Particle Swarm Optimisers for Cluster formation in Wireless Sensor Networks

IFAC Proceedings Volumes

2012

In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Section: Choosing Regularization Parameters By Optimizing the Loomse mentioning

confidence: 99%

An Elastic Net Orthogonal Forward Regression Algorithm

Hong

IFAC Proceedings Volumes

2012

“…That is, if a particle is outside the search space, it is moved back inside the search space randomly, rather than forcing it to stay at the border [14].…”

Section: Algorithm Flopsmentioning

confidence: 99%

Particle Swarm Optimisation Aided Minimum Bit Error Rate Multiuser Transmission

Wang

2009 IEEE International Conference on Communications

Tan

et al. 2009

Abstract-We consider the downlink of multiuser system from a transmitter equipped with multiple antennas to multiple non-cooperative single-antenna mobile receivers. Particle swarm optimisation (PSO) algorithm is invoked to solve the constrained nonlinear optimisation problem for the minimum bit error rate (MBER) multiuser transmission (MUT). The proposed PSO aided MBER-MUT scheme provides much better performance over the conventional minimum mean-square-error MUT scheme, and it achieves a much lower complexity compared to the state-of-theart sequential quadratic programming based MBER MUT.

“…The PSO method is popular owing to its simplicity in implementation, ability to rapidly converge to a ''reasonably good'' solution and to ''steer clear'' of local minima. It has been successfully applied to wide-ranging optimization problems [34][35][36][37][38]. In order to satisfy the shaping parameter constraints, the normalisation are applied in PSO as appropriate.…”

Section: Introductionmentioning

confidence: 99%

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

Hong

2012

Neurocomputing

a b s t r a c tIn this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.