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Selective ensemble (SEN) learning algorithm can improve generalization performance of regression model. How to select SEN models' leaning parameters is an important issue. In this paper, based on kernel partial least squares (KPLS) constructed candidate sub-models, a new SENKPLS modeling method is proposed using dual layer genetic algorithm (GA) optimization. At first, adaptive GA (AGA) is used to...
An important objective of building monitoring is to diagnose the building states and evaluate possible damage. This is a data classification problem. The building states come from many on-line sensors. Normal classification methods, such as support vector machine (SVM), cannot classify this large data stream. In this paper, the classical SVM is extended to an on-line classifier (OLSVM). This SVM can...
Different frequency spectral feature sub-sets of mill shell vibration and acoustical signals contain different information for modeling parameters of mill load. Selective ensemble modeling based on manipulate training samples can improve generalization performance of soft sensor model. Based on the former studies, we proposed a new dual layer selective ensemble learning strategy. At first, vibration...
This paper describes a novel non-linear modeling approach by on-line clustering, fuzzy rules and fuzzy support vector machines. Structure identification is realized by on-line clustering method and support vector machines, and the rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, tue upper bounds of modeling...
Mill load (ML) estimation plays a major role in improving the grinding production rate (GPR) and the product quality of the grinding process. The ML parameters, such as mineral to ball volume ratio (MBVR), pulp density (PD) and charge volume ratio (CVR), reflect the load inside the ball mill accurately. The relative amplitudes of the high-dimensional frequency spectrum of shell vibration signals contain...
Parameters of ball mill load (ML) affects production capacity and energy consumption of the grinding process, which have stronger correlation with shell vibration spectrum. A novel spectral features extraction and selection approach combined with empirical mode decomposition(EMD), power spectral density(PSD), kernel principal component analysis(KPCA), genetic algorithms(GA) and partial least square(PLS)...
In this paper, we present an algorithm to speed up the training of SVM. The proposed algorithm is based on SV candidates selection strategy, exploiting the observation that typically from a set of elements with the same label, if there exist SV, then most of them are on the boundary of the set. We compute the non convex hull sets that envelop the elements with the same label, this sets have in general...
Normal support vector machine (SVM) algorithms are not suitable for classification of large data sets because of high training complexity. This paper introduces a novel SVM classification approach for large data sets. It has two phases. In the first phase, an approximate classification is obtained by SVM using fast clustering techniques to select the training data from the original data set. In the...
Combining neural networks and fuzzy systems is a great tool for modeling nonlinear systems. Few researches have presented useful or practical results on the case of lack of data, which does not provide necessary information for training the model. In this paper, we proposed a new modeling idea based on nonparametric regression, which provide us prior information for constructing the fuzzy system....
It is difficult to establish a black-box model for sparse data, because not enough data can be applied for training. This paper presents a novel identification approach using multiple fuzzy neural networks. It focuses on structure and parameters uncertainty which have been widely explored in the literature. Firstly, the sparse data are used within a fixed time interval to generate model structure...
In recent years support vector machines (SVM) has received considerable attention due to its high generalization ability and performance for a wide range of applications. However, the most important problem of this method is slow training for classification problems with a large data sets because the quadratic form is completely dense and the memory requirements grow with the square of the number...
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