Showing papers by "Mojtaba Ahmadieh Khanesar published in 2007"
27 Jun 2007
TL;DR: This algorithm is shown to be a better interpretation of continuous PSO into discrete PSO than the older versions and a number of benchmark optimization problems are solved using this concept and quite satisfactory results are obtained.
Abstract: Particle swarm optimization (PSO) as a novel computational intelligence technique, has succeeded in many continuous problems. But in discrete or binary version there are still some difficulties. In this paper a novel binary PSO is proposed. This algorithm proposes a new definition for the velocity vector of binary PSO. It will be shown that this algorithm is a better interpretation of continuous PSO into discrete PSO than the older versions. Also a number of benchmark optimization problems are solved using this concept and quite satisfactory results are obtained.
355 citations
27 Jun 2007
TL;DR: Sliding mode control of rotary inverted pendulum is presented and based on this performance two sliding surfaces are designed, then system is controlled by proper definition of a lyapunov function.
Abstract: This paper presents sliding mode control of rotary inverted pendulum. Rotary inverted pendulum is a nonlinear, unstable and non-minimum-phase system. Designing sliding mode controller for such system is difficult in general. Here, first the desired performance is introduced and based on this performance two sliding surfaces are designed, then system is controlled by proper definition of a lyapunov function. The lyapunov function designed puts more emphasis on the control of the inverted pendulum rather than the control of the motor.
28 citations
12 Dec 2007
TL;DR: In this study, a fuzzy system is added to the classical sliding mode controller to adaptively estimate the equivalent signal of the sliding mode controllers and the stability of the controller and the adaptation law is guaranteed.
Abstract: In this study, a fuzzy sliding mode controller for a rotary inverted pendulum is designed. Sliding mode controllers are high performance nonlinear controllers. Not only Sliding mode controller stabilizes the system under control effectively but also it robustly compensates the effect of bounded uncertainties and shows invariance properties in the presence of bounded disturbance. In this study, a fuzzy system is added to the classical sliding mode controller to adaptively estimate the equivalent signal of the sliding mode controller. In addition, using Lyapunov theory the stability of the controller and the adaptation law is guaranteed.
26 citations
24 Sep 2007
TL;DR: This learning algorithm aims to solve main problems of the gradient descent based methods for the optimization of the RFNNs, which are instability, local minima and the problem of generalization of trained network to the test data.
Abstract: In this study, a hybrid learning algorithm for training the recurrent fuzzy neural network (RFNN) is introduced. This learning algorithm aims to solve main problems of the gradient descent (GD) based methods for the optimization of the RFNNs, which are instability, local minima and the problem of generalization of trained network to the test data. PSO as a global optimizer is used to optimize the parameters of the membership functions and the GD algorithm is used to optimize the consequent part's parameters of RFNN. As PSO is a derivative free optimization technique, a simpler method for the train of RFNN is achieved. Also the results are compared to GD algorithm.
17 citations