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In reinforcement learning, it is important to get nearly right answers early. Good prediction early can reduce the prediction error afterward and accelerate learning speed. We propose fuzzy Q-map, function approximation algorithm based on on-line fuzzy clustering in order to accelerate learning. Fuzzy Q-map can handle the uncertainty owing to the absence of environment model. Applying membership function...
The error-correcting output codes (ECOC) method reduces the multi-class learning problem into a series of binary classifiers. In this paper, we propose a modified Hadamard-type ECOC method. This method uses both N'th order and N/2'th-order Hadamard matrix to construct error correcting output codes, which is called hybrid Hadamard ECOC. Experiments based on dichotomizers of support vector machines...
This paper describes a system for detection and classification of moving objects based on support vector machines (SVM) and using 3D data. Two kinds of camera systems are used to provide the classification system with 3D range images: time-of-flight (TOF) camera and stereo vision system. While the former uses the modulated infrared lighting source to provide the range information in each pixel of...
This paper presents a new model to identify the criticality class of spare parts (SPs) based on fuzzy and grey theory. By using group-discussing and anonymous questionnaire methods, index set for the evaluation of criticality class of SPs are put forward. A new algorithm, which is integrated of modified Delphi, AHP, fuzzy comprehensive evaluation and grey relational analysis, is designed to convert...
The fuzzy sliding mode control problem is studied for a class of uncertain nonlinear systems with time delay. The Takagi-Sugeno (T-S) fuzzy model is employed to represent the uncertain nonlinear systems with time delay. By using a novel virtual state feedback technique and linear matrix inequalities (LMI), sufficient conditions for design of robust sliding mode plane are given based on the Lyapunov...
This paper deals with new regression method of predicting fuzzy multivariable nonlinear regression models using triangular fuzzy numbers. The proposed method is achieved by implementing the locally weighted least squares support vector machine regression where the local weight is obtained from the positive distance metric between the test data and the training data. Two types of distance metrics for...
More recently, the partial least square regression (PLSR) has been suggested applying to pattern discrimination. However, the eigen-structure problem essential to the discriminant PLSR basically depends on a slightly modified version of the between-class scatter matrix Sb. Unfortunately, the class structure information contained in the within-class matrix Sw is skipped when using PLSR for discrimination...
Topic-based mixture model (TBMM) is a learning algorithm for factored classification. In factored classification, the class label is factored into a vector of class features. For example, the class label for a personal Web page at a university might be described by two features: the academic discipline of the person, and their position (e.g., 'chemistry professor' or 'physics student'). An approach...
The current algorithms of learning the structure of dynamic Bayesian networks attempt to find single "best" model. However, this approach ignores the uncertainty in model selection and is prone to overfitting and local optimal problem. Markov chain Monte Carlo algorithm based on Bayesian model averaging can provide a way for accounting for this model uncertainty, but the convergence is too...
The Dempster's combination rule has been widely used recently since it is a convenient and promising method to combine multi-source information with their own confidence degrees/evidences. On the other hand, it has been criticized and debated upon its some counterintuitive behavior and too restrictive requirements. To clarify the theoretical essence of the Dempster's combination rule and provide a...
This paper describes a new method for sensors multiple fault diagnosis and isolation. The information fusion method is based on expanded evidence theory, which offers a new combination rule under different but compatible frames of discernment. By this method, the maximum of available knowledge supported by each source of information is exploited and the uncertainty of the effective state between the...
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