CTRNN parameter learning using differential evolution

Frontiers in Artificial Intelligence and Applications

Volumn 178, Issue , 2008, Pages 783-784

  De Falco, Ivanoe ^a   Cioppa, Antonio Della ^b   Donnarumma, Francesco ^c   Maisto, Domenico ^a   Prevete, Roberto ^c   Tarantino, Ernesto ^a  

^a ICAR CNR   (Italy)

^b UNIVERSITY OF SALERNO   (Italy)

^c UNIVERSITY OF NAPLES FEDERICO II   (Italy)

Author keywords

CTRNN; Differential Evolution; Dynamical Systems; Genetic Algorithms

Indexed keywords

CONTINUOUS TIME SYSTEMS; DYNAMICAL SYSTEMS; RECURRENT NEURAL NETWORKS;

AGENT CONTROL SYSTEMS; BLOCKINGS; BOUNDEDNESS; CONTINUOUS-TIME RECURRENT NEURAL NETWORKS; DIFFERENTIAL EVOLUTION; INITIAL SUPPORT; NEURAL NETWORK PARAMETERS; PARAMETER LEARNING; PARAMETER SPACES; SEARCH PARAMETERS;

GENETIC ALGORITHMS;

EID: 84957801420     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: 10.3233/978-1-58603-891-5-783     Document Type: Conference Paper

References (5)

1. Ken-ichi Funahashi and Yuichi Nakamura, ‘Approximation of dynamical systems by continuous time recurrent neural networks’, Neural Networks, 6(6), 801–806, (1993).
2. David E. Goldberg, ‘Real-coded genetic algorithms, virtual alphabets, and blocking’, Complex Systems, 5, 139–167, (1991).
3. J. K. Hale and H Kocac, Dynamics and Bifurcations, Springer-Verlag, 1991.
4. K Price, R Storn, and Lampinen J, Differential Evolution: A Practical Approach to Global Optimization, Natural Computing Series, Springer-Verlag, 2005.
5. Brian M. Yamauchi and Randall D. Beer, ‘Sequential behavior and learning in evolved dynamical neural networks’, Adaptive Behavior, 2(3), 219–246, (1994).