Biomass concentration prediction via an input-weighed model based on artificial neural network and peer-learning cuckoo search

Biomass concentration (BC) is considered as one of the most important biochemical parameters. Its reliable on-line estimation is crucial in the real-time status monitoring and quality control of fermentation processes. Considering that each input variable may have different influence on BC in actual fermentation processes, a novel input-weighted empirical model based on the radial basis function neural network (RBFN) and a new peer-learning cuckoo search (PLCS) algorithm, is proposed in this paper to predict BC. The determination of input variable weights and RBFN parameters for the proposed BC prediction model is framed as one and the same optimization problem. Inspired by a common social phenomenon that the mutual learning between team members (peers) would be extremely helpful for their team to accomplish a work efficiently, a PLCS algorithm is proposed to solve the resulting optimization (RO) problem, and thereby accomplish the development of the proposed BC prediction model. The effectiveness and superiority of this new prediction model is validated using the production data from a lab-scale nosiheptide fermentation process. Moreover, the performance of PLCS is also demonstrated on the RO problem with these data and some benchmark functions.