System identification: theory for the user
System identification: theory for the user
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Nonlinear Parametric Model Identification with Genetic Algorithms. Application to a Thermal Process
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
On one method of structural-parametric identification of dynamic systems
Automation and Remote Control
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Studying alternative (with respect to conventional) methods of stochastic parameter identification of nonlinear discrete plants is shown to be of current interest. For the first time, generalized probabilistic criteria for discrete time are proposed to solve the problem of parametric identification of nonlinear stochastic plants. A parametric identification algorithm is proposed based on the criterion of the minimum probability of estimation error. A toy example is considered to illustrate the efficiency of the proposed approach.