This paper studies a class of nonlinear networked control systems based on the Euler approximate model method.

To achieve this objective, bubble fluidized bed with single particles and binary mixture respective was simulated with Euler-Euler model based on f.

Based on approximate models and genetic arithmetic,a scheme for the plane with multi-flaps was proposed to implement the multidisciplinary design optimization(MDO) of full-envelope flight control law and control effectors geometry & their placement.

In this approach, uniform design method was employed to generate the geometric information of samples, whose performances are calculated by CFD technique; the relationship between the geometric information and its performances of samples is mapped by the approximate model constructed by parallel artificial networks (PANN); genetic algorithms is employed to find .

In the light of the huge amount of time-consuming CFD (computational fluid dynamics) calculations during the numerical optimization of a turbo-machinery unit,developed was an optimized design method based on an approximate model.

To reduce huge computation of the traditional stochastic optimization methods for engineering optimization,approximation model methods with acceptable accuracy for engineering design are developed based on the statistical theory.

Based on the original algorithm, the Kriging model is employed as an approximation model for Bayesian analysis.

The paper presents an algorithm combining square response surface and Radial Basis Function Neural Network(RBFNN) to solve the problem that RBFNN is often difficult to meet the precision request of approximation model.