Traditional forecasting methods need strict assumptions such as normality and linearity. It is very difficult to satisfy these assumptions for real-world time series. Many realworld time series can be easily analyzed by using fuzzy time series methods since fuzzy time series methods do not require any strict assumptions. Therefore, fuzzy time series approaches have been getting more and more at...

This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. The system learns autonomously without supervision or a priori training data. Two novel techniques are proposed. The first technique combines Q(λ)-learning with function approximation (fuzzy inference system) to tune the parameters of a fuzzy logic controller operating in continuous state...

Caixia Li , Sreenatha Gopalarao Anavatti, Tapabrata Ray University of New South Wales @ Australian Defence Force Academy [email protected] Abstract-This paper focuses on the design and implementation of the analytical hierarchy process (AHP) using Fuzzy inference techniques. This AHP-FUZZY approach is a multi-criteria combination system. The nature of the AHP-FUZZY approach is pair-...

Image processing is characterized by a procedure of information processing for which both the input and output are images, such as photographs or frames of video [Jain’86]. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it.[webpage1] Fuzzy Image Processing (FIP) is a collection of different fuzzy appr...

Evolving fuzzy systems are data-driven fuzzy (rule-based) systems supporting an incremental mode of model adaptation in dynamically changing environments; typically, such models are learned on a continuous stream of data in an online manner. This paper advocates the use of visualization techniques in order to help a user gain insight into the process of model evolution. More specifically, dynam...

We are developing a prototype intelligent intrusion detection system (IIDS) to demonstrate the effectiveness of data mining techniques that utilize fuzzy logic and genetic algorithms. This system combines both anomaly based intrusion detection using fuzzy data mining techniques and misuse detection using traditional rule-based expert system techniques. The anomaly-based components are developed...

Fuzzy logic techniques were originally designed to translate expert knowledge – which is often formulated by using imprecise (“fuzzy”) from natural language (like “small”) – into precise computer-understandable models and control strategies. Such a translation is still the main use of fuzzy techniques. Lately, it turned out that fuzzy methods can help in another class of applied problems: namel...

Intelligent techniques are versatile in signal classification and estimation. Prominent techniques like fuzzy logic, artificial neural networks, genetic algorithms plays a significant role in signal classification and are helpful in developing various continuous control schemes. This chapter presents the effectiveness of fuzzy logic concept in classifying the vibration signals based on their fr...

This paper describes the various fuzzy rule based techniques for image segmentation. Fuzzy rule based segmentation techniques can incorporate the domain expert knowledge and manipulate numerical as well as linguistic data. They are also capable of drawing partial inference using fuzzy IF-THEN rules. For these reasons they have been intensively applied in medical imaging. But these rules are app...

This paper presents a survey on the ex isting methods for the stability analysis of fuzzy control systems including both conventional Mamdani s rule based fuzzy controllers and Takagi Sugeno controllers The overview considers several approaches including input output stability frequency response methods Lyapunov techniques and qualitative methods The paper also sumarizes the new stability techn...