COMPARING EVOLUTIONARY ALGORITHMS ON TUNING THE PARAMETERS OF FUZZY WAVELET NEURAL NETWORK
Document Type : Research Paper
10.22099/ijste.2013.1888
Abstract
In recent years Fuzzy Wavelet Neural Networks (FWNNs) have been used in many areas. Function approximation is an important application of FWNNs. One of the main problems in effective usage of FWNN is tuning of its parameters. In this paper several different evolutionary algorithms including Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Evolutionary Strategy (ES), Fast Evolutionary Strategy (FES) and variants of Differential Evolutionary algorithms (DE) are used for adjusting these parameters on five test functions. The obtained results are compared based on some measures by using multiple non-parametric statistical tests. The comparison reveals the superiority of some variants of DE in terms of convergence behavior and the ability of function approximation.
(2013). COMPARING EVOLUTIONARY ALGORITHMS ON TUNING THE PARAMETERS OF FUZZY WAVELET NEURAL NETWORK. Iranian Journal of Science and Technology Transactions of Electrical Engineering, 37(2), 193-198. doi: 10.22099/ijste.2013.1888
MLA
. "COMPARING EVOLUTIONARY ALGORITHMS ON TUNING THE PARAMETERS OF FUZZY WAVELET NEURAL NETWORK", Iranian Journal of Science and Technology Transactions of Electrical Engineering, 37, 2, 2013, 193-198. doi: 10.22099/ijste.2013.1888
HARVARD
(2013). 'COMPARING EVOLUTIONARY ALGORITHMS ON TUNING THE PARAMETERS OF FUZZY WAVELET NEURAL NETWORK', Iranian Journal of Science and Technology Transactions of Electrical Engineering, 37(2), pp. 193-198. doi: 10.22099/ijste.2013.1888
VANCOUVER
COMPARING EVOLUTIONARY ALGORITHMS ON TUNING THE PARAMETERS OF FUZZY WAVELET NEURAL NETWORK. Iranian Journal of Science and Technology Transactions of Electrical Engineering, 2013; 37(2): 193-198. doi: 10.22099/ijste.2013.1888
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