跨越—侧抑制神经网络序列学习BOD特征建模

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

通过对跨越—侧抑制神经网络(S-LINN)的权值连接分析,设计一种跨越输出权值——隐含层前馈连接权值分别训练的序列学习(SFSL)方法,并结合S-LINN的BOD(biochemical oxygen demand)特征建模方法,实现其在线预测.权值序列学习方法能够实现网络权值的快速收敛,进一步提高网络的学习性能.实验研究表明,S-LINN的特征建模能够实现BOD的准确预测,并且SFSL对网络的学习精度和泛化能力均有明显提升.

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 character- istic biochemical oxygen demand （BOD） modeling of the S-LINN, the method can real_ize 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 mad that the SFSL improves the approximation and generalization abilities of the S-LINN.