Optimization of self-organizing polynomial neural networks

The main disadvantage of self-organizing polynomial neural networks (SOPNN) automatically structured and trained by the group method of data handling (GMDH) algorithm is a partial optimization of model weights as the GMDH algorithm optimizes only the weights of the topmost (output) node. In order to estimate to what extent the approximation accuracy of the obtained model can be improved the particle swarm optimization (PSO) has been used for the optimization of weights of all node-polynomials. Since the PSO is generally computationally expensive and time consuming a more efficient Levenberg–Marquardt (LM) algorithm is adapted for the optimization of the SOPNN. After it has been optimized by the LM algorithm the SOPNN outperformed the corresponding models based on artificial neural networks (ANN) and support vector method (SVM). The research is based on the meta-modeling of the thermodynamic effects in fluid flow measurements with time-constraints. The outstanding characteristics of the optimized SOPNN models are also demonstrated in learning the recurrence relations of multiple superimposed oscillations (MSO).

► Optimization of self-organizing polynomial neural networks (SOPNN) by Levenberg–Marquardt algorithm is proposed.
► We demonstrate extraordinary approximation and prediction characteristics of optimized SOPNN.
► We demonstrate modeling the complex calculation procedure by SOPNN surrogate applicable to real-time flow rate measurements.
► We show superior SOPNN characteristics in modeling multiple superimposed oscillations.