In this paper, an artificial intelligent tool is proposed using fuzzy logic (FL) and recurrent neural networks (RNN) for definition and forecast of patient’s clinical condition. The fuzzy logic-based proposed first phase of the tool permits the analysis of the current state of the patient, which allows the training of the artificial neural network. In the second phase, two Elman networks Multi ...

This paper proposes the development of a fuzzy predictive control. Genetic algorithms (GA’s) are used to automatically tune the controller. A recurrent neural network is used to identify the process, and then provides predictions about the process behavior, based on control actions applied to the system. These predictions are used by the fuzzy controller, in order to accomplish a better control...

Any action taken on a process, for example in response to an abnormal situation or in reaction to unsafe conditions, relies on the ability to identify the state of operation or the events that are occurring. Although there might be hundreds or even thousands of measurements in a process, there are generally few events occurring. The data from these measurements must then be mapped into appropri...

This paper is concerned with the H∞ fuzzy control problem for a class of systems with repeated scalar nonlinearities and random packet losses. A modified Takagi–Sugeno (T–S) fuzzy model is proposed in which the consequent parts are composed of a set of discrete-time state equations containing a repeated scalar nonlinearity. Such a model can describe some well-known nonlinear systems such as rec...

In this paper a network comprising alternative branching nodes with probabilistic outcomes is considered. In other words, network nodes are probabilistic with exclusive-or receiver and exclusive-or emitter. First, an analytical approach is proposed to simplify the structure of network. Then, it is assumed that the duration of activities is positive trapezoidal fuzzy number (TFN). This paper com...

modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. thisstudy made us of adaptive neuro-fuzzy ...

We present a Monte Carlo approach for training partially observable diffusion processes. We apply the approach to diffusion networks, a stochastic version of continuous recurrent neural networks. The approach is aimed at learning probability distributions of continuous paths, not just expected values. Interestingly, the relevant activation statistics used by the learning rule presented here are...