Learning a Simple Recurrent Neural State Space Model to Behave Like Chua's Double Scroll

IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications

Volumn 42, Issue 8, 1995, Pages 499-502

  Suykens, Johan A K ^a   Vandewalle, Joos ^a  

^a Katholieke Universiteit Leuven   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; CHAOS THEORY; COMPUTER SIMULATION; ELECTRIC NETWORK SYNTHESIS; LEARNING SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION; RANDOM PROCESSES; SENSITIVITY ANALYSIS; STATE SPACE METHODS; STATISTICAL METHODS; VECTORS;

CHUA DOUBLE SCROLL; LEARNING RECURRENT NETWORKS; NARENDRA DYNAMIC BACKPROPAGATION; NEURAL STATE SPACE MODEL; NEURONS; RANDOM INITIAL PARAMETER VECTORS; RECURRENT NETWORK ARCHITECTURE;

NEURAL NETWORKS;

EID: 0029357641     PISSN: 10577122     EISSN: None     Source Type: Journal    
DOI: 10.1109/81.404066     Document Type: Article

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