Valerie Haggan-Ozaki

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This paper considers the nonlinear systems modeling problem for control. A structured nonlinear parameter optimization method (SNPOM) adapted to radial basis function (RBF) networks and an RBF network-style coefficients autoregressive model with exogenous variable model parameter estimation is presented. This is an off-line nonlinear model parameter(More)
This paper considers the modeling and control problem for nonstationary nonlinear systems whose dynamic characteristics depend on time-varying working-points and may be locally linearized. It is proposed to describe the system behavior by the RBFARX model, which is an ARX model with Gaussian radial basis function (RBF) network-style coefficients depending(More)
* College of Information Science & Engineering, Central South University, Changsha, 410083, China. Currently a visiting researcher at the Institute of Statistical Mathematics, 4-6-7 Minami Azabu, Minato-ku, Tokyo 106-8569, Japan. Fax: +81-3-5421-8796, E-mail: peng@ism.ac.jp ** The Institute of Statistical Mathematics, 4-6-7 Minami Azabu, Minato-ku, Tokyo(More)
This paper presents a modeling and control method for thermal power plants having nonlinear dynamics varying with load. First a load-dependent exponential ARX (Exp-ARX) model that can effectively describe the plant nonlinear properties and requires only off-line identification is presented. The model is then used to establish a constrained multivariate(More)
On the basis of the market microstructure theory and the continuous time stochastic volatilitystyle microstructure model, a discrete time stochastic volatility microstructure model with stateobservability is proposed for describing the dynamics of financial markets. From the discrete time microstructure model proposed, estimates of two immeasurable state(More)
This paper considers modeling and control problems of the non-stationary nonlinear processes whose dynamics depends on the working point. A hybrid RBF-ARX model-based predictive control (MPC) strategy without resorting to on-line parameter estimation for this kind of processes is presented. The RBF-ARX model is composed of the RBF networks and a rather(More)
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