LEARNING PROCEDURE IN A NEURAL CONTROL MODEL FOR THE URINARY-BLADDER (REPRINTED FROM PROCEEDINGS OF THE 14TH ANNUAL INTERNATIONAL-CONFERENCE OF THE IEEE-ENGINEERING-IN-MEDICINE-AND-BIOLOGY-SOCIETY, OCTOBER 29 TO NOVEMBER 1, 1992, PARIS, FRANCE, VOLUME 14, PG 1508-1509, 1992)

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.