Learning of B-spline Neural Network Using New Particle Swarm Approaches

New approaches of particle swarm optimisation algorithm based on Gaussian and Cauchy distributions to adjust the control points of B-spline neural networks are proposed. B-spline networks are trained by gradient-based methods, which may fall into local minimum during the learning procedure. To overcome the problems encountered by the conventional learning methods, particle swarm optimisation  a swarm intelligence methodology  can provide a stochastic search for global optimisation of B-spline networks for nonlinear system identification. Simulation results show the potential of the proposed optimisation with particle swarm of B-spline networks for the identification of Rössler system.