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BackPropagation through Cyclic Structures
2003Recursive neural networks are a powerful tool for processing structured data. According to the recursive learning paradigm, the information to be processed consists of directed positional acyclic graphs (DPAGs). In fact, recursive networks are fed following the partial order defined by the links of the graph. Unfortunately, the hypothesis of processing
Bianchini M. +3 more
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Modelling with constructive backpropagation
Neural Networks, 1999Neural network methods have proven to be powerful tools in modelling of nonlinear processes. One crucial part of modelling is the training phase where the model parameters are adjusted so that the model performs the desired operation as well as possible. Besides parameter estimation, an important problem is to select a suitable model structure.
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From Backpropagation to Neurocontrol
2011This chapter provides an overview of the most powerful practical tools developed so far, and under development, in the areas which the Engineering Directorate of National Science Foundation has called “cognitive optimization and prediction”. It deals with a condensed overview of key tools and discusses the historical background and the larger ...
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1996
We saw in the last chapter that multilayered networks are capable of computing a wider range of Boolean functions than networks with a single layer of computing units. However the computational effort needed for finding the correct combination of weights increases substantially when more parameters and more complicated topologies are considered.
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We saw in the last chapter that multilayered networks are capable of computing a wider range of Boolean functions than networks with a single layer of computing units. However the computational effort needed for finding the correct combination of weights increases substantially when more parameters and more complicated topologies are considered.
openaire +1 more source