This paper presents a new approach to the problem of call admission control (CAC) of variable bit rate (VBR) trac in an asynchronous transfer mode (ATM) network. Our approach employs an integrated neural network and fuzzy controller to implement the CAC controller. This scheme capitalizes on the learning ability of a neural network and the robustness of a fuzzy controller. Experiments show tha...

This paper proposes a self-structuring fuzzy neural network (SFNN) using asymmetric Gaussian membership functions in the structure and parameter learning phases. An adaptive self-structuring asymmetric fuzzy neural-network control (ASAFNC) system which consists of an SFNN controller and a robust controller is proposed. The SFNN controller uses an SFNN with structure and parameter learning phase...

To control structures against wind and earthquake excitations, Adaptive Neuro Fuzzy Inference Systems and Neural Networks are combined in this study. The control scheme consists of an ANFIS inverse model of the structure to assess the control force. Considering existing ANFIS controllers, which require a second controller to generate training data, the authors’ approach does not need anot...

In recent years, there has been an expansive growth in the study and implementation of neural networks over a spectrum of research domains. Neural network based Predictive control is recognized as an efficient methodology to address difficult control problems. The NARMA model is an exact representation of the input-output behaviour of finite dimensional non-linear discrete time dynamical system...

Model Predictive Control (MPC) refers to a class of algorithms that compute a sequence of manipulated variable adjustments in order to optimize the future behaviour of a plant. MPC technology can now be found in a wide variety of application areas. The neural network predictive controller that is discussed in this paper uses a neural network model of a nonlinear plant to predict future plant pe...

This paper present the implementation and comparative study of dynamic behaviour of three phase induction motor with different control strategies such as PID controller, Fuzzy Logic and Neural Network techniques. The system consist of three phase variable frequency drive with gating signal are generated using PIC microcontroller. The Simulink model of PID controller, fuzzy Logic and Neural Netw...

In this paper, adaptive neural network control of robot manipulators in the task space is considered. The controller is developed based on a neural network modeling technique which neither requires the evaluation of inverse dynamical model nor the time-consuming training process. It is shown that, if Gaussian radial basis function networks are used, uniformly stable adaptation is assured, and a...

This paper develops a design methodology of sliding mode ANFIS-Based multi-inputs multi-outputs (MIMO) fuzzy neural network (AMFNN) control for robotic systems. This control system consists of a sliding mode (SM) controller and an AMFNN controller. The SM controller is used to deal with uncertain parts of system dynamics and external disturbances and the AMFNN controller is served as a controll...

A guidance law, based on supervisory recurrent fuzzy neural network control (SRFNNC), is proposed for the autonomous underwater vehicle (AUV) guidance systems. This SRFNNC system is comprised of a recurrent fuzzy neural network (RFNN) controller and a supervisory controller. The RFNN controller is used to mimic an ideal controller and the supervisory controller is designed to compensate for the...