On Monotonic Radial Basis Function Networks
研究了径向基函数网络的单调性条件，针对非归一化和归一化两种架构分别提出约束方法，将单调性转化为网络权重的线性约束，便于求解优化问题。
This article deals with monotonicity conditions for radial basis function (RBF) networks. Two architectures of RBF networks are considered-1) unnormalized network with a local character of the basis function and 2) a normalized network where the value of RBF is taken relatively with respect to the others. Different approaches are followed for each of them. For the former, monotonicity is enforced in prescribed points whereas for the latter sufficient monotonicity conditions are formulated. In both cases, the monotonicity conditions are expressed as linear constraints on the network weights that enable efficient solving of the related optimization problems. Many illustrative examples are presented to show the advantages of incorporating prior information in the form of monotonicity.