ResilientBackpropagation Properties

Gets the decrease parameter, also referred as eta minus. Default is 0.5.

Gets or sets the function to be optimized.

Gets or sets a function returning the gradient vector of the function to be optimized for a given value of its free parameters.

Gets the increase parameter, also referred as eta plus. Default is 1.2.

Gets or sets the maximum number of iterations performed by the learning algorithm.

Gets the number of variables (free parameters) in the optimization problem.

Gets the current solution found, the values of the parameters which optimizes the function.

Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.

Gets or sets the maximum change in the average log-likelihood after an iteration of the algorithm used to detect convergence.

Gets or sets the minimum possible update step, also referred as delta max. Default is 1e-6.

Gets or sets the maximum possible update step, also referred as delta min. Default is 50.

Gets the output of the function at the current Solution.