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Learning procedure in a neural control model for the urinary bladder
Author(s) -
Bastiaanssen Erica H. C.,
Vanderschoot Jan,
van Leeuwen Johan L.
Publication year - 1993
Publication title -
neurourology and urodynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.918
H-Index - 90
eISSN - 1520-6777
pISSN - 0733-2467
DOI - 10.1002/nau.1930120314
Subject(s) - artificial neural network , gradient descent , medicine , urinary bladder , urology , mean squared prediction error , control (management) , control theory (sociology) , artificial intelligence , computer science , algorithm
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.