This study investigates whether people represent the beginnings and ends of events as fuzzy temporal frames and subsequently construct event temporal relations. The study adopted Allen’s (1984; 1991) seven logical categories of temporal relations. Constructing the seven relations often requires the accurate encoding and (or) retrieval of the beginnings and ends of events. We used a recurrent ne...

In this paper, the impulsive fuzzy recurrent neural network with both time-varying delays and distributed delays is considered. Applying the idea of vector Lyapunov function, M-matrix theory and analytic methods, several sufficient conditions are obtained to ensure the existence, uniqueness and global exponential stability of equilibrium point for the addressed neural network. Moreover, the est...

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...

A two-layer Recurrent Neural Network Model (RNNM) and an improved Backpropagation-through-time method of its learning are described. For a complex nonlinear plants identification, a fuzzy-neural multi-model, is proposed. The proposed fuzzy-neural model, containing two RNNMs is applied for real-time identification of nonlinear mechanical system. The simulation and experimental results confirm th...

Recently, there is great deal of interest in the use of fuzzy expert systems in control applications. Controllers based on fuzzy logic belong to the class of static or memoryless nonlinear controllers and provide better control than is possible using linear control. The major strength of fuzzy controllers lies in the way a nonlinear output mapping of a number of inputs can be specified easily u...

We present a new model of a Max–Min recurrent neural network that is able to identify fuzzy dynamic systems from a set of examples. Once the neural network is trained, the fuzzy relation that describes the system is encoded in its weights. c © 2001 Elsevier Science B.V. All rights reserved.

The solution of the prediction problem is presented for the finite possibilistic modelling [3,4,6,7]. A recurrent variant of finite possibilistic models is considered. In this variant, we define the regularization condition for constructing a quasi-optimal estimator of fuzzy transition operator (FTO). We construct the discrete recurrent extremal fuzzy process with possibilistic uncertainty, the...

The paper proposed to apply a hierarchical fuzzy-neural multi-model and Takagi–Sugeno (T–S) rules with recurrent neural procedural consequent part for systems identification, states estimation and adaptive control of complex nonlinear plants. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive trajectory tracking control syste...

Fuzzy time series techniques are more suitable than traditional time series techniques in forecasting problems with linguistic values. Two shortcomings of existing fuzzy time series forecasting techniques are they lack persuasiveness in dealing with recurrent number of fuzzy relationships and assigning weights to elements of fuzzy rules in the defuzzification process. In this paper, a novel fuz...