Soft Sensing Modeling Based on Extreme Learning Machine for Biochemical Processes

An improved extreme learning machine (ELM) method was proposed for soft sensing modeling, and the disadvantage of profuse hidden nodes in ELM network was overcome. By adding a kind of sorting neurons into the hidden layer of the single-hidden-layer feed forward neural network (SLFN), a new SLFN structure was proposed. A method was proposed to solve the problem of the number difference of the sample sorts, and then the same hidden neural nodes could be used to fit the different kind of samples. By this way, the number of hidden nodes was decreased, the model structure was simplified, and the on-line computation speed was increased markedly. The proposed method provided a new approach to build soft sensing models, and it was successfully applied to the soft sensing modeling the of thalli concentration for the Nosiheptied fermentation process to realize the on-line prediction of the thalli concentration.