Abstract
This paper introduces a hybrid optimized polynomial neural network (HOPNN), a novel architecture that is constructed by using a combination of fuzzy rule-based models, polynomial neural networks (PNNs) and a hybrid optimization algorithm. The proposed hybrid optimization algorithm is developed by a combination of a space search algorithm and Powell’s method. The structure of this HOPNN comprises of a synergistic usage of fuzzy-relation-based polynomial neurons and polynomial neural network. The fuzzy-relation-based polynomial neurons are fuzzy rule-based models, while the polynomial neural network is an extended group method of data handling (GMDH). The architecture of HOPNN is essentially modified PNNs whose basic nodes of the first (input) layer are fuzzy-rule-based polynomial neurons rather than conventional polynomial neurons. Moreover, the proposed hybrid optimization algorithm is exploited to optimize the structure topology of HOPNN. The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.
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Wang, D., Ji, D., Huang, W. (2012). Hybrid Optimized Polynomial Neural Networks with Polynomial Neurons and Fuzzy Polynomial Neurons. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_10
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DOI: https://doi.org/10.1007/978-3-642-33269-2_10
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