Construction of an Optimized Multilayer Neural Network Within a Nonlinear Model of Generalized Error
In this paper, we propose a method for optimizing the structure of a multilayer neural network based on minimizing nonlinear generalized error, which is based on the principle of minimum length of description. According to this principle, the generalized error is determined by the error in the description of the model and the error in the approximation of the data by the neural network in the nonlinear approximation.