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We consider the least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernel. An explicit expression of the solution of this kernel scheme is derived. Then we provide a sample error with a decay of O(1/√m) and estimate the approximation error in terms of some kind of K-functional.
In this paper, a new fuzzy adaptive local modeling method based on local learning and weighted least squares support vector machine (LS-SVM) is proposed by building fuzzy membership model for the training data. Just as LSSVM, local LS-SVM is also sensitive to outliers or noises. A proper fuzzy membership model based on support vector data description (SVDD) is proposed to deal with the problem. Fuzzy...
A method of forecasting process capability index was recommended based on least squares support vector machines (LS-SVM). The parameters of LS-SVM were optimized by Bayesian framework. The higher precision model of prediction for process capability index was built by optimizing parameters. The prediction results show it have many advantage, such as lower error and higher fitting, and it can be used...
In the identification of nonlinear dynamical models it happens that not only the system dynamics have to be modeled but also the noise has a dynamic character. We show how to adapt Least Squares Support Vector Machines (LS-SVMs) to take advantage of a known or unknown noise model. We furthermore investigate a convex approximation based on over-parametrization to estimate a linear autoregressive noise...
Bath temperature and alumina concentration are two important but hard to measure online parameters of aluminum reduction cell. To this problem, a novel method based on least squares support vector machine (LS-SVM) and chaos optimization is proposed to establish predictive models of the two parameters. This method employs chaos optimization technique to iterate and search in feasible regions so as...
A new online prediction model based on Adaptive Recursive Least Squares Support Vector Machine (ARLSSVM) is presented in this paper, and applied to predict silicon-manganese alloy composition in a 30MVA submerged arc furnace smelting process. By using Recursive Least Squares Support Vector Machine (RLSSVM) regression algorithm, it avoids the difficulty of solving high-dimensional inverse matrix and...
Miniature unmanned helicopter (MUH) is controlled member which is very complicated. There are highly nonlinear, close coupled, time-variation, open-loop unstable etc. The paper introduces a method of model building for miniature unmanned helicopter (MUH), based on least square support vector machine. Having been tested with practical flight data, the method of model building manifests well.
Intelligent heuristic algorithms have been paid more and more attention in solving large-scale, complex optimization problems. Membrane computing is a new branch of natural computing with the features of distribution and great parallelism. PSO is also a simple and effective intelligent computing method. Considering the features of membrane computing and PSO, a hybrid algorithm MCBPSO is proposed in...
Kernel logistic regression (KLR) is a powerful and flexible classification algorithm, which possesses an ability to provide the confidence of class prediction. However, its training-typically carried out by (quasi-)Newton methods-is rather time-consuming. In this paper, we propose an alternative probabilistic classification algorithm called Least-Squares Probabilistic Classifier (LSPC). KLR models...
Ship motion prediction is essential for the safety of shipboard helicopter. If roll/pitch/heave exceeds some prescribed operating limit, potential crashes may occur. In order to prolong the prediction length, a hybrid algorithm based on particle swarm optimization and simulated annealing (HPSO) is proposed to choose the parameters of least square support vector machine (LSSVM). The HPSO-LSSVM method...
Due to ferroalloy submerged arc furnace smelting is an extremely complicated chemistry and physics reaction process, the exact forecast of energy consumption related to the stable and efficient operation of submerged arc furnace, introducing a multi-scale energy consumption prediction model based on least squares support vector machine (LS-SVM), first of all, by conducting the wavelet decomposition...
This paper introduces an efficient geometric approach for data classification that can build class models from large amounts of high dimensional data. We determine a convex model of the data as the outcome of convex hull non-negative matrix factorization, a large-scale variant of Archetypal Analysis. The resulting convex regions or archetype hulls give an optimal (in a least squares sense) bounding...
Aimed at the quantitative analysis of pulverized coal ignition temperature, this paper presents a piecewise least squares support vector machine modeling method, where several sub-models are created according to the burning characteristics of lignite, bituminous coal, lean coal and anthracite coal etc. and the parameters of each sub-model are optimized independently. By implementing the piecewise...
Least Squares Support Vector Machines(LSSVM) regression principle and sparsity configuration were introduced. In this paper online dynamic modeling based on Sparse LSSVM(SLSSVM) was proposed for wood drying process with strong coupling and nonlinear characteristics. The sample data of Fraxinus mandshurica in the speed-down drying stage were gathered in the experiments of a downscaled industrial wood...
In accordance with optimization control and modeling of polymyxin fermentation process, least square support vector machine(LSSVM) model was established, and a method was proposed to find the better parameter value by using quantum-behaved particle swarm optimization(QPSO) which has better search ability. The QPSO-LSSVM model was trained and tested with polymyxin fermentation data-set. The results...
A new data reconciliation algorithm based on least squares support vector machines (LSSVM) for nonlinear dynamic process is proposed in this work. Firstly, response of processes and training data is obtained by computation tools or simulation software. Secondly, the local models of processes are identified by LSSVM. Finally, process data reconciliation is transformed to nonlinear program problem with...
Building a software failure model (SFM) is an important means to compute the software reliability. While there are not enough faults data, the estimation for model parameters is not accurate, which would decrease the fitting precision. In addition, the estimation for parameters of complex models is very complicated. An SFM based on the least squares support vector regression machines (LSSVRM) could...
For estimating fuzzy system which is imprecise and represented by fuzzy sets, a regularized least squares support vector fuzzy regression model is proposed. The proposed fuzzy regression model is applying the fuzzy sets principle in weight vector and bias term. Determining the weight vector and the bias term of the proposed fuzzy regression model only requires a single matrix inversion, as against...
Estimating parameters uncertainties is an important issue in geoacoustic inversion. From the Bayesian rule, the geoacoustic parameters uncertainties are characterized by their posterior probability distributions (PPDs). In present, Grid Searchching (GS), Monte Carlo integration (MCI) and a hybrid SA(Simulated Annealing )-MCMC(Markov Chain Monte Carlo) method has been developed to estimate the PPD...
Analog circuit sizing is the task to determine the sizes of all components in the circuit during automated synthesis. Randomized combinatorial optimization algorithms are desired for quicker determination of a set of optimal sizes of the components. These algorithms require set of multiple performance parameters, for a very large number of sized circuits. Therefore the reduction in time required to...
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