Generalized Regression Neural Network (GRNN) is usually applied to the Function approximation. This paper, based on the principle of GRNN, presents a method for the predictive model of nonlinear complex system. The presented algorithm is applied to the learning and predicting process for the system modeling. The simulations show the described method has good effects on predicting the dynamic pr...

Model predictive control requires the solution of a sequence of continuous optimization problems that are nonlinear if a nonlinear model is used for the plant. We describe briefly a trust-region feasibility-perturbed sequential quadratic programming algorithm (developed in a companion report), then discuss its adaptation to the problems arising in nonlinear model predictive control. Computation...

N "For the Sake of the Children" [Summer 1992], Richard Gill criticizes a study several collaborators and I published on the effects of divorce on children.* Gill first charges that our methodology and our interpretation are flawed, then launches into a broader discussion of contemporary marriage and divorce. In an earlier Public Interest article, "Day Care or Parental Care?" [Fall 1991], Gill ...

Previous research indicates that the human decision-making process is somewhat nonlinear and that nonlinear models would be more suitable than linear models for developing advanced decision-making models. In our study, we tested this generally held hypothesis by applying linear and nonlinear models to expert's decision-making behavior and measuring the predictive accuracy (predictive validity) ...

We describe a genetic programming system which learns nonlinear predictive models for lossless image compression. Sexpressions which represent nonlinear predictive models are learned, and the error image is compressed using a Huffman encoder. We show that the proposed system is capable of achieving compression ratios superior to that of the best known lossless compression algorithms, although i...

Generalized predictive control (GPC) algorithm has been applied to all kinds of industry control systems. But systemic and effective method for nonlinear system has not been found. To this problem, this paper integrates the characteristics of PID technology and GPC, present a PID generalized predictive control algorithm for a class of nonlinear system, and improves the control quality of the sy...

We describe a genetic programming system which learns nonlinear predictive models for lossless image compression. S-expressions which represent nonlinear predictive models are learned, and the error image is compressed using an adaptive Huffman encoder. We show that the proposed system is capable of achieving compression ratios superior to that of the best known lossless compression algorithms.

This paper discusses an algorithm for efficiently calculating the control moves for constrained nonlinear model predictive control. The approach focuses on real-time optimization strategies that maintain feasibility with respect to the model and constraints at each iteration, yielding a stable technique suitable for suboptimal model predictive control of nonlinear process. We present a simulati...

A MIMO nonlinear adaptive predictive control strategy is presented in which the wavelet neural network based on a set of orthogonal wavelet functions is adopted. A nonlinear mapping from the network-input space to the wavelons output space in the hidden layer is performed firstly. Then, the output layer uses a linear structure. Its weight coefficients can be estimated by a linear least-squares ...

In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi–Sugeno (T–S) type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong nonline...