |
Number of input variables. |
|
Number of hidden neurons. | |
output_dim |
Number of output neurons. |
Value
Mean squared error.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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