Abstract:
:The configuring of a radial basis function network (RBFN) consists of selecting the network parameters (centers and widths in RBF units and weights between the hidden and output layers) and network architecture. The issues of suboptimum and overfitting, however, often occur in RBFN configuring. This paper presented a hybrid particle swarm optimization (HPSO) algorithm to simultaneously search the optimal network structure and parameters involved in the RBFN (HPSORBFN) with an ellipsoidal Gaussian function as a basis function. The continuous version of PSO was used for parameter training, while the modified discrete PSO was employed to determine the appropriate network topology. The proposed HPSORBFN algorithm was applied to modeling the inhibitory activities of substituted bis[(acridine-4-carboxamide)propyl]methylamines to murine P388 leukemia cells and the bioactivities of COX-2 inhibitors. The results were compared with those obtained from RBFNs with the parameters optimized by continuous PSO and by conventionally RBFN training the algorithm for a fixed network topology, indicating that the HPSO was competent for RBFN configuring in that it converged quickly toward the optimal solution and avoided overfitting.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Zhou YP,Jiang JH,Lin WQ,Zou HY,Wu HL,Shen GL,Yu RQdoi
10.1021/ci600218dsubject
Has Abstractpub_date
2006-11-01 00:00:00pages
2494-501issue
6eissn
1549-9596issn
1549-960Xjournal_volume
46pub_type
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