id: 05361416
dt: a
an: 05361416
au: Tian, Jin; Li, Minqiang; Chen, Fuzan
ti: An evolutionary RBFNN learning algorithm for complex classzification
    problems.
so: Liu, Derong (ed.) et al., Advances in neural networks ‒ ISNN 2007. 4th
    international symposium on neural networks, ISNN 2007, Nanjing, China,
    June 3‒7, 2007. Proceedings, Part II. Berlin: Springer (ISBN
    978-3-540-72392-9/pbk). Lecture Notes in Computer Science 4492, 448-456
    (2007).
py: 2007
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-540-72393-6_54
ab: Summary: A self-optimizing approach for complex classifications is proposed
    in this paper to construct dynamical radial basis function neural
    network (RBFNN) models based on a specially designed genetic algorithm
    (GA). The algorithm adopts a matrix-form mixed encoding and
    specifically designed genetic operators to optimize the decayed-radius
    selected clustering (DRSC) process by co-evolving all of the parameters
    of the network’s layout. The individual fitness is evaluated as a
    multi-objective optimization task and the weights between the hidden
    layer and the output layer are calculated by the pseudo-inverse
    algorithm. Experimental results on eight UCI datasets show that the
    GA-RBFNN can produce a higher accuracy of classification with a much
    simpler network structure and outperform those models of neural network
    based on other training methods.
rv: