For the highly complex systems, it is difficult to build a robust global model by neural networks, and efficiently managing the large amounts of experimental data is often required in real-time applications.

In this paper, an effective method for building local models is proposed to enhance robustness and learning speed in globally supervised neural networks.

Furthermore, each local neural network is learned in the same manner as a Gaussian process (GP), because GP produces prediction that captures the uncertainty inherent in actual systems, and typically provides superior results.

A mixture of local model is created and then augmented using weighted regression.

This method, referred to as LGPN, is utilized for approximating the complex terramechanics models under fixed soil parameters.

The prediction results show that the proposed method yields significant robustness, modeling accuracy, and rapid learning speed.