CN104281580A - Evidence fusion information process based on hybrid DSm model - Google Patents
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Abstract
The invention discloses an evidence fusion information process based on a hybrid DSm model. The general evidence fusion information process based on the hybrid DSm model is established through the method, and the DSm theory information process can be divided into eight steps in the time sequence as shown in the figure 1. Classification is conducted by studying the information flow direction of the information process of evidence fusion and the main data and the auxiliary data of the information process. The evidence fusion information process and the input and output information of related nodes are analyzed in a detained mode, the steps of data input, data representation and data processing are included, the calculation amount is controlled on the premises that the DSm theory processing effect is not lowered, and it is ensured that the DSm theory is used efficiently and orderly. The evidence fusion information process is effectively used under the hybrid DSm model, a free DSm model and a Shafer model can be used by adding corresponding constraint conditions, and wide application value is achieved.
Description
Technical field
Invention creates a kind of evidence fusion information flow mixed under DSm model, may be used for, to the overall process of mixing DSm model evidence fusion, specifying interdependent node input/output information, realizing complete process control.
Background technology
Dezert-Smarandache theory (DSm is theoretical) is a kind of important Uncertain Reasoning Theory, it can either process traditional mutual exclusion and merge problem, can process again fuzzy in actual fused, uncertain, conflict etc. more complicated problem " Advances and Applications of DSmT for Information Fusion. " (Florentin Smarandache and Jean Dezert.American Research Press, Rehoboth, USA, Vol.1, Vol.2and Vol.3,2004/2006/2009).Not only there is the Shafer model of Dempster-Shafer theory (DS is theoretical) in DSm theory, also add free DSm model and mixing DSm model.Mixing DSm model extension Shafer model deficiency notationally, and there is not free DSm model and cause owing to segmenting a lot of part the defect not having physical significance.Mixing DSm model can have fuzzy concept and discrete hypothesis simultaneously, namely adds constraint condition, makes the theoretical dynamic fusion problem that can process under mixing DSm model in reality of DSm.Therefore, mixing DSm model is the universal model that can be applicable to various condition, and other two models can be regarded as the special case of mixing DSm model.But existing research is mainly for the single problem occurred in DSm Theory and applications, as the improvement of rule of combination, the reduction etc. of calculated amount.The research of single problem can solve some particular problem, but cannot consider the effect of research from the angle of entirety, so the input/output information of macroscopic information flow process and interdependent node represents, processes is problem demanding prompt solution.Dezert and Smarandache, first proposed three layers of DSm fusion framework, based on free DSm model, retrains and is fused to the second layer, finally completing process in decision-making level.Scholar is had to do simple description from the information flow of angle to DSm theory of recurrence, the information flow provided can only the flow direction of expressing information, do not have information to represent, analysis " the recurrence Target recognition fusion method based on Dezert-Smarandache the theory " (Hu Lifang of the basic problem such as relation between information, Guan Xin, He You, control theory and application, 2012,29 (1): 79-84.); The broad sense also having researchist to propose evidence theory merges framework, evidential reasoning structure is divided into four levels, but do not provide detailed flow process " broad sense evidential reasoning fusion structure. " (the gold zone Chinese, Li Peng, Wang Min. broad sense evidential reasoning fusion structure, intelligent system journal, 2010,5 (6): 487-491.); Li Xinde respectively from two angles give the theoretical approximate reasoning fusion method of DSm flow process " a kind of fast hierarchical passs rank DSmT approximate reasoning fusion method (A); " (Li Xinde, Jean Dezert, the gold zone Chinese, Meng Zheng great, Wu Xuejian, electronic letters, vol, 2010,38 (11): 2566-2572.).To sum up, the existing research of information flow is in scattered state substantially, is necessary to carry out intensive research to key issues such as the data representation in information flow and flow process, process, displays.
Summary of the invention
Object of the present invention proposes for the weak point in above-mentioned background technology just.The present invention constructs the evidence fusion information flow of mixing DSm model.First the evidence fusion main-process stream of mixing DSm model is established, and DSm theoretical information flow process can be divided into eight steps sequential: step 1 is input data, step 2 is rules selections, step 3 is Selecting parameter, step 4 is data prediction, and step 5 is data fusion, and step 6 is result decision-makings, step 7 is result of decision process, and step 8 is conclusion output displays.Total information flow process as shown in Figure 1.Next analyzes detailed process and the interdependent node input/output information of committed step.By setting up evidence fusion information flow, can carry out Data Fusion for the use DSm theory of engineering technical personnel provides foundation.Engineering technical personnel can according to specific tasks and actual requirement, and combining information flow process is reasonably selected rule of combination, decision rule, is optimized each several part, makes effect reach best.
In order to realize above-mentioned goal of the invention, the invention provides a set of evidence fusion information flow based on mixing DSm model.
The first step: be described the specific object of target or event, contains four kinds of data required for data processing, decision-making;
Second step: select at rule of combination character database screening rule;
3rd step: user determines fusion mode, synthetic method, decision-making technique and display mode according to the actual requirements, fusion mode, decision-making technique and display mode are determined by user, and combined method is determined by second step;
4th step: complete the conversion of data between different method for expressing, obtain simplifying calculation code, makes data be convenient to calculate;
5th step: carry out fusion treatment by simplifying the evidence that calculation code represents, obtains rationally effective fusion results.
6th step: carry out result decision-making according to the decision rule selected in Selecting parameter again, must simplify the result of decision that calculation code represents;
7th step: complete the conversion of data between different method for expressing, obtain the result of decision that character code represents, makes result be convenient to understand;
8th step: conclusion output display.
The invention has the advantages that:
(1) input/output information of interdependent node under mixing DSm model is represented, processes and carried out comprehensive analysis, establish complete information flow, theoretical foundation is provided to the application of DSm theory;
(2) based on information flow to proposing new information expressing method, utilize rational coding form to effectively reduce calculated amount.
Accompanying drawing explanation
Fig. 1 is the evidence fusion information flow chart based on mixing DSm model, this flow process is divided into eight steps: step 1 is input data, step 2 is rules selections, step 3 is Selecting parameter, step 4 is data prediction, and step 5 is data fusion, and step 6 is result decision-makings, step 7 is result of decision process, and step 8 is conclusion output displays.
Fig. 2 is the Vean diagram of proposition and two kinds of codings (n=3) of correspondence thereof.The feature of Smarandache coding and calculation code is illustrated in Vean diagram.
Fig. 3 is data prediction process flow diagram.Data prediction is from the character code of input evidence, to being converted to presentation code, then is converted to calculation code, is finally converted to simplifies calculation code when considering constraint condition.
Fig. 4 is data fusion process flow diagram (for PCR6 rule).Data fusion carries out fusion treatment by simplifying the evidence that calculation code represents, obtains rationally effective fusion results.
Fig. 5 is result decision flow diagram (for DSmP).Result decision-making is carried out reprocessing to synthesis result and is obtained the result of decision.
Fig. 6 is applicating example information flow.
Embodiment
Be described in detail below in conjunction with the technical scheme of accompanying drawing to invention:
DSm theoretical information flow process can be divided into three parts from the function completed: information prepares, information represents and information processing.Information prepares to comprise step 1, step 2 and step 3, mainly carries out clear and definite description to the problem of process, determines the various parameters related in disposal route and process; Information represents and comprises step 4, step 7 and step 8, is mainly the result of decision conversion after the conversion of the data before information processing and information processing and final output display; Information processing comprises step 5 and step 6, mainly comprises the decision-making after the fusion to evidence, fusion.
1. information prepares
The data that DSm theoretical information flow process relates to are numerous, not only have and need evidence to be processed and attribute thereof, also have the parameter such as synthetic method, decision-making technique.The determination of these data and parameter is called that information prepares.
1.1 input data
The input data of DSm theory need to be described the specific object of target or event, contain four kinds of data required for data processing, decision-making.One is framework of identification structure, and framework of identification structure is used for characterizing the classification under different model and various boundary conditions belonging to evidence.Framework of identification structure is the basis of various method for expressing.Two is evidences, comprises the burnt unit of evidence and corresponding basic brief inference thereof.Evidence needs core data to be processed.Three is constraint condition, and constraint condition is that the mathematicization of process data internal relation represents.Constraint condition can be divided into two classes: a class is the repellency constraint between proposition; Another kind of be proposition compatible but learn in practice according to priori and non-existent non-existence constraint.Four is can the burnt unit of decision-making, can the burnt unit of decision-making according to actual conditions which burnt unit when decision-making as the result of decision, and will can not can will get rid of as the element of the result of decision completely, the rationality of guarantee decision-making.
1.2 Selecting parameter
Input data are determined by objective reality, and Selecting parameter is more it is considered that the subjective factor of slip-stick artist or user.Selecting parameter is that user determines fusion mode, synthetic method, decision-making technique and display mode according to the actual requirements, and fusion mode, decision-making technique and display mode are determined by user, and combined method selects module to determine by rule of combination.
Rule of combination is the core that DSm theoretical information merges, and the selection of rule directly determines syncretizing effect, is selected to analyze as a module by rule of combination herein.Rules selection screens at rule of combination character database.Wherein, building database can be evaluated with character three major types to rule of combination mainly through synthesising property, sequential character and engineering.
2. information represents
Prepared from information, in DSm theory, information category is various.Information represents treatment effeciency and the identification whether precise and high efficiency is related to data.Information represents can be divided into data prediction and result of decision process, and two parts are all to complete the conversion of data between different method for expressing, and data prediction will make data be convenient to calculate, and result of decision process makes result be convenient to understand.Different pieces of information 26S Proteasome Structure and Function feature determines the difference of method for expressing.
2.1 information expressing method
Under DSm theoretical frame, it be the information expressing method of different pieces of information structure content with funtion, clear expression, efficient process.The information expressing method of DSm theory comprises four kinds: character code, presentation code, Smarandache coding and calculation code.
The original method for expressing of character code and the burnt unit of evidence, as " θ
1∩ θ
2".Character code is easy to read, and what be mainly used in user is mutual.Presentation code is character code all by numeral, as " θ
1∩ θ
2" be expressed as [1-1 2], wherein-1 conventional letter ∩, other symbols also have corresponding coded representation.Presentation code is the bridge of character code and other code conversion.Smarandache coding method by Smarandache at document " Partial ordering on hyper-power sets. " (Dezert J, Smarandache F.Advances and Applications of DSmT for Information Fusion (Collected Works Vol.I) .American Research Press, Rehoboth, propose 2004:49-60.), Smarandache coding method represents the separate section of Vean diagram with enumerating.When Fig. 2 (a) illustrates n=3, the Vean diagram of proposition and the Smarandache coding of correspondence thereof.Although easy understand is compared in Smarandache coding method, notationally have superiority, need a large amount of logical add, logical multiply computing when calculating, complexity is higher.For solving Smarandache coding problem in the calculation, Arnaud Martin is at document " Implementing general belief function framework with a practical codification for low complexity. " (Martin A, Advances and Applications of DSmT for Information Fusion (Collected Works Vol.III) .American Research Press, Rehoboth, 2009:3-74.) in propose calculation code.Calculation code is also represent the separate section of Vean diagram, but its corresponding numeral is [1: 2
n-1] integer in.Calculation code significantly can reduce calculated amount.When Fig. 2 (b) illustrates n=3, the Vean diagram of proposition and the calculation code of correspondence thereof.
2.2 information represent flow process
Different method for expressing needs representing its effect of respective nodes competence exertion of flow process.Information represents that flow process is divided into data prediction and result of decision process.Data prediction is from the character code of input evidence, to being converted to presentation code, then is converted to calculation code, is finally converted to simplifies calculation code when considering constraint condition.And result of decision process is from simplifying the result of decision that calculation code represents, being finally converted to the character code being easy to show, is the inverse operation of data prediction.The flow process of data prediction as shown in Figure 3.
Data prediction, first according to framework of identification structure, generates complete computation coding; Secondly, according to restrictive condition and complete computation coding framework, the element that do not exist constraint condition limited is removed, and obtain simplifying calculation code, wherein, constraint condition is also represent with character code, be converted to presentation code; Finally, first the character code of original evidence being obtained presentation code by process, it being converted to the array architecture of simplifying calculation code and representing original evidence according to obtaining simplifying calculation code again.By above step, evidence is converted to calculation code by character code, facilitates computing machine to synthesize data, the process such as decision-making, evaluation.
3. information processing
Information processing determines the syncretizing effect of information, is the core of information flow.Information processing is divided into two parts: data fusion and result decision-making.Data fusion carries out fusion treatment by simplifying the evidence that calculation code represents, obtains rationally effective fusion results.Carry out result decision-making according to the decision rule selected in Selecting parameter again, obtain the result of decision that calculation code represents.
3.1 data fusion
Data fusion needs three partial datas, and one is selected composition rule, and two is simplify calculation code, and three is evidences.The core of data fusion is the process to evidence.For PCR6 rule of combination " A new generalization of the proportional conflict redistribution rule stable in terms of decision. " (Martin A, Osswald C.Advances and Applications of DSmT for Information Fusion (Collected Works Vol.II) .American Research Press, Rehoboth, 2006:69-88.), Data Fusion is as shown in Figure 4.
Data Fusion, first according to the result of rules selection part, is defined as PCR6 rule; Calculate at this and intersect evidence and empty set position, reliability corresponding for empty set is re-started distribution according to the allocation scheme of PCR6, by the fractional additional of distribution in crossing evidence, obtain code reassignment evidence; Finally duplicate removal process is carried out to code reassignment evidence, obtain synthesizing result.
3.2 result decision-makings
Result decision-making is carried out reprocessing to synthesis result and is obtained the result of decision.Result decision-making is not only relevant with synthesis result, also relate to decision rule select, can decision element and simplify calculation code as auxiliary data.With DSmP " A new probabilistic transformation of belief mass assignment. " (Dezert J, Smarandache F.Information Fusion, 11
thinternational Conference on, Cologne, Germany, 2008:1410-1418.) method is example, result decision flow diagram is as shown in Figure 5.
Result decision-making first according to can decision element code, obtain concrete can decision element, then according to can decision element to synthesis result with simplify Accounting Legend Code and process, generate based on can the decision-making evidence of decision element and decision-making calculation code; Second step is to decision-making rule judgment; Finally by selected decision-making technique, decision-making is carried out to decision-making evidence, obtain the result of decision.
4 applicating examples
In order to the validity of authorization information flow process, by an example, whole information flow is described.If model is mixing DSm model, framework of identification is that Θ={ A, B, C}, dimension is 3.As shown in Figure 6, the numerical value of the primary variables related in information flow in information flow is as shown in table 1 for complete information flow.Wherein solid black lines represents the path of evidence at information flow.
Table 1 applicating example information flow primary variables value
Claims (4)
1., based on the evidence fusion flow process of mixing DSm model, be specially:
Based on the evidence fusion flow process of mixing DSm model, eight steps can be divided into: step 1 is input data sequential, step 2 is rules selections, step 3 is Selecting parameter, step 4 is data prediction, and step 5 is data fusion, and step 6 is result decision-makings, step 7 is result of decision process, and step 8 is conclusion output displays.
2., based on the information preparation module of the evidence fusion flow process of mixing DSm model, be specially:
1) data are inputted.The input data of DSm theory need to be described the specific object of target or event, contain four kinds of data required for data processing, decision-making.Framework of identification structure, evidence, constraint condition and can the burnt unit of decision-making.
2) Selecting parameter.Input data are determined by objective reality, and Selecting parameter is more it is considered that the subjective factor of slip-stick artist or user.Selecting parameter is that user determines fusion mode, synthetic method, decision-making technique and display mode according to the actual requirements, and fusion mode, decision-making technique and display mode are determined by user, and combined method is selected by rule of combination, determine.
3., based on the information expressing method of the evidence fusion flow process of mixing DSm model, be specially:
Under DSm theoretical frame, it be the information expressing method of different pieces of information structure content with funtion, clear expression, efficient process.The information expressing method of DSm theory comprises four kinds: character code, presentation code, Smarandache coding and calculation code.The original method for expressing of the burnt unit of character code and evidence, character code is easy to read, and what be mainly used in user is mutual.Presentation code is character code all by numeral, and presentation code is the bridge of character code and other code conversion.Smarandache coding method represents the separate section of Vean diagram with enumerating, Smarandache coding method notationally has superiority, but needs a large amount of logical add, logical multiply computing when calculating, and complexity is higher.Calculation code is also represent the separate section of Vean diagram, but its corresponding numeral is [1:2
n-1] integer in.Calculation code significantly can reduce calculated amount.
4. the information based on the evidence fusion flow process of mixing DSm model represents flow process, is specially:
Information represents that flow process is divided into data prediction and result of decision process.Data prediction is from the character code of input evidence, to being converted to presentation code, then is converted to calculation code, is finally converted to simplifies calculation code when considering constraint condition.And result of decision process is from simplifying the result of decision that calculation code represents, being finally converted to the character code being easy to show, is the inverse operation of data prediction;
Data prediction, first according to framework of identification structure, generates complete computation coding; Secondly, according to restrictive condition and complete computation coding framework, the element that do not exist constraint condition limited is removed, and obtain simplifying calculation code, wherein, constraint condition is also represent with character code, be converted to presentation code; Finally, first the character code of original evidence is obtained presentation code by process, obtain simplifying calculation code according to oneself and it is converted to the array architecture of simplifying calculation code and representing original evidence again.By above step, evidence is converted to calculation code by character code, facilitates computing machine to synthesize data, the process such as decision-making, evaluation.
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Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7337086B2 (en) * | 2005-10-18 | 2008-02-26 | Honeywell International, Inc. | System and method for combining diagnostic evidences for turbine engine fault detection |
CN101639864A (en) * | 2009-08-18 | 2010-02-03 | 东南大学 | Multi-level hierarchical DSmT rapid approximate reasoning fusion method |
CN102930281A (en) * | 2011-08-11 | 2013-02-13 | 金宏斌 | Dempster-Shafer (DS) theory and Dezert-Smarandache (DSm) theory-based interactive self-adaptive target identification method |
-
2013
- 2013-07-02 CN CN201310273792.5A patent/CN104281580A/en active Pending
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7337086B2 (en) * | 2005-10-18 | 2008-02-26 | Honeywell International, Inc. | System and method for combining diagnostic evidences for turbine engine fault detection |
CN101639864A (en) * | 2009-08-18 | 2010-02-03 | 东南大学 | Multi-level hierarchical DSmT rapid approximate reasoning fusion method |
CN102930281A (en) * | 2011-08-11 | 2013-02-13 | 金宏斌 | Dempster-Shafer (DS) theory and Dezert-Smarandache (DSm) theory-based interactive self-adaptive target identification method |
Non-Patent Citations (2)
Title |
---|
李鸿飞等: "基于混合DSm模型的组合规则评价体系", 《系统工程与电子技术》 * |
黄心汉等: "《DSmT理论及其在信息融合中的应用》", 30 June 2011, 国防工业出版社 * |
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