Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study

This research provides a study on how the weights of artificial neural networks (ANNs) can be automatically updated by applying bio-inspired algorithms, particularly using the particle swarm optimisation (PSO) algorithm, grasshopper optimisation algorithm (GOA) and grey wolf optimisation (GWO). These evolutionary computation algorithms were used to evolve the synaptic weights of ANNs to find a particular architecture of ANNs. The developed nonlinear models were targeted to the identification of a particular nonlinear prediction system, an industrial winding process, as a case study. These new models were referred, respectively, to as ANN-PSO, ANN-GOA and ANN-GWO. The proposed models were compared with other linear and nonlinear conventional models including least square error and multiple nonlinear regression methods, respectively, as well as other state-of-the-art models including multilayer perceptron-type NNs, radial basis function and recurrent local linear neuro-fuzzy. The performance of the developed models was assessed using several metric criteria. Comparison of the proposed ANN-PSO, ANN-GOA and ANN-GWO models with other traditional and state-of-the-art models asserts the efficacy of the proposed modelling approaches.

Automated summary

This research focuses on updating the weights of artificial neural networks using bio-inspired algorithms such as PSO, GOA, and GWO, for identifying specific architectures in nonlinear prediction systems. The developed models were compared with traditional and state-of-the-art models, showing the efficacy of the proposed approaches in modeling.