Читать книгу Artificial Intelligence and Quantum Computing for Advanced Wireless Networks - Savo G. Glisic - Страница 63

This section focuses on the accuracy and interpretability trade‐off in fuzzy model‐based solutions. A fuzzy model based on an experience‐oriented learning algorithm is presented that is designed to balance the trade‐off between both of the above aspects. It combines support vector regression (SVR) to generate the initial fuzzy model and the available experience on the training data and standard fuzzy model solution.

Fuzzy systems have been used for modeling or control in a number of applications. They are able to incorporate human knowledge, so that the information mostly provided for many real‐world systems could be discovered or described by fuzzy statements. Fuzzy modeling (FM) considers model structures in the form of fuzzy rule‐based systems and constructs them by means of different parametric system identification techniques. In recent years, the interest in data‐driven approaches to FM has increased. On the basis of a limited training data set, fuzzy systems can be effectively modeled by means of some learning mechanisms, and the fuzzy model after learning tries to infer the true information. In order to assess the quality of the obtained fuzzy models, there are two contradictory requirements: (i) interpretability, the capability to express the behavior of the real system in a way that humans can understand, and (ii) accuracy, the capability to faithfully represent the real system. In general, the search for the desired trade‐off is usually performed from two different perspectives, mainly using certain mechanisms to improve the interpretability of initially accurate fuzzy models, or to improve the accuracy of good interpretable fuzzy models. In general, improving interpretability means reducing the accuracy of initially accurate fuzzy models.

It is well known that support vector machine (SVM) has been shown to have the ability of generalizing well to unseen data, and giving a good balance between approximation and generalization. Thus, some researchers have been inspired to combine SVM with FM in order to take advantage of both of approaches: human interpretability and good performance. Therefore, support vector learning for FM has evolved into an active area of research. Before we discuss this hybrid algorithm‚ we discuss separately the basics of the FM and SVR approach.