Chong Wu

Communicating between information systems

Communication between information systems is a basic problem in granular computing. The concept of homomorphism is a useful mathematical tool to study the communication between two information systems. In this paper, some properties of information systems under homomorphisms are investigated. The concepts of consistent functions are first introduced and their properties are investigated. The concepts of relation mappings between two universes are then proposed in order to construct a binary relation on one universe according to the given binary relation on the other universe. The main properties of the mappings are studied. Finally, the notions of homomorphisms of information systems based on arbitrary binary relations are proposed, and it is proved that attribute reductions in the original system and image system are equivalent to each other under the condition of homomorphism.

Data compression with homomorphism in covering information systems

In reality we are always faced with a large number of complex massive databases. In this work we introduce the notion of a homomorphism as a kind of tool to study data compression in covering information systems. The concepts of consistent functions related to covers are first defined. Then, by classical extension principle the concepts of covering mapping and inverse covering mapping are introduced and their properties are studied. Finally, the notions of homomorphisms of information systems based on covers are proposed, and it is proved that a complex massive covering information system can be compressed into a relatively small-scale information system and its attribute reduction is invariant under the condition of homomorphism, that is, attribute reductions in the original system and image system are equivalent to each other.

Applying RBR and CBR to develop a VR based integrated system for machining fixture design

A fixture is a special tool used to accurately and stably locate the workpiece during machining process. Proper fixture design improves the quality and production of parts and also facilitates the interchangeability of parts that is prevalent in much of modern manufacturing. This study combines the rule-based reasoning (RBR) and case-based reasoning (CBR) method for machining fixture design in a VR based integrated system. In this paper, an approach combines the RBR and fuzzy comprehensive judgment method is proposed for reasoning suitable locating schemes and locating features. Based on the reasoning results, a CBR method for machining fixture design is then presented. This method could help designers, by referencing previous design cases, to make a conceptual fixturing solution quickly. Finally, the implementation of proposed system is outlined and cases study has been used to demonstrate the applicability of the proposed approach.

Research of fast SOM clustering for text information

The state-of-the-art text clustering methods suffer from the huge size of documents with high-dimensional features. In this paper, we studied fast SOM clustering technology for Text Information. Our focus is on how to enhance the efficiency of text clustering system whereas high clustering qualities are also kept. To achieve this goal, we separate the system into two stages: offline and online. In order to make text clustering system more efficient, feature extraction and semantic quantization are done offline. Although neurons are represented as numerical vectors in high-dimension space, documents are represented as collections of some important keywords, which is different from many related works, thus the requirement for both time and space in the offline stage can be alleviated. Based on this scenario, fast clustering techniques for online stage are proposed including how to project documents onto output layers in SOM, fast similarity computation method and the scheme of Incremental clustering technology for real-time processing, We tested the system using different datasets, the practical performance demonstrate that our approach has been shown to be much superior in clustering efficiency whereas the clustering quality are comparable to traditional methods.

Credit Risk Assessment Model of Commercial Banks Based on Fuzzy Neural Network

A commercial bank credit risk assessment model based on fuzzy neural network has been established using the credit assessment index system established for commercial banks. This network is a 6 layered structure with 4 factor inputs and one output measuring the credit risk of commercial banks. The fuzzy rule layer has the capability of making necessary adjustments in accordance with specific conditions of problems. The operation of this model is much better than the totally black-box operation of a neural system. A substantiation analysis has been made with 167 observations as sample data; training results indicate that the network prediction has less error.

Prediction of Passenger Flow on the Highway Based on the Least Square Suppoert Vector Machine

Abstract A support vector machine is a machine learning method based on the statistical learning theory and structural risk minimization. The support vector machine is a much better method than ever, because it may solve some actual problems in small samples, high dimension, nonlinear and local minima etc. The article utilizes the theory and method of support vector machine (SVM) regression and establishes the regressive model based on the least square support vector machine (LS-SVM). Through predicting passenger flow on Hangzhou highway in 2000–2008, the paper shows that the regressive model of LS-SVM has much higher accuracy and reliability of prediction, and therefore may effectively predict passenger flow on the highway.

Weight evaluation for features via constrained data-pairscan't-linkq

Facing the massive amount of data appearing on the web, automatic analysis tools have become essential for web users to discover valuable information online. Precise similarity measurement plays a decisive role in enabling analysis tools to acquire high-quality performances. Because different features contribute diversely to similarity calculation, it is necessary to utilize weight to measure features contribution and import it into similarity measurement. To accurately assign features weight, constrained data-pairs provided by users are usually imported into the weight evaluation procedure, whereas conventional plans all fail to consider two challenges: (a) asymmetrical distribution of constrained data-pairs, and (b) inconsistency contained by constrained data-pairs. If these two issues occur, conventional plans are incompetent at addressing them or are even unable to work. Thus, this paper proposes a novel constraint based weight evaluation to address these two issues. For the former, constrained data-pairs are partitioned into several equivalent classes, and distributing parameters are assigned to constrained data-pairs to balance their distributions. For the latter, constrained data-pairs are connected one after another, and belief values are thereby formed to indicate their probability of being inconsistent. Experimental results demonstrate that this type of evaluation is independent of any algorithm. With this evaluation, similarities can be calculated more accurately.

Application of Support Vector Regression Method in Stock Market Forecasting

Stock market forecasting has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the stock market forecasting problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with three-layer fully connected backpropagation neural networks (BNN). The experiment results show that SVM outperforms the BNN.

Games-Based Brand Competition Strategy

The purpose of the article is to provide an overview of brand management for brand strategy. Through making use of game theory, the article analyzes the methods of brand competition. The article will put forward the train of brand competition strategy. Although the competition of market is fierce, company can gain the advantage in the competition only if the company has a good strategic.

Credit Risk Assessment Model of Commercial Banks Based on Support Vector Machines

Scope: Commercial banks, as the key of the nations economy and the center of financial credit, play a multiple irreplaceable role in the financial system. Credit risks threaten the economic system as a whole. Therefore, predicting bank financial credit risks is crucial to prevent and lessen the incoming negative effects on the economic system. Objective: This study aims to apply a credit risk assessment model based on support vector machines (SVMs) in a Chinese case, after analyzing the credit risk rules and building a credit risk system. After the modeling, it presents a comprehensive computational comparison of the classification performances of the techniques tested, including Back-Propagation Neural Network (BPN) and SVMs. Method: In this empirical study, we utilize statistical product and service solutions (SPSS) for the factor analysis on the financial data from the 157 companies and Matlab and Libsvm toolbox for the experimental analysis. Conclusion: We compare the assessment results of SVMs and BPN and get the indication that SVMs are very suitable for the credit risk assessment of commercial banks. Empirical results show that SVMs are effective and more advantageous than BPN. SVMs, with the features of simple classification hyperplane, good generalization ability, accurate goodness of fit, and strong robustness, have a better developing prospect although there are still some problems with them, such as the space mapping of the kernels, the optimizing scale, and so on. They are worthy of our further exploration and research.