A Sigma-Pi-Sigma Neural Network (SPSNN)
Neural Processing Letters
Identification of nonlinear discrete-time systems using raised-cosine radial basis function networks
International Journal of Systems Science
Computation of local radius of information in SM-IBC identification of nonlinear systems
Journal of Complexity
Performance of deterministic learning in noisy environments
Neurocomputing
Model-based update in task-level feedforward control using on-line approximation
Automatica (Journal of IFAC)
Human gait recognition via deterministic learning
Neural Networks
Adaptive fuzzy control for differentially flat MIMO nonlinear dynamical systems
Integrated Computer-Aided Engineering
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Considers radial basis function (RBF) network approximation of a multivariate nonlinear mapping as a linear parametric regression problem. Linear recursive identification algorithms applied to this problem are known to converge, provided the regressor vector sequence has the persistency of excitation (PE) property. The main contribution of this paper is formulation and proof of PE conditions on the input variables. In the RBF network identification, the regressor vector is a nonlinear function of these input variables. According to the formulated condition, the inputs provide PE, if they belong to domains around the network node centers. For a two-input network with Gaussian RBF that have typical width and are centered on a regular mesh, these domains cover about 25% of the input domain volume. The authors further generalize the proposed solution of the standard RBF network identification problem and study affine RBF network identification that is important for affine nonlinear system control. For the affine RBF network, the author formulates and proves a PE condition on both the system state parameters and control inputs