A new time-invariant fuzzy time series forecasting method based on genetic algorithm

In recent years, many fuzzy time series methods have been proposed in the literature. Some of these methods use the classical fuzzy set theory, which needs complex matricial operations in fuzzy time series methods. Because of this problem, many studies in the literature use fuzzy group relationship tables. Since the fuzzy relationship tables use order of fuzzy sets, the membership functions of fuzzy sets have not been taken into consideration. In this study, a new method that employs membership functions of fuzzy sets is proposed. The new method determines elements of fuzzy relation matrix based on genetic algorithms. The proposed method uses first-order fuzzy time series forecasting model, and it is applied to the several data sets. As a result of implementation, it is obtained that the proposed method outperforms some methods in the literature

A new model selection strategy in artificial neural networks

In recent years, artificial neural networks have been used for time series forecasting. Determining architecture of artificial neural networks is very important problem in the applications. In this study, the problem in which time series are forecasted by feed forward neural networks is examined. Various model selection criteria have been used for the determining architecture. In addition, a new model selection strategy based on well-known model selection criteria is proposed. Proposed strategy is applied to real and simulated time series. Moreover, a new direction accuracy criterion called modified direction accuracy criterion is discussed. The new model selection strategy is more reliable than known model selection criteria

Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks

In a clustering problem, it would be better to use fuzzy clustering if there was an uncertainty in determining clusters or memberships of some units. Determining the number of cluster has an important role on obtaining sensible and sound results in clustering analysis. In many clustering algorithm, it is firstly need to know number of cluster. However, there is no pre-information about the number of cluster in general. The process of determining the most proper number of cluster is called as cluster validation. In the available fuzzy clustering literature, the most proper number of cluster is determined by utilizing cluster validation indices. When the data contain complexity are being analyzed, cluster validation indices can produce conflictive results. Also, there is no criterion point out the best index. In this study, artificial neural networks are employed to determine the number of cluster. The data is taken as input so the output is membership degree. The proposed method is applied some data and obtained results are compared to those obtained from validation indices like PC, XB, and CE. It is shown that the proposed method produce accurate results