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This work describes a toolbox of nonlinear regression models developed on an open-source platform of Scilab. The models are formed from radial basis function (RBF) neural network structures. For a fast calculation of the models, we adopt a linear solver in implementations. A specific effort is made on applications of linear priors, which presents a unique feature different from other existing regression...
In this work, we present a study of nonlinear modelings based on RBF networks. The incorporation of prior knowledge in modelings is our specific concern for adding transparency and improving the performance of the networks. We focus on the prior knowledge within the class of linear constraints, which includes both linear equality and linear inequality constraints. Different with other existing modeling...
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