Modeling and development of control systems to deal with the congestion at intersection in urban traffic is a critical research issue. Several approaches have been used to develop the modeling and controlling phenomenon in the said problem. These approaches include, Petri net, Fuzzy Logic, Neural Network, Genetic Algorithms, Activity Theory, Multi Agent Systems and many more. This paper is a su...

This paper describes a self-organizing artificial neural network, based on Kohonen’s model of self-organization, which is capable of handling fuzzy input and of providing fuzzy classification. Unlike conventional neural net models, this algorithm incorporates fuzzy set-theoretic concepts at various stages. The input vector consists of membership values for linguistic properties along with some ...

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to monitor the patient behavior, data are collected from patient’s body by a medical monitoring device so as to calculate the output using embedded software. incorrect calculations may endanger the patient’s life if the software fails to meet the patient’s requirements. accordingly, the veracity of the software behavior is a matter of concern in the medicine; moreover, the data collected from t...

This ability in fuzzy UML, practically leaves the customers and market’s need without response in this important and vital area. Here, the available sequence diagrams in fuzzy UML will map into fuzzy Petri net. However, the formal models ability will be added to the Semi-formal fuzzy UML. This formalization will add the automatic processing ability to the Semi-formal fuzzy UML. Further more, t...

This paper presents experimental results of an original approach to the Neural Network learning architecture for the control and the adaptive control of mobile robots. The basic idea is to use non-recurrent multi-layer-network and the backpropagation algorithm without desired outputs, but with a quadratic criterion which spezify the control objective. To illustrate this method, we consider an e...

Introducing neuro-fuzzy concept in decision-making problems, makes a new way artificial intelligence and expert systems. Sometimes, neural networks are used to optimize certain performances. In general, knowledge acquisition becomes difficult when problem’s variables, constraints, environment, decision maker’s attitude complex behavior encountered with. A sense of fuzziness prevails these situa...

In this paper, an intelligent adaptive control system (IACS) for induction motor (IM) servo drive to achieve high dynamic performance is proposed. The proposed IACS comprises a recurrent functional-linkbased Petri fuzzy-neural-network (RFLPFNN) controller and a robust controller so that the developed adaptive control scheme has more robustness against parameters uncertainties and approximation ...