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» Evolving Complex Neural Networks
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ISCAS
1995
IEEE
97views Hardware» more  ISCAS 1995»
15 years 1 months ago
A New Paradigm for Developing Digital Systems Based on a Multi-Cellular Organization
Embryological electronics or “Embryonics” is a new paradigm for developing digital systems of any complexity, endowed of universal computation, self-repair and self-reproducti...
Daniel Mange, Serge Durand, Eduardo Sanchez, Andr&...
ASC
2006
14 years 11 months ago
Supervised neuronal approaches for EEG signal classification: Experimental studies
Using artificial neural networks for Electroencephalogram (EEG) signal interpretation is a very challenging tasks for several reasons. The first class of reasons refers to the nat...
Frédéric Alexandre, Kerkeni Nizar, K...
IJON
2007
184views more  IJON 2007»
14 years 10 months ago
Convex incremental extreme learning machine
Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden n...
Guang-Bin Huang, Lei Chen
JMLR
2008
141views more  JMLR 2008»
14 years 10 months ago
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
Many complex control problems require sophisticated solutions that are not amenable to traditional controller design. Not only is it difficult to model real world systems, but oft...
Faustino J. Gomez, Jürgen Schmidhuber, Risto ...
IWANN
2005
Springer
15 years 3 months ago
Co-evolutionary Learning in Liquid Architectures
A large class of problems requires real-time processing of complex temporal inputs in real-time. These are difficult tasks for state-of-the-art techniques, since they require captu...
Igal Raichelgauz, Karina Odinaev, Yehoshua Y. Zeev...