Attribute Reduction Based on Inclusion Degree for Incomplete and Fuzzy Decision Information Systems

The information systems with incomplete attribute values and fuzzy decisions commonly exist in many applications whose knowledge reduction is one of the most important practical significance. Model of incomplete and fuzzy decision information system is firstly constructed. On the basis of the notion of inclusion degree between fuzzy sets, the attribute reduction for incomplete and fuzzy decision information system, which ensures invariable inclusion degree between every tolerance class and fuzzy decision set, is raised. To reduce the complexity of finding attribute reduction, discernibility sets, discernibifity matrixes and the minimal disjunctive normal form of discernibility sets for incomplete and fuzzy decision information system are introduced. Finally, the algorithm and an example are given, and the solution of the example is proved that the approach to attribute reduction based on inclusion degree is valid.     

Diagnosis and Localization of Pipeline Leak Based on Fuzzy Decision-making Method

A leak detection plays a key role in the overall integrity monitoring for a oil pipeline system.A fuzzy decision-making approach to pipeline leak localization is proposed in this paper. The two main methods,pressure gradient localization and negative pressure wave localization,are combined with fuzzy logical decision-making method to form a novel fault diagnosis scheme.The combination scheme can improve the precision of localization.An application example,14km long oil pipeline leak detection and localization,is illustrated.This method is compared with others through practical experiments and its validity is confirmed by the results.     

Text Classification Based on Domain Ontology

With the quick increase of information and knowledge, automatically classifying text documents is becoming a hotspot of knowledge management. A critical capability of knowledge management systems is to classify the text documents into different categories, which are meaningful to users. In this paper, a text topic classification model based on domain ontology by using Vector Space Model is proposed. Eigenvectors as the input to the vector space model are constructed by utilizing concepts and hierarchical structure of ontology, which also provides the domain knowledge. However, a limited vocabulary problem is encountered while mapping keywords to their corresponding ontology concepts. A synonymy lexicon is utilized to extend the ontology and compress the eigenvector. The problem that eigenvectors are too large and complex to be calculated in traditional methods can be solved. At last, combing the concept＇s supporting, a top-down method according to the ontology structure is used to complete topic classification. An experimental system is implemented and the model is applied to this practical system. Test results show that this model is feasible.     

Rough Sets Model Based on Random Fuzzy Sets and Fuzzy Logic Operators

As knowledge block in knowledge base is fuzzy and obtained randomly, we propose a random fuzzy rough set model based on random fuzzy sets and fuzzy logic operators. We give some properties of the random fuzzy rough set. We investigate the relationship between fuzzy measures defined by lower approximation and upper approximation of fuzzy set and fuzzy probability measures.     

Web Classification Based on Latent Semantic Indexing

A new web document automatic classification algorithm based on Latent Semantic Indexing （LSIWAC）, is proposed in this paper. LSMAC uses the LSI based on Singular Value Decomposition （SVD） to compress the document vector space to lower dimensional space. Using the optimal clustering, LSIWAC can cluster part of web documents Then, LSIWAC uses the optimal discriminate transform to get feature vector from every clustering＇s discriminate features. Finally, it uses the conception classification algorithm to classify the rest documents. LSIWAC solves the high dimension problem and improves the precision of web classification.     

Structured Development Environment Based on the Object-Oriented Concepts

The object-oriented software development is a kind of promising software methodology and leading to a wholly new way for solving problems.In the research on the rapid construction of Structured Development Environment(SDE)that supports detailed design and coding in software development,a generator that can generate the SE has been applied as a metatool.The kernel of SDE is a syntax-directed editor based on the object-oriented concepts.The key issue in the design of SDE is how to represent the elements of target language with the class concept and a program internally.In this paper,the key concepts and design of the SDE and its generator as well as the implementation of a prototype are to be discussed.     

Knowledge Based Pipeline Network Classification and Recognition Method of Maps

Map recognition is an essential data input means of Geographic Information System (GIS). How to solve the problems in the procedure, such as recognition of maps with crisscross pipeline networks, classification of buildings and roads, and processing of connected text, is a critical step for GIS keeping high-speed development. In this paper, a new recognition method of pipeline maps is presented, and some common patterns of pipeline connection and component labels are established. Through pattern matching, pipelines and component labels are recognized and peeled off from maps. After this approach, maps simply consist of buildings and roads, which are recognized and classified with fuzzy classification method. In addition, the Double Sides Scan (DSS) technique is also described, through which the effect of connected text can be eliminated.     

Structured Development Environment Based on the Object-Oriented Concepts

The object oriented software development is a kind of promising software methodology and leading to awholly new way for solving problems. In the research on the rapid construction of Structured Development Envi-ronment (SDE)that supports detailed design and coding in software development, a generator that can gener-ate the SDE has been applied as a metatool. The kernel of SDE is a syntax-directed editor based on the ob-ject-oriented concepts. The key issue in the design of SDE is how to represent the elements of target languagewith the class concept and a program internally. In this paper, the key concepts and design of the SDE and itsgenerator as well as the implementation of a prototype are to be discussed.     

A Fuzzy Approach to Classification of Text Documents

This paper discusses the classification problems of text documents. Based on the concept of the proximity degree, the set of words is partitioned into some equivalence classes.Particularly, the concepts of the semantic field and association degree are given in this paper.Based on the above concepts, this paper presents a fuzzy classification approach for document categorization. Furthermore, applying the concept of the entropy of information, the approaches to select key words from the set of words covering the classification of documents and to construct the hierarchical structure of key words are obtained.     

Inclusion Isotonicity of Convex–Concave Extensions for Polynomials Based on Bernstein Expansion

In this paper the expansion of a polynomial into Bernstein polynomials over an interval I is considered. The convex hull of the control points associated with the coefficients of this expansion encloses the graph of the polynomial over I. By a simple proof it is shown that this convex hull is inclusion isotonic, i.e. if one shrinks I then the convex hull of the control points on the smaller interval is contained in the convex hull of the control points on I. From this property it follows that the so-called Bernstein form is inclusion isotone, which was shown by a longish proof in 1995 in this journal by Hong and Stahl. Inclusion isotonicity also holds for multivariate polynomials on boxes. Examples are presented which document that two simpler enclosures based on only a few control points are in general not inclusion isotonic. Received September 12, 2002; revised February 5, 2003 Published online: April 7, 2003     

Type-2 Fuzzy Sets and Newton’s Fuzzy Potential in an Algorithm of Classification Objects of a Conceptual Space

Journal of Logic, Language and Information - This paper deals with Gärdenfors’ theory of conceptual spaces. Let $${mathcal {S}}$$ be a conceptual space consisting of 2-type fuzzy sets...     

The Detection and Orientation Method to DRDoS Attack Based on Fuzzy Association Rules

DDoS （Distributed Denial of Service） attack is being the most extensive danger and difficulty to defense. A new kind of DDoS attack named DRDoS （Distributed Reflector Denial of Service） appears in recent years, which is more dangerous than DDoS attack because it is in stronger disguise. In this paper, the principle of DRDoS attack is studied and the network traffic is analyzed by fuzzy association rules with path restricted when DRDoS attack happens. And the association rules about spoofed network traffic and attack network traffic are mined. Also the DRDoS Attack Defensive Architecture based on Multi-Agent （D2AMA） is set up in order to realize the detection, orientation and defensive function. D2AMA is validated by NS-2 platform. It can detect and orient the attack source in a short time, realize scrvvning the attack source and stop transmitting attack traffic. The experimental result proves that D2AMA can find out the attack, orient the attack source and hold out the attack effectively.     

The Multi-agent Learning Model Based on Dynamic Fuzzy Logic

Machine Learning is one of the key problems of Artificial Intelligence, and the agent learning has become an important branch of machine learning. One of the main characters of intelligent agent is that it can adapt to the unknown environment. The ability to learn is the key property of agent. Because the learning act of agent is dynamic and fuzzy, this paper uses the conception of Dynamic Fuzzy Logic （DFL）tl]. Based on DFL, this paper first presents two single-agent learning algorithms, namely, single-agent leaning algorithm based on DFL with immediate reward and single-agent learning algorithm based on DFL with mediate reward. Then the paper gives a multi-agent learning model based on DFL, namely a multi-agent learning model planned on a whole. Furthermore, this paper validates that the model is useful by an example.     

Adaptive Fuzzy PID Control Method Based on Identification Structure

I. INTRODUCTION There are more or less non-linear systems in industry process and sometimes it is hard or hardly to find an accurate model, so the common PID control cannot realize the accurate control on these conditions. Fuzzy control does not need to find an accurate mathematic model, so it is applied more and more widely in industry process and many other realms [1,2]. This paper use identification structure at first to establish the mathematics model, and then use logical reasoning …     

Lattice Boltzmann Model Based on the Rebuilding-Divergency Method for the Laplace Equation and the Poisson Equation

In this paper, a new lattice Boltzmann model based on the rebuilding-divergency method for the Poisson equation is proposed. In order to translate the Poisson equation into a conservation law equation, the source term and diffusion term are changed into divergence forms. By using the Chapman-Enskog expansion and the multi-scale time expansion, a series of partial differential equations in different time scales and several higher-order moments of equilibrium distribution functions are obtained. Thus, by rebuilding the divergence of the source and diffusion terms, the Laplace equation and the Poisson equation with the second accuracy of the truncation errors are recovered. In the numerical examples, we compare the numerical results of this scheme with those obtained by other classical method for the Green-Taylor vortex flow, numerical results agree well with the classical ones.     

Improvement of Control Strategy for Variable-frequency Icebox Based on Variable Universe Fuzzy Control

In this paper, a new approach on variable-frequency icebox control system is presented. We establish a new-type function of contraction-expansion factor to optimize the controller of variable-frequency icebox, and the system that has this control module can conquer the disadvantages in conventional variable-frequency icebox and has high robustness. It is applied successfully in decreasing undulation and reducing the cost of icebox. It is applied successfully by Matlable.     

Mathematical Modeling of Fractional Differential Filtration Dynamics Based on Models with Hilfer–Prabhakar Derivative

We construct a generalized mathematical model to describe the fractional differential dynamics of filtration processes in fractured porous media, based on the use of the concept of Hilfer–Prabhakar fractional derivative. Within the framework of this model, we obtain a number of closed form solutions to boundary-value problems of filtration theory for modeling the dynamics of pressures at launch of wells in case of plane-radial filtration, as well as by activity of galleries under plane-parallel filtration.     

Evolving Fuzzy Min–Max Neural Network Based Decision Trees for Data Stream Classification

Learning from data streams is a challenging task which demands a learning algorithm with several high quality features. In addition to space complexity and speed requirements needed for processing the huge volume of data which arrives at high speed, the learning algorithm must have a good balance between stability and plasticity. This paper presents a new approach to induce incremental decision trees on streaming data. In this approach, the internal nodes contain trainable split tests. In contrast with traditional decision trees in which a single attribute is selected as the split test, each internal node of the proposed approach contains a trainable function based on multiple attributes, which not only provides the flexibility needed in the stream context, but also improves stability. Based on this approach, we propose evolving fuzzy min–max decision tree (EFMMDT) learning algorithm in which each internal node of the decision tree contains an evolving fuzzy min–max neural network. EFMMDT splits the instance space non-linearly based on multiple attributes which results in much smaller and shallower decision trees. The extensive experiments reveal that the proposed algorithm achieves much better precision in comparison with the state-of-the-art decision tree learning algorithms on the benchmark data streams, especially in the presence of concept drift.