Abstract: Radial basis function(RBF)neural network was a type of efficient feedforward neural network, which had simple structure and good generalization ability. It had been widely used in data classification. However, for some special classification scenarios, such as the scenarios of dealing with the monotonic data, RBF neural network could not fully realize its potential. For this challenge, monotonic radial basis function neural network(MC-RBF)was proposed. The model added a prior knowledge about monotonicity which was expressed in terms of inequality based on partial order of training data. The Tikhonov regularization was introduced to MC-RBF to ensure the uniqueness and boundedness of the solution of the optimization problem. The experimental results showed that MC-RBF had better classification performance than the classical RBF neural network when dealing with monotonic datasets.

Key words: monotonic constraint, data classification, Tikhonov regularization, radial basis function neural network, classification performance