% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sboa_mlp.R
\name{sboa_mlp}
\alias{sboa_mlp}
\title{Train a single-hidden-layer MLP using SBOA}
\usage{
sboa_mlp(
  X_train,
  y_train,
  hidden_dim = 10,
  n_agents = 30,
  max_iter = 500,
  lower = -1,
  upper = 1,
  seed = NULL,
  verbose = TRUE
)
}
\arguments{
\item{X_train}{Training input data.}

\item{y_train}{Training output data.}

\item{hidden_dim}{Number of hidden neurons.}

\item{n_agents}{Number of search agents.}

\item{max_iter}{Maximum number of iterations.}

\item{lower}{Lower bound for parameter search.}

\item{upper}{Upper bound for parameter search.}

\item{seed}{Optional random seed.}

\item{verbose}{Logical; if \code{TRUE}, progress is printed.}
}
\value{
An object of class \code{"sboa_mlp"}.
}
\description{
Trains a single-hidden-layer multilayer perceptron with the
Secretary Bird Optimization Algorithm.
}
\examples{
set.seed(123)

X_train <- matrix(runif(40), nrow = 10, ncol = 4)
y_train <- matrix(runif(10), nrow = 10, ncol = 1)

fit <- sboa_mlp(
  X_train = X_train,
  y_train = y_train,
  hidden_dim = 3,
  n_agents = 10,
  max_iter = 20,
  lower = -1,
  upper = 1,
  seed = 123,
  verbose = FALSE
)

print(fit)
pred <- predict(fit, X_train)
head(pred)
}
\references{
Dilber, B., and Ozdemir, A. F. (2026). A novel approach to training
feed-forward multi-layer perceptrons with recently proposed secretary bird
optimization algorithm. Neural Computing and Applications.
DOI: 10.1007/s00521-026-11874-x
}
