Optimization of self-organizing polynomial neural networks (CROSBI ID 192096)
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Podaci o odgovornosti
Marić, Ivan
engleski
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).
polynomial neural networks; GMDH; Levenberg–Marquardt algorithm; Particle swarm optimization; Time series modeling
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Podaci o izdanju
40 (11)
2013.
4528-4538
objavljeno
0957-4174
10.1016/j.eswa.2013.01.060
Povezanost rada
Elektrotehnika, Računarstvo