英语论文网

n MWNN is the product of these two. With the proving capability of the ANFIS as a powerful approximation method [20] , that has the both ability of the learning parameter in the neural networks and the localized approximation of the TSK fuzzy model[5], different types of networks based on neuro-fuzzy model has been proposed. In TSK fuzzy model the consequent part of each rule is approximated by a linear function of the inputs. Essentially, neuro-fuzzy models based on TSK model are nonlinear, but conceptually, it is an aggregation of the linear models. If the system under consideration is chaos some forecasting able information in the system may not be predicted well, by the aggregation of these linear models. In nonlinear dynamic systems and time series application, the linear local models in TSK are adequate to predict the behavior of the system, but nonlinear local models in TSK are better to predict the nonlinear dynamic behavior of the system under consideration. For example, Wavelet neuro-fuzzy (WNF) model can be used as a good general approximation [21,22] . In these models, the premise part of each fuzzy rule, represents a localized region of the input space in which a wavelet network is used as local model in the consequent part of fuzzy rules. In the present paper, proposed wavelet neural networks, i.e., SWNN and MWNN are used as a local model in the consequent part of fuzzy rules that leads to the proposition of summation wavelet neuro-fuzzy (SWNF) and multiplication wavelet neuro-fuzzy (MWNF), respectively. By joining the localized region transformation of wavelet activation function with the localized approximation of each fuzzy rule; an increase in precision of models has been experienced.

In both wavelet network and wavelet neuro-fuzzy models three types of non-orthogonal wavelet function, namely Mexican hat, Morlet and Sinc, are used. Ability of proposed model is examined with four examples of time series. The rest of the paper is organized as follow: In Section2 ,a brief discussion of the wavelet function and wavelet transform is presented. Section 3 proposes SWNN and MWNN models. Approximation properties and convergence analysis of the proposed networks also describes in this section. Wavelet neuro-fuzzy (WNF) model are proposed in Section 4 . This section also dealt with the convergence analysis of SWNF and MWNF. Experimental results are revealed in Section5 and, finally conclusions are relegated to Section 6 .

2. Wavelet function
The wavelet transform (WT) in its continuous form provides a flexible time-frequency window, which narrows when observing high frequency phenomena and widens when analyzing low frequency behavior. Thus, time resolution becomes arbitrarily good at high frequencies, while the frequency resolution becomes arbitrarily good at low frequen-cies. This kind of analysis is suitable for signals composed of high frequency components with short duration and low frequency components with long duration, which is often the case in practical situations. Here, a brief review from the theory of wavelets is described that gives basic idea about the wavelets and the related work.

References
[1] A. Hossen, Power spectral density estimation via wavelet decomposition, Electron. Lett. 40 (17) (2004) 1055–1056.
[2] S. Krishnamachari, R. Chellappa, GMRE models and wavelet decom-position for texture segmentation, Int. Conf. Image Process. 3 (6) (1本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。