Abstract. Artificial neural networks are powerful tools to learn functional relationships between data. They are widely used in engineering applications. Recurrent neural networks for fuzzy data have been introduced to map uncertain structural processes with deterministic or uncertain network parameters. Based on swarm intelligence, a new training strategy for neural networks is presented in th...

The processing of sensory information is fundamental to the basic operation of the nervous system. Our nervous system uses this sensory information to gain knowledge of our bodies and the world around us. This knowledge is of great importance as it provides the coherent and accurate information necessary for successful motor control. Yet, all this knowledge is of an uncertain nature because we ...

Uncertainty may be taken to characterize in­ ferences, their conclusions, their premises or all three. Under some treatments of uncer­ tainty, the inference itself is never character­ ized by uncertainty. We explore both the sig­ nificance of uncertainty in the premises and in the conclusion of an argument that in­ volves uncertainty. We argue that for un­ certainty to characterize the conclusi...

This paper proposes a novel method for identification of a class of the uncertain active suspension systems by using on-line adaptive T-S fuzzy-neural controller. In reality, vehicles may encounter unpredictable road conditions, e.g., rocks and potholes influencing the dynamic behavior of active suspension systems. These road conditions are not only cause parts of the active suspension system t...

We develop an adaptive control architecture to achieve stabilization and command following of uncertain dynamical systems with improved transient performance. Our framework consists of a new reference system and an adaptive controller. The proposed reference system captures a desired closed-loop dynamical system behavior modified by a mismatch term representing the high-frequency content betwee...

Verification of probabilistic systems is usually based on variants of Markov processes. For systems with continuous dynamics, Markov processes are generated using discrete approximation methods. These methods assume an exact model of the dynamic behavior. However, realistic systems operate in the presence of uncertainty and variability and they are described by uncertain models. In this paper, ...

Uncertain logic is a branch of multi-valued logic for dealing with subjective information, which explains formulas as uncertain variables and defines their truth values as uncertain measures. In this paper, uncertain satisfiability is proposed to verify the consistency of a set of truth values for the formulas. Furthermore, for a set of satisfiable formulas with given truth values, the problem ...

Developing reliable context-aware applications remains a big challenge, even after a decade of research in this area. Usually a lot of code is required to handle an application’s correct behavior in a variety of different situations. Along with a growing amount of code, also increases the risk of programming errors that may lead to an undesired behavior in particular situations. In this paper w...

Autonomic personal computing is personal computing on autonomic computing platforms. Its goals combine those of personal computing with those of autonomic computing. The challenge of personal autonomic computing is to simplify and enhance the end-user experience, delighting the user by anticipating his or her needs in the face of a complex, dynamic, and uncertain environment. In this paper we i...