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Neural Networks, IEEE - INNS - ENNS International Joint Conference on (2000)
Como, Italy
July 24, 2000 to July 27, 2000
ISSN: 1098-7576
ISBN: 0-7695-0619-4
pp: 4569
F. Rosati , Universit? di Ancona
P. Campolucci , Universit? di Ancona
F. Piazza , Universit? di Ancona
ABSTRACT
A large class of non-linear dynamic adaptive systems such as dynamic recurrent neural networks can be very effectively represented by Signal-Flow-Graphs (SFGs). By this method, complex systems are described as a general connection of many simple components, each of them implementing a simple one-input one-output transformation, as in an electrical circuit. Even if graph representations are popular in the neural network community, they are often use d for qualitative description rather than for rigorous representation and computational purposes. Following an approach originally developed by A.Y. Lee for continuous-time systems based on the concept of adjoint graph, a new algorithm to estimate the derivative of the output with respect to an internal parameter was recently proposed by some of the authors for discrete-time systems. This paper extends further this approach to multirate digital systems, which are nowadays widely used. The new method can be employed for gradient-based learning of general multirate circuits, such as new “multirate” neural networks.
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CITATION

F. Piazza, F. Rosati and P. Campolucci, "A General Approach to Gradient Based Learning in Multirate Systems and Neural Networks," Neural Networks, IEEE - INNS - ENNS International Joint Conference on(IJCNN), Como, Italy, 2000, pp. 4569.
doi:10.1109/IJCNN.2000.860832
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