Self-organizing neural networks by construction and pruning

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Feedforward neural networks have been successfully developed and applied in many areas because of their universal approximation capability. However, there still remains the problem of determining a suitable network structure for the given task. In this paper, we propose a novel self-organizing neural network which automatically adjusts its structure according to the task. Utilizing both the constructive and the pruning procedures, the proposed algorithm finds a near-optimal network which is compact and shows good generalization performance. One of its important features is reliability, which means the randomness of neural networks is effectively reduced. The resultant networks can have suitable numbers of hidden neurons and hidden layers according to the complexity of the given task. The simulation results for the well-known function regression problems show that our method successfully organizes near-optimal networks.
Publisher
IEICE-Inst Electronics Information Communications Eng
Issue Date
2004
Language
English
Article Type
Article
Keywords

FEEDFORWARD NETWORKS; IDENTIFICATION; ALGORITHM

Citation

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E87D, no.11, pp.2489 - 2498

ISSN
0916-8532
URI
http://hdl.handle.net/10203/80170
Appears in Collection
EE-Journal Papers(저널논문)
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