Learning procedure in a neural control model for the urinary bladder

Abstract

A continuous neural network coupled to a dynamical model of the urinary bladder is defined. The neural network is trained to control the bladder model to track a prescribed volume fluctuation, by adjusting weights and time constants. The gradients of the error in the output neurons of the neural network are unknown. Therefore, the learning procedure discussed here minimizes the error functional without using gradient descent.