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In this paper, rough set theory is applied to evaluate the stability of rock slope through the analysis to slope failure examples. Genetic algorithms (GA) is incorporated to reduce attributes and the corresponding evaluation knowledge is used to build an expert system on rock slope stability evaluation. The system adopts inexact reasoning and default reasoning methods to deal with incomplete match...
Attributes reduction is one major problems in rough set theory. A method of attributes reduction based on scan vector is proposed in this paper. Firstly, define a new conception of discernible vector by which we can transform the information table into discernible vector set. Secondly, a plus rule for the discernible vector based on its good structure is defined, and consequently we can obtain a scan...
In this paper, we propose a new efficient data reduction algorithm through combining lattice with rough set. On the basis of lattice learning, the algorithm applies the concept of attribute reduction in the theory of rough sets and calculates the importance degree of attributes automatically by a density based approach. Under acceptable classification precision and complexity, it reduces row and column...
Rough neural network has the advantage of reducing training time and optimizing network topology architecture. According to such attribute, a novel face recognition method is presented based on multi-features using fusion of multiple rough neural network classifiers. First, three different feature domains are used for extracting features from input images, including IO (the interest operator), PCA...
A vision-based inspection method based on rough set theory, fuzzy set and neural network algorithm is presented. The rough set method is proposed to remove redundant features for its data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable form for input to a BP network classifier. The BP neural classifier is considered the most popular, effective...
This paper presents a mechanism called R_Apriori for learning rules from large datasets. The existing rough set based methods are not applicable for large data sets for its high time and space complexity. In this paper, large data sets are divided into several parts, in combination with Apriori algorithm, implicated rules are derived in liner relation to size of data set. At last, experiment result...
Incomplete information system is commonly encountered to emitter recognition problem in practical reconnaissance environment. The main reason is that reports with emitter parameter values are unknown. In order to solve emitter recognition problems in incomplete information system, rough set theory is introduced. A new emitter recognition method to incomplete information system based on rough set theory...
The theory of rough set is of great advantages in dealing with the ambiguity in information systems. Combining it with abstract algebra is a way generalizing it. Some papers proposed the concepts of rough groups and rough rings in approximate spaces, and investigated their properties. In this paper, firstly we introduce the concepts of rough module, rough sub-module, rough quotient module in approximate...
For highly mine with mash gas, a fault location method of rough set theory for mine ventilator is presented, where fault attributes are divided into ten kinds, these decision tables of blocking partition are established; the rough set core of every decision table is got through reduction; the blocking tables with the dissimilarity fault kind combined to make a decision table; then the minimum decision...
In this paper, the method of conflict analysis based on rough set is applied to investigate the satisfactions of the optimized decision of the water resources allocation in the inland river basin of arid regions. Analyzing the support of the decision for the special districts (e.g., upriver, middle-river and downriver), respectively, in the inland river basin of arid regions, the feasibility of an...
Systematic risk that is presented by beta is the avoidless risk on the stock market. Beta is calculated by linear analysis between the daily prices of stocks and the security index of stock market. However, many studies have showed there are stronger relationships between beta and financial ratios. In this paper, a hybrid intelligent system is applied to recognize the clusters of beta with financial...
On the characteristics of multi-source, heterogenous structure, mass data of WebGIS as well as the complexity of the system, the paper proposes the design model of the manager-agents based on Web and multi-agent. Moreover, through analyzing the characteristics of WebGIS, the paper makes a further research on model of the manager-agents. It puts the emphasis on the decision-module of the manager-agent,...
In Pawlak's rough set system, approximation quality can be used to measure the classification capability of a condition attribute and can be used to define the significance of condition attributes. But the measure only gives us the determinate classification capability, and does not give us the uncertain classification capability. The information entropy is a mean value in terms of probability that...
Axiomatic characterization of rough approximation operators is one of the important aspects in the study of rough set theory. In axiomatic approach, various classes of rough approximation operators are characterized by different sets of axioms. Axioms of approximation operators guarantee the existence of certain types of binary relations producing the same operators. In this paper, the approximation...
In this paper, we present some new ideas for fuzzy reasoning from the viewpoint of fuzzy rough set. After introducing the existing research of fuzzy rough sets, we show that the well-known CRI method in fuzzy reasoning is just a special upper approximation operator in fuzzy rough set theory. We also develop some practical methods for fuzzy system by using lower approximation operators in fuzzy rough...
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