Self-generation RBFNs using evolutional PSO learning

In this paper, a powerful evolutional particle swarm optimization (PSO) learning algorithm is developed that self-generates radial basis function neural networks (RBFNs) to deal with three nonlinear problems. In general, the simple but efficient PSO learning algorithm is adapted for solving complex and global optimization problems. With computer simulation, this paper illustrates, in detail, how the PSO algorithm can automatically tune the centers and spreads of each radial basis function, and the connection weights. Furthermore, in parallel with the free RBFNs parameters, the number of radial basis functions of the constructed RBFNs can be automatically minimized by choosing a special fitness function which takes this factor into account. Therefore, the proposed PSO allows high accuracy within a short training time when determining RBFNs with small numbers of radial basis functions. Simulation results demonstrate the proposed RBFNs’ efficiency to stabilize an inverted pendulum, approximate a nonlinear function and approximate a discrete dynamic system.