Name
Abstract | ||
|---|---|---|
Target behaviours can be achieved by finding suitable parameters for Continuous Time Recurrent Neural Networks (CTRNNs) used as agent control systems. Differential Evolution (DE) has been deployed to search parameter space of CTRNNs and overcome granularity, boundedness and blocking limitations. In this paper we provide initial support for DE in the context of two sample learning problems. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.3233/978-1-58603-891-5-783 | ECAI |
Keywords | Field | DocType |
initial support,continuous time recurrent neural,differential evolution,suitable parameter,agent control system,ctrnn parameter learning,target behaviour,parameter space | Mathematical optimization,Computer science,Recurrent neural network,Differential evolution,Parameter learning,Dynamical systems theory,Parameter space,Artificial intelligence,Granularity,Control system,Genetic algorithm,Machine learning | Conference |
Volume | ISSN | Citations |
178 | 0922-6389 | 2 |
PageRank | References | Authors |
0.40 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ivanoe De Falco | 1 | 242 | 34.58 |
| Antonio Della Cioppa | 2 | 141 | 20.70 |
| Francesco Donnarumma | 3 | 42 | 5.89 |
| D. Maisto | 4 | 146 | 11.20 |
| Roberto Prevete | 5 | 138 | 20.67 |
| Ernesto Tarantino | 6 | 361 | 42.45 |