Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Matrix computations (3rd ed.)
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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By kernelizing the traditional least-square based identification method, an adaptive kernel learning (AKL) network is proposed for nonlinear process modeling, which utilizes kernel mapping and geometric angle to build the network topology adaptively. The generalization ability of AKL network is controlled by introducing a regularized optimization function. Two forms of learning strategies are addressed and their corresponding recursive algorithms are derived. Numerical simulations show this simple AKL networks can learn the process nonlinearities with very small samples, and has excellent modeling performance in both the deterministic and stochastic environments.