Although the RBF is the most widely used SVR kernel function,the expression complexity of RBF makes it difficult to get analytical formula of rolling optimization in model predictive control.A model of predictive control based on RBF-SVR is established,the multi-agent particle swarm optimization (...
The calculation formula is as follows R2=1−∑i=1m(yi−y^i)2/∑i=1m(yi−y¯)2 (8) Where, m is the number of samples to construct the surrogate model; yi is the actual response function value, y^i is the predicted function value of surrogate model which is constructed using...
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Moreover, our proposed MPE formula outperforms other selection methods used for fault-free neural networks. 展开 关键词: faulty neural networks multi-node open fault radial basis function kernel width mean prediction error DOI: 10.1007/s11063-010-9145-x ...
{*}\)is the normal vector of the optimal hyperplane, andb1is the offset of the optimal hyperplane. In the solution and analysis of this type of optimization problem, the Karush–Kuhn–Tucker (KKT) condition will play a very important role. In the second constraint formula, the solution ...
After that, the L2-soft-margin parameter C is obtained by an analytic formula in terms of a jackknife estimate of the perturbation in the eigenvalues of the kernel matrix. In comparison with SVM model selection based on differentiable bounds, such as radius/margin bounds, experimental results on...
Curve fittingNeural networksStopping powerThis article presents a new framework for fitting measured scientific data to a simple empirical formula by introducing an additional linear neuron to the standard Gaussian kernel radial basis function (RBF) neural networks. The proposed method is first used to...
Curve fittingNeural networksStopping powerThis article presents a new framework for fitting measured scientific data to a simple empirical formula by introducing an additional linear neuron to the standard Gaussian kernel radial basis function (RBF) neural networks. The proposed method is first used to...
Simulation results show that the chosen optimal kernel width by our proposed MPE formula is very close to the actual one by the conventional method. Moreover, our proposed MPE formula outperforms other selection methods used for fault-free neural networks....
Some famous scholars proposed an experienced formula to select the optimal shape parameters [5,6,7]. Rippa chooses the best shape parameter c based on the minimization of the estimation of the error function and proposes the leave-one-out cross-validation (LOOCV) method [8]. However, it was...