A class of investment model of fixed assets with time delay is given in this paper. The model involves distributed parameter system with non-local and delayed boundary condition. The optimal control of accumulation ra...
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ISBN:
(纸本)0780384032
A class of investment model of fixed assets with time delay is given in this paper. The model involves distributed parameter system with non-local and delayed boundary condition. The optimal control of accumulation rate is discussed. The existence and uniqueness of optimal accumulation rate are given using the theory of Banach space.
The aim of this work is to obtain sufficient conditions for multidimensional jump-diffusion processes in distribution stability. The technique employed is to construct appropriate Lyapunov functions.
The aim of this work is to obtain sufficient conditions for multidimensional jump-diffusion processes in distribution stability. The technique employed is to construct appropriate Lyapunov functions.
There are many advantages of artificial neural networks such as high prediction accuracy, robustness, no requirements on data distribution, but knowledge captured by neural networks is not transparent to users. This r...
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ISBN:
(纸本)0780384032
There are many advantages of artificial neural networks such as high prediction accuracy, robustness, no requirements on data distribution, but knowledge captured by neural networks is not transparent to users. This results in a major problem for users of neural network-based systems. It is significant to extract rules from neural networks. This paper proposes a new method for extracting weighted fuzzy production rules from trained neural networks by structural learning based on matrix of importance index.
Support vector machines (SVMs) are powerful tools for providing solutions to classification and function approximation problems. In this paper, the comparison among the four classification methods is conducted. The fo...
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ISBN:
(纸本)0780384032
Support vector machines (SVMs) are powerful tools for providing solutions to classification and function approximation problems. In this paper, the comparison among the four classification methods is conducted. The four methods are Lagrangian Support Vector Machine (LSVM), Finite Newton Lagrangian Support Vector Machine (NLSVM), Smooth Support Vector Machine (SSVM) and Finite Newton Support Vector Machine (NSVM). The comparison of their Algorithm in generating a linear or nonlinear kernel classifier, accuracy and computational complexity is also given. The study provides some guidelines for choosing an appropriate one from four SVM classification methods in a classification problem.
There have been many heuristics to induce decision tree. The traditional ones include ID3 and Min-Ambiguity etc. We have proposed one new heuristic for generalizing decision tree and done some experiments to have prov...
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ISBN:
(纸本)0780384032
There have been many heuristics to induce decision tree. The traditional ones include ID3 and Min-Ambiguity etc. We have proposed one new heuristic for generalizing decision tree and done some experiments to have proved its merits. In this paper, we do more experiments to support that the insensible method is more robust than others and analyze the reason of robustness about the insensible method.
Feature subset selection is one of the widely used and practical methods for pattern recognition and classification, which aims to reduce the number of features to be used. Optimal fuzzy-valued feature subset selectio...
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ISBN:
(纸本)0780384032
Feature subset selection is one of the widely used and practical methods for pattern recognition and classification, which aims to reduce the number of features to be used. Optimal fuzzy-valued feature subset selection (OFFSS) method is efficient for feature subset selection of two-class problem. However, the original OFFSS is not suitable for multi-class problem. This paper gives an unproved version of OFFSS. The OFFSS algorithm is extended to the multi-class problem in which information entropy is used to reduce computational complexity of the method. The feasibility and simplicity of the improved algorithm are demonstrated by applying it to fuzzy decision tree induction.
Personalized information service (PIS) is used to provide users with the useful information according to the personalized interest. Its key technique is the user modeling and the personalized information recommendatio...
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ISBN:
(纸本)0780384032
Personalized information service (PIS) is used to provide users with the useful information according to the personalized interest. Its key technique is the user modeling and the personalized information recommendation. In this paper we first introduce some concepts and techniques for the key technique, and then propose a framework of personalized information service on fat clients. Finally, experiments show that the system framework and the technique are feasible.
Feature subset selection has been playing a very important role on data-mining and pattern recognition. OFFSS (Optimal Fuzzy-valued Feature Subset Selection) is a new feature selection method that selects an optimal f...
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ISBN:
(纸本)0780384032
Feature subset selection has been playing a very important role on data-mining and pattern recognition. OFFSS (Optimal Fuzzy-valued Feature Subset Selection) is a new feature selection method that selects an optimal feature subset by considering both the overall overlapping degree between two classes of examples and the size of feature subset In comparison with other methods such as OFEI, FQI and MIFS, OFFSS has no significant difference in training accuracy of the selected feature subset but has much less computational complexity. Since the OFFSS algorithm is dependent of a similarity measure so that different similarity measures may lead to different feature subsets to be selected. In this paper, we study the impact of numerical results of similarity measures on the results of OFFSS for the same dataset Based on triangle membership functions, we demonstrate the relationship among threshold, feature subset, and classification accuracy that are produced by OFFSS using three classes of similarity measures respectively. And then one similarity measure is found for selecting less number of features.
It is well recognized that the fuzzy measure plays a crucial role in fusion of multiple different classifiers using fuzzy integral. Many papers have focused on how to determine a fuzzy measure. Taking into account the...
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ISBN:
(纸本)0780384032
It is well recognized that the fuzzy measure plays a crucial role in fusion of multiple different classifiers using fuzzy integral. Many papers have focused on how to determine a fuzzy measure. Taking into account the intuitive idea that every classifier has different classification ability to the different class and the important role of the fuzzy integral in the process of information fusion, this paper presents an optimization problem. By solving this optimization problem, the density function can be determined. Our study focuses on the Choquet fuzzy integral and the g-Lamda fuzzy measure. It shows that, in comparison with other fuzzy integrals such as Sugeno integral, the Choquet fuzzy integral and the corresponding g-Lamda fuzzy measure have the better performance for the system classification accuracy.
In the model of fusion of multiple classifiers based on fuzzy integral, the fused results are heavily dependent on the fuzzy measures which are defined on singletons and named fuzzy densities. Therefore, estimation of...
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ISBN:
(纸本)0780384032
In the model of fusion of multiple classifiers based on fuzzy integral, the fused results are heavily dependent on the fuzzy measures which are defined on singletons and named fuzzy densities. Therefore, estimation of the densities or the measures is very important for the entire fusion process. Most of the existing methods regard the accuracy as an essential fac.or in constructing fuzzy densities. In this paper, the uncertainty of classifiers appeared during the classifying process is considered, and a new definition of fuzzy density which incorporated accuracy and uncertainty of the classifier is presented. A new method for determining fuzzy densities is proposed by considering both randomness and the cognitive uncertainty that is inherent in the source. This new method can reasonably measure the importance of each classifier and makes the performance of the fusion model improved significantly.
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