Biochemical Oxygen Demand Characteristic Modelling Based on Span-lateral Inhibition Neural Network Sequential Learning

Based on the analysis of the weight connection of the span-lateral inhibition neural network (S-LINN), a sequence learning approach (SFSL) is proposed on the basis of separate training of the span-output weights and the hidden-layer-feedforward weights during the learning process. By combining the intelligent characteristic biochemical oxygen demand (BOD) modeling of the S-LINN, the method can realize forecasting values online. The new learning strategy not only accelerates the convergence of weights but also improves the performance of the S-LINN. Experiment results show that the proposed S-LINN sequential learning characteristic modeling approach can achieve high-accuracy BOD prediction and that the SFSL improves the approximation and generalization abilities of the S-LINN.