Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Logistic Networks With DNA-Like Encoding and Interactions
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Artificial neural networks based on bifurcating recursive processing elements
Artificial neural networks based on bifurcating recursive processing elements
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
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This paper addresses quantitative techniques for the design and characterization of artificial neural networks based on Chaotic Neural Nodes, Recursive Processing Elements, and Bifurcation Neurons. Such architectures can be programmed to store cyclic patterns, having as important applications spatio temporal processing and computation with non fixed-point attractors. The paper also addresses the performance measurement of associative memories based on Recursive Processing Elements, considering situations of analog and digital noise in the prompting patterns, and evaluating how this noise reflects in the Hamming distance between the desired stored pattern and the answer pattern produced by the neural network.