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Locally weighted regression (LWR) is a local memory learning strategy which performs regression around an interest point, which is very efficient for learning the modeling of nonlinear system. This paper researches the possibility of using locally weighted regression for prediction modeling of a nonlinear system for converter re-vanadium in metallurgical(More)
SVM (support vector machine) is an important tool of solving the nonlinear problem. This paper introduces the methods of constructing support vector stepwise regression - starting from the multiple linear regression model of the sample subset to search the support vectors. It provides a speedy algorithm of support vector stepwise regression with the aim of(More)
The aim of this note is to study robust non-fragile stabilization and robust non-fragile D-stabilization for the delta-operator formulated linear uncertain systems. The uncertainty is assumed to be norm-bounded and multiplicative controller gain variations are assumed in existence in the state feedback gain. Based on the notion of non-fragile quadratic(More)
To solve some problems brought about by the Hill Starting Aid Equipment mounted in the vehicle with an Automated Mechanical Transmission, in this work the authors analyzed the vehicle's dynamics problem and the control problem when the vehicle starts on a slope road, then presented an intelligent control method based on the iterative learning theory for(More)
A new approach is proposed to predict the silicon content in hot metal with neural network trained by chaos particle swarm optimization. Firstly, an advanced particle swarm optimization algorithm based on chaos search(CPSO) is presented to enhance the local searching ability and improve the convergence speed. Then CPSO is applied to train neural network and(More)
This paper researches the possibility of using locally weighted algorithm for intelligent modeling of a nonlinear system for vanadium extraction in metallurgical process and proposes some optimized methods by finding the optimized regression coefficients by gradient descent and kernel function bandwidth by weighted distance. But kernel matrix computation(More)
This paper studied a Radial Basis Function(RBF) network learning algorithm based on immune recognition principle. In the algorithm, the recognized data is regarded as antigens and the compression mapping of antigens as antibodies, i, e, the hidden layer centers. In order to improve convergence speed and precision of the RBF network, we adopt the least(More)
Kernel regression of RBF NN building on the notion of density estimation is frequently used for modeling prediction. But kernel matrix computation for high dimensional data source demands heavy computing power. To shorten the computing time, the paper designs a parallel algorithm to compute the kernel function matrix of kernel regression of RBF NN. The(More)
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