Multimodal Control by Variable-Structure Neural Network Modeling for Coagulant Dosing in Water Purification Process

Abstract

Stochastic neural network has the characteristics of good global convergence and fast gradient-based learning ability. It can be applied to multidimensional nonlinear systems, but its generalization ability is poor. In this paper, combined with rule base, through the PCA method, an improved multimodal variable-structure random-vector neural network algorithm (MM-P-VSRVNN) is proposed for coagulant dosing, which is a key production process in water purification process. Ensuring for qualified water, how to control coagulation dosage effectively, obtain valid production cost, and increase more profits is a focus in the water treatment plan. Different with the normal neural network mode, PCA is used to optimize hidden-layer nodes and update the neural network structure at every computation. This method rectifies coagulant dosage effectively while keeping valid coagulation performance. By the way, the MM-P-VSRVNN algorithm can decrease computation time and avoid overfitting learning ability. Finally, the method is proved feasible through the experiment and analyzed by the simulation result.