Although input vector properties are the main determinants of neural network performance, most studies use a simple linear scaling model to normalize the input vector without considering input vector normalization. Because single normalization maps only a single property of the input data, the current normalization approach may not be effective for improving prediction accuracy. In addition, a linear type of normalization is just a linear mapping that mirrors raw data properties. On the other hand, non-linear normalization can reflect non-linearly by emphasizing certain properties. This study aims to propose a new and efficient normalization method that can provide better prediction performance by learning machines. For this purpose, various regularization methods such as linear and nonlinear mapping models and a combination of the two models were considered. The research methodology is as follows. a) Based on theoretical studies on linear and nonlinear models of regularization methods, a method that combines two different normalization methods is proposed. b) Data are prepared for experiments in neural network models. c) Experiments are conducted with the neural network model on two network topologies with three different normalization methods and 30 trials with different initia...