Multiple-prior-knowledge neural network for industrial processes

Weighing fusion method for truck scale based on an optimal neural network with derivative constraints and a lagrange multiplier

“…Ferrari [16] and Gianluca [17], used the prior knowledge to establish the constraints of the RBFNN's structure, which improved the RBFNN's generalization ability. Lou et al [18], discussed a popular and effective method that embeds system's prior knowledge into neural networks, the prior knowledge is including invariance, monotonicity, homogeneity, concavity, etc. Lin et al [19] proposed a weighing fusion method for truck scale based on prior knowledge (e.g., the ideal weighing model of a truck scale) and neural network ensembles.…”

“…Fortunately, the prior knowledge is very useful for optimizing NNs [13][14][15][16][17][18][19]. Yajun et al [14] used some obvious prior knowledge, such as the symmetry, the ranking list, the boundary and the monotonicity, to propose a constrained neural network regression model for improving the conventional NN's generalization ability.…”