جستجوی مقالات مرتبط با کلیدواژه "خوشه بندی" در نشریات گروه "ریاضی"

This research presents a new method of coarse-tuning the fuzzy reinforcement learning architecture using agent-environment interactive data. This method solves two main challenges the fuzzy reinforcement learning: the low-speed training process and determining the input membership functions of the fuzzy structure. First, the agent interacts with the environment to gather training data. Then, the transition probability matrix and the expected return matrix are calculated by applying a clustering algorithm due to the continuous space. Each cluster is a state of the environment, and an approximation of the transition probability from one cluster to another is calculated using the gathered data. Finally, the parameters of the fuzzy system are adjusted using the modified value iteration method of dynamic programming for the continuous space. The proposed method is fully described with an example. This method increases the learning speed and adjusts the input membership functions of the fuzzy system.

In this paper, a new method for identifying outlier time series based on GARCH model by exponential distance approach is presented in three steps: first fuzzy and hard clustering methods are implemented on time series, then the outlier time series are detected and removed from the dataset. After removeing outlying time series, clustering algorithms are applied for dataset again. The 30 stocks of the top active, lucrative and profitable stocks in the Iranian stock market are used to evaluate the presented methods. By computing the Silhouette and Xe-Beni indexes, the accuracy of the clustering methods are compared and finally, it is shown that by removong the outlier time series, the GARCH model based on the exponential distance approach has the best performans.

Given that the current era is the era of information explosion, so the clustering of existing data and information is inevitable that must be done. Since in many cases we encounter widespread uncertainties in the available data, the best way to use clustering techniques is to combine them with fuzzy mathematics. Researchers have developed a variety of clustering algorithms for data clustering, some of which have fuzzy versions. The basic idea in fuzzy clustering is to assume that each cluster is a set of elements, then by changing the definition of element membership in this set from a state where an element can only be a member of a cluster, to a state where each element can To rank different memberships within several clusters, provide more realistic categories. The fuzzy mean c clustering algorithm (FCM) is the most common fuzzy clustering method. Various forms of FCM have been proposed so far. In this paper, based on FCM algorithm and similarity criterion, an efficient and robust clustering method for fuzzy data is proposed. This is the method. The similarity criterion proposed in this paper is based on α-sections and gives the decision maker the ability to make decisions at different levels. At the end, two numerical examples and a practical example are presented to show the efficiency of the proposed method.