Neuro-fuzzy computing, which provides efficient information processing capability by devising methodologies and algorithms for modeling uncertainty and imprecise information, forms at this juncture, a key component of soft computing. An integrated neuro-fuzzy system is simply a fuzzy inference system trained by a neural networklearning algorithm. The learning mechanism fine-tunes the underlying...

a mobile manipulator robot is known as a complex system due to some properties such as coupling between the manipulator and mobile chassis, holonomic and nonholonomic constraints, multivariable and nonlinear dynamics. the control of robot faces the external disturbance, parametric uncertainty and unmodeled dynamics. therefore, the use of an adaptive fuzzy system is suggested for its capability ...

Fuzzy control systems have had various applications in a wide range of science and engineering in recent years. Since an unstable control system is typically useless and potentially dangerous, stability is the most important requirement for any control system (including fuzzy control system). Conceptually, there are two types of stability for control systems: Lyapunov stability (a special case ...

A hybrid system is a system containing a mixture of discrete event components and continuous variable components. The existing hybrid system modeling methods are effective to handle crisp cases but can be difficult to represent deterministic uncertainties and subjectivity inherited in many real-world applications. We generalize the crisp hybrid system framework to a fuzzy hybrid system framewor...

The probabilistic fuzzy set (PFS) and the related probabilistic fuzzy logic system (PFLS) is designed for handling the uncertainties in both stochastic and nonstochastic nature. In this paper, a bell-shaped probabilistic fuzzy set is proposed and the related PFLS is constructed and applied to a modeling problem to study stochastic modeling capability. It clearly discloses that the bell-shaped P...

Exploring the growing interest in extending the theory of probability and statistics to allow for more flexible modeling of uncertainty, ignorance, and fuzziness, the properties of fuzzy modeling are investigated for statistical signals, which benefit from the properties of fuzzy modeling. There is relatively research in the area, making explicit identification of statistical/stochastic fuzzy m...