Based on analysis of basic cubic spline interpolation, the clamped cubic spline interpolation is generalized in this paper. The methods are presented on the condition that the first derivative and second derivative of...
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As an important component of data mining, Cluster Analysis (CA) has being attached importance to artificial intelligence, machine learning and other fields. Traditional clustering methods have been studied for a relat...
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As an important component of data mining, Cluster Analysis (CA) has being attached importance to artificial intelligence, machine learning and other fields. Traditional clustering methods have been studied for a relatively long time;their technologies are mature and consequently they are well-applied. However, they are insufficient in clustering accuracy, noise sensitivity, along with effect on mass of data and non-convex clustering. Granular computing, which is regarded as a label of theories, methodologies, techniques, and tools, is an emerging conceptual and computing par informationprocessing. It plays an important role informationprocessing for fuzzy, uncertainty, partial truth and soft computing and is one of the main study stream in A.I. This paper introduces some new clustering methods, such as Fuzzy clustering, Clustering Algorithm Based on Rough Set, and clustering algorithm based on quotient space theory, emphatically expounds the basic thought and typical algorithms of these methods, and comparative analysis is carried out among these methods. Finally, new clustering algorithms are prospected and we put forward the value of research direction.
Based on analysis of cubic spline interpolation, the differentiation formulas of the cubic spline interpolation on the three boundary conditions are put up forward in this paper. At last, this calculation method is il...
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The rough neural networks (RNNs) are the neural networks based on rough set and one kind of hot research in the artificial intelligence in recent years, which synthesize the advantage of rough set to process uncertain...
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The grey system forecasting model, neural network forecasting model and support vector machine forecasting model are proposed in this paper. Taking the road goods traffic volume from year of 1996 to 2003 in the whole ...
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The bottleneck problem has emerged in feature selection when processing high-dimension and large-scale data, so in the past decade, the researches on feature selection have not adhere to the traditional algorithms and...
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The bottleneck problem has emerged in feature selection when processing high-dimension and large-scale data, so in the past decade, the researches on feature selection have not adhere to the traditional algorithms and ideas, showing a new trend of combining many new mathematical tools, which opens new space for feature selection applied in pattern recognition and makes further development in knowledge discovery and data mining. Granular computing has begun to take shape and show effect as a new idea of intelligentinformationprocessing, which creates the conditions for feature selection applied in data. The paper describes a new feature selection algorithm, basing on granular computing and making rough set approximation as background, the algorithm generates the granules, using a tolerance function, distinguishes noise data and inconsistent data, to achieve feature selection in the information table, and be effective for large-scale data sets.
SVM (Support Vector Machines) is a novel algorithm of machine learning which is based on SLT (Statistical Learning Theory). It can solve the problem characterized by nonlinear, high dimension, small sample and local m...
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This paper systematically studies the problem of decision rule acquisition in inconsistent incomplete decision systems (IIDSs). First, a tolerance granular framework model based on tolerance granular computing is pres...
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This paper systematically studies the problem of decision rule acquisition in inconsistent incomplete decision systems (IIDSs). First, a tolerance granular framework model based on tolerance granular computing is presented, which is suitable for variety types of decision rules in IIDSs; secondly, with the proposed model, a framework for acquiring all minimum decision rule sets for each type is given, which solves the problem of decision rule acquisition in IIDSs to a certain degree; finally, an example is given to show the efficiency of our framework.
This paper presents a method for monitoring the particle swarm optimization process that accounts for the random nature of the system's external environment and the fuzzy character of the particles' decision-m...
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This paper presents a method for monitoring the particle swarm optimization process that accounts for the random nature of the system's external environment and the fuzzy character of the particles' decision-making process by regarding the fitness function as a fuzzy random variable. The belief level value and the Borel set of chance measures are also used to monitor the particle swarm optimization process and two simulation experiments show the congregate scenes of the particle swarm optimization.
Detection of moving vehicles plays a very important role in intelligent Transport. Aiming at the deficiency of moving vehicle detection, we proposed the adaptive detection method of moving vehicles based on the online...
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