Monotonicity conditions for radial basis function networks
2018 IEEE Symposium Series on Computational Intelligence (SSCI)（2018）
摘要
The paper presents a way how to impose monotonicity requirement on radial basis function networks. Monotonicity conditions are expressed as linear constraints on the network weights that enables efficient solving of the related optimization problems. Two illustrative examples are given to demonstrate advantages of incorporation a prior information in the form of monotonicity.
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关键词
Radial basis function networks,Prior knowledge,Monotonic regression,Least square approximation
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