Nonlinear System Identification Using Clonal Particle Swarm Optimization-based Functional Link Artificial Neural Network

In this paper, the clonal particle swarm optimization (C-PSO)-based functional link artificial neural network model (FLANN) has been applied for the identification of a nonlinear system. System identification in different challenging situations such as noisy and time varying environments has been a matter of great concern for researchers and scientists for the last few decades. Other variants of the FLANN network, trained with some of the optimization techniques, such as the genetic algorithm (GA), particle swarm optimization (PSO), and comprehensive learning particle swarm optimization (CLPSO) have also been applied in this interesting field of research. The proposed C-PSO algorithm is based on the clonal principle in a natural immune system. In the C-PSO, the essence of the clonal operator is to generate a set of clone particles near the expected candidate solution. Hence, the search spaces are enlarged, and the diversity of the cloned particles is increased to avoid trapping in local minima. The simulation study reveals the superiority of the proposed C-PSO-based FLANN model, in terms of the convergence rate, over other competitive networks. The performance comparison is also carried out based on the computational complexity.