In this paper the theory of fuzzy logic is mixed with the theory of Markov systems and the abstraction of a Markov system with fuzzy states introduced. The notions such as fuzzy transient, fuzzy recurrent etc., were introduced. The results based on these notions are introduced.

A genetic algorithm (GA) based recurrent fuzzy neural network modeling method for dynamic nonlinear chemical process is presented. The dynamic recurrent fuzzy neural network (RFNN) is constructed in terms of Takagi-Sugeno fuzzy model. The consequent part is comprised of the dynamic neurons with output feedback. The number and the parameters of membership functions in the premise part are optimi...

In this paper, a Pseudo-Gaussian-based Recurrent Compensatory Fuzzy Neural Network (PG-RCFNN) is proposed for identification of dynamic systems. The recurrent network is embedded in the PG-RCFNN by adding feedback connections in the second layer, where the feedback units act as memory elements. The compensatorybased fuzzy reasoning method is using adaptive fuzzy operations of fuzzy neural netwo...

Accurate prediction of solar activity as one aspect of space weather phenomena is essential to decrease the damage from these activities on the ground based communication, power grids, etc. Recently, the connectionist models of the brain such as neural networks and neuro-fuzzy methods have been proposed to forecast space weather phenomena; however, they have not been able to predict solar activ...

Abstract— A kind of recurrent fuzzy neural network (RFNN) is constructed by using recurrent neural network (RNN) to realize fuzzy inference. In this kind of RFNN, temporal relations are embedded in the network by adding feedback connections on the first layer of the network. And a RFNN based adaptive control (RFNNBAC) is proposed, in which, two RFNN are used to identify and control plant respec...

Neurofuzzy systems—the combination of artificial neural networks with fuzzy logic—have become useful in many application domains. However, conventional neurofuzzy models usually need enhanced representational power for applications that require context and state (e.g., speech, time series prediction, control). Some of these applications can be readily modeled as finite state automata. Previousl...

A novel self-learning control algorithm for human-machine systems is presented. The designed controller is based on a probabilistic extension of recurrent fuzzy systems, which allows the consideration of non-deterministic information in addition to deterministic control signals. The behavior of the controller is adapted by varying the conditional probabilities of state switching, wherefore the ...

This paper reports on a novel recurrent fuzzy classification method for robust detection of context activities in an environment using either single or distributed sensors. It also introduces a classification of system architectures for uncertainty calculation in general. Our proposed novel method utilizes uncertainty measures for improvement of detection, fusion and aggregation of context know...