Selami Beyhan

Learn More
— In this paper, we compare indirect adaptive fuzzy control and sliding-mode control in a robot manipulator application. The manipulator performs pick-and-place tasks with unknown and variable payloads. The change of payload causes large variations in the dynamics of the robot. The sliding-mode controller deals with the payload change through its inherent(More)
This paper presents a new orthogonal neural network (ONN) which is utilized successively for online identification and control of nonlinear discrete-time systems. The proposed network is designed with auto regressive with exogenous (ARX) terms of inputs and outputs, and their orthogonal terms by Chebyshev polynomials. The network is a single layer neural(More)
In this paper, the well-known heuristic Artificial Bee Colony algorithm (ABC) and deterministic Levenberg-Marquardt (LM) optimization method are unified to get better performance of nonlinear optimization. In the proposed cascaded ABC-LM algorithm, the power of the ABC and LM algorithms are synergized to reduce computational-time and get rid of the problem(More)
  • S. Beyhan
  • 2012
In this paper, an adaptive dynamic neural-network observer is designed for unknown or uncertain nonlinear systems and utilized to estimate unmeasurable states. The contributions of paper are in twofold. First, using variable learning rate and internally stable neurons, convergence of parameters is guaranteed and overall stable adaptive observer is designed.(More)
In this paper, a novel radial basis function (RBF) neural network is proposed and applied successively for online stable identification and control of nonlinear discrete-time systems. The proposed RBF network has one hidden layer neural network (NN) with its all parameters being adaptable. The RBF network parameters are optimized by gradient descent method(More)
In this paper, a novel radial basis function (RBF) neural network is proposed and applied successively for online stable identification and control of nonlinear discrete-time systems. The proposed RBF network is a one hidden layer neural network (NN) with its all parameters being adaptable. The RBF network parameters are optimized by gradient descent method(More)
  • 1