An inferential estimation strategy of Texaco gasification furnace temperature based on primary component analysis and new radial basis function networks is proposed.

Based on improved hybrid models an inferential estimator for the output conversion of fixedbed reactors is presented.

Based on kernel ridge regression (KRR) inferential estimator, a novel inferential control strategy is proposed to implement on-line examination and control of aviation kerosene dry point in an atmospheric distillation column.

Simulation is carried out by using the new algorithm,and the results show that the indirect presumption estimation features high precision and effectiveness by selecting suitable auxiliary function.

In order to overcome the difficulties induced by the nonlinearity, time-variation and indeterminacy of polymerization process, a manifold series-parallel hybrid model for inference estimation was presented, which was based on process mechanism knowledge and the results obtained from neural networks models and regression models.

Synthesizing the two identification methods of weighted least square and resricted memory, the mutivariable system recursive estimate algorithems of unknown parameter of autoregressive models in the presence of controlled inputare are given.

In this paper,the two identification methods of weighted least square and restricted memory are synthesized and it gives out recursive estimate algorithms of unknown parameter of autoregressive model with controlled input.