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Apprenticeship learning has recently attracted a wide attention due to its capability of allowing robots to learn physical tasks directly from demonstrations provided by human experts. Most previous techniques assumed that the state space is known a priori or employed simple state representations that usually suffer from perceptual aliasing. Different from previous research, we propose a novel approach...
Extreme learning machine (ELM) has attracted considerable attention in recent years due to its numerous applications in classification and regression. In this study, the authors investigate the performance of an ELM-based threshold selection algorithm for 60 GHz millimetre wave time of arrival estimation using energy detector (ED). A hybrid metric based on the skewness, kurtosis, standard deviation,...
A fault diagnosis method of probabilistic neural network was presented for turbine generator unit. the probabilistic neural network is based on probability statistics theory and Bayes classification rule, so it can efficiently identify and diagnose the fault of turbine generator unit. Theoretical analysis, practical procedure of neural network setting and training are given out. The simulation results...
We present an innovative flexible computation support service for financial risk management. Commercial bank faces a higher requirement on quantitative risk management from Basel II accord. In practice, quantitative risk computation needs both the internal and external data, and the risk calculation models and corresponding parameters should be updated for a periods of time. To deal with these challenges,...
The inspection process of rolling coil wastes time and increases cost of enterprise. The enterprise is eager to find a prediction analysis method which can predict performance of rolling coil by some of relative data. Data Warehouse provide plenty of data for performance prediction. Because productive process is complicated, the statistical analysis method and the traditional machine learning method...
We present a novel method to learn the priorities of rules for sequential rule execution during the running of a rule engine system. The priority based ordering of rules influences the condition evaluation count of rule execution. User-assigned priorities can not guarantee optimal execution performance. We present and prove that the execution count and the dependency relationship are two factors influencing...
Rule flow is a directed graph with condition and action operator over business object's attributes. The results from the the rule flow is usually not linearly separable, which proposes great challenges to rule flow learning from sample results. This paper proposes to use multiple linear classifiers for rule flows whose condition is the linear combination of business object attributes. This is a two-step...
Rough Set Theory is increasingly becoming one of the most effective tools for pattern recognition until now. While, in rough set theory, the discretization of continuous attributes is indispensable in any data preprocessing for the rough set computational intelligence algorithm and also critical to the quality of rule generated. To solve this problem, and based on the available researching achievements,...
In fault pattern recognition field, the real-time online fault diagnosis is a new requirement especially from the high-speed machines, and also the magnificent researching direction. The precision and speed of the classification are important research issues in fault pattern recognition for this kind of intelligent fault diagnosis. Although many improved ANN (artificial neural net) methods have been...
Local binary pattern (LBP) is a powerful texture descriptor that is gray-scale and rotation invariant according to T. Ojala et al. (2002). Because texture is one of the most clearly observable features in low-resolution palmprint images, we think local binary pattern based features are very discriminative for palmprint identification. In this paper, we propose a palmprint identification approach using...
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