NETTOOL: A Hybrid Connectionist-Symbolic Development Environment
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Training Higher Order Gaussian Synapses
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
Linear and Algorithmic Formulation of Co-operative Computation in Neural Nets
EUROCAST '91 Proceedings of the A Selection of Papers from the Second International Workshop on Computer Aided Systems Theory
Performance enhancement using nonlinear preprocessing
IEEE Transactions on Neural Networks
Discrete-time backpropagation for training synaptic delay-based artificial neural networks
IEEE Transactions on Neural Networks
High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
Detecting coherent structures in a turbulent wake by using delay based networks
Computer Standards & Interfaces - Intelligent data acquisition and advanced computing systems
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This paper introduces a neural network that combines the power of two different approaches to obtaining more efficient neural structures for processing complex real signals: the use of trainable temporal delays in the synapses and the inclusion of product terms within the combination function. In addition to the neural network structure itself, the paper presents a new algorithm for training this particular type of networks and provides a set of examples using chaotic series, which compare the results obtained by these networks and training algorithm to other structures.