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This paper investigates the parameter estimation of errors-in-variables Wiener (EIV-W) nonlinear systems. In such nonlinear systems, both input and output contain interference noises, and some intermediate processes are also interfered by noises. The hierarchical technology is applied to decompose the whole system into two subsystems firstly. For the linear subsystem, in order to obtain unbiased estimates of model parameters, a bias compensation method is introduced. Then, the bias-compensated least squares (BLS) algorithm is proposed. For the nonlinear subsystem, on the basis of particle swarm optimisation (PSO), the fuzzy control technology is added to improve the ability of jumping out of the local optimum. Thus, a bias-compensated least squares and fuzzy PSO based hierarchical (BLS-FPSO-H) method is derived at last. In simulation, a numerical example and a case study about the carbon fibre stretching process are implemented. Results indicate that the BLS-FPSO-H algorithm can effectively identify EIV-W nonlinear systems, the convergence speed and identification accuracy are greatly improved than the basic PSO method and some other PSO variants. [ABSTRACT FROM AUTHOR]

This paper investigates the leader-following consensus problem for a singular multi-agent system with nonlinear dynamics and signed digraph topology. Firstly, an algorithm is proposed to address the structural balance problem of the signed network, which is suitable for all types of signed graphs. Secondly, by using M-matrix theory, the bipartite consensus conditions for the singular nonlinear multi-agent system are established when the topology digraph is structurally balanced. Thirdly, the global asymptotic stability conditions are presented when the signed network is structurally unbalanced. Both the controllers are designed by using pinning control strategies. Finally, numerical examples are given to illustrate the correctness and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]