Papers

@conference{Gestal2006323,

title = {Description of RANNs and their generalisation capabilities by means of rule extraction by genetic programming},

author = {Marcos Gestal and Juan R. Rabuñal-Dopico and Julián Dorado and Javier Pereira},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-56149111314&partnerID=40&md5=739d0f3a9cbdfb7842154980eb6b540a},

isbn = {0889866104; 9780889866102},

year  = {2006},

date = {2006-01-01},

urldate = {2006-01-01},

journal = {Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2006},

pages = {323-328},

abstract = {Artificial Neural Networks have achieved satisfactory results in different fields such as example classification or image identification. Real-world processes usually have a temporal evolution, and they are the type of processes where Recurrent Networks have special success. Nevertheless they are still reluctantly used, mainly due to the fact that they do not adequately justify their response. But, if ANNs offer good results, why giving them up? Suffice it to find a method that might search an explanation to the outputs that the ANN provides. This work presents a technique, totally independent from ANN architecture and the learning algorithm used, which makes possible the justification of the ANN outputs by means of expression trees.},

note = {cited By 0},

keywords = {Artificial intelligence; Backpropagation; Computer programming; Electric fault location; Genetic algorithms; Genetic programming; Learning algorithms; Learning systems; Recurrent neural networks; Soft computing; Trees (mathematics), Artificial neural networks; Expression trees; Generalisation; Generalisation capabilities; Image identifications; Recurrent artificial neural networks; Recurrent networks; Rule extraction; Rule extractions; Series prediction; Temporal evolutions, Neural networks},

pubstate = {published},

tppubtype = {conference}

}