TY - CHAP
T1 - Explicit Center Selection and Training for Fault Tolerant RBF Networks
AU - Wong, Hiu Tung
AU - Wang, Zhenni
AU - Leung, Chi-Sing
AU - Sum, John
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2019
Y1 - 2019
N2 - Although some noise tolerant center selection training algorithms for RBF networks have been developed, they usually have some disadvantages. For example, some of them cannot select the RBF centers and train the network simultaneously. Others do not allow us to explicitly define the number of RBF nodes in the resultant network, and we need to go through a time consuming procedure to tune the regularization parameter such that the number of RBF nodes used satisfies our pre-specified value. Therefore, it is important to develop some noise resistant algorithms that allow us to specify the number of RBF nodes in the resultant network. In addition, they should be able to train the network and to select RBF nodes simultaneously. This paper formulates the RBF training problem as a generalized M-sparse problem. We first define a noise tolerant objective function for RBF networks. Afterwards, we formulate the training problem as a generalized M-sparse problem, in which the objective function is the proposed noise tolerant training objective function and the constraint is an l0-norm of the weight vector. An iterative algorithm is then developed to solve this generalized M-sparse problem. From simulation experiments, the proposed algorithm is superior to the state-of-art noise tolerant algorithms. In addition, the proposed algorithm allows us to explicitly define the number of RBF nodes in the resultant network. We prove that the algorithm converges and that the fixed points of the proposed algorithms are the local minimum of this generalized M-sparse problem.
AB - Although some noise tolerant center selection training algorithms for RBF networks have been developed, they usually have some disadvantages. For example, some of them cannot select the RBF centers and train the network simultaneously. Others do not allow us to explicitly define the number of RBF nodes in the resultant network, and we need to go through a time consuming procedure to tune the regularization parameter such that the number of RBF nodes used satisfies our pre-specified value. Therefore, it is important to develop some noise resistant algorithms that allow us to specify the number of RBF nodes in the resultant network. In addition, they should be able to train the network and to select RBF nodes simultaneously. This paper formulates the RBF training problem as a generalized M-sparse problem. We first define a noise tolerant objective function for RBF networks. Afterwards, we formulate the training problem as a generalized M-sparse problem, in which the objective function is the proposed noise tolerant training objective function and the constraint is an l0-norm of the weight vector. An iterative algorithm is then developed to solve this generalized M-sparse problem. From simulation experiments, the proposed algorithm is superior to the state-of-art noise tolerant algorithms. In addition, the proposed algorithm allows us to explicitly define the number of RBF nodes in the resultant network. We prove that the algorithm converges and that the fixed points of the proposed algorithms are the local minimum of this generalized M-sparse problem.
KW - Center selection
KW - Fault tolerance
KW - RBF network
UR - http://www.scopus.com/inward/record.url?scp=85076880349&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85076880349&origin=recordpage
U2 - 10.1007/978-3-030-36711-4_24
DO - 10.1007/978-3-030-36711-4_24
M3 - RGC 12 - Chapter in an edited book (Author)
SN - 9783030367107
T3 - Lecture Notes in Computer Science
SP - 273
EP - 285
BT - Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
A2 - Gedeon, Tom
A2 - Wong, Kok Wai
A2 - Lee, Minho
PB - Springer
T2 - 26th International Conference on Neural Information Processing (ICONIP 2019)
Y2 - 12 December 2019 through 15 December 2019
ER -