Fuzzy logic, neural network, fuzzy-neural networks play an important role in the linguistic modeling of intelligent control and decision making in complex systems. The Fuzzy-Neural Network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes an Artificial Immune Algorithm (AIA) based optimal learning fuzzy-neural network (IM-FNN). T...

In this paper, an adaptive neuro-fuzzy system, called HyFIS, is proposed to build and optimise fuzzy models. The proposed model introduces the learning power of neural networks into the fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training eramples by a ...

In This paper, the authors propose a Sensorless Direct Torque and Flux Control (DTFC) of Induction Motor (IM) using two approach intelligent techniques: Mamdani Fuzzy Logic (FL) controller is used for controlling the rotor speed and Artificial Neural Network (ANN) applied in switching select stator voltage. We estimated the rotor speed by using the Model Reference Adaptive Systems (MRAS). The c...

Management flight simulators provide a “microworld” in which users control an organization in a realistic environment without the need to disturb the real system. This paper presents a web-based intelligent virtual learning environment to enhance the education of engineering management students in quality management and statistical process control techniques. The paper introduces the structure ...

In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons cannot only adaptively adjust fuz...

Intelligent systems may be viewed as a framework for solving the problems of nonlinear system control. The intelligence of the system in the nonlinear or changing environment is used to recognize in which environment the system currently resides and to service it appropriately. This paper presents a general methodology of adaptive control based on multiple models in fuzzy form to deal with plan...

The intelligent control technology with the traditional control theory formats intelligent compound controller in order to learn from each other and improve the overall advantage of complementary features. Fuzzy PID control neuron PID control, fuzzy sliding mode control, and optimal control of the neural network. This paper discusses the current situation and development of smart composite cont...