AU2021104642A4 - Evidence reasoning method and apparatus based on information entropy weighted allocation - Google Patents
Evidence reasoning method and apparatus based on information entropy weighted allocation Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06N5/04—Inference or reasoning models
- G06N5/045—Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
Abstract
Provided are an evidence reasoning method and apparatus based on information
entropy weighted allocation, an electronic device and a storage medium. The evidence
reasoning method comprises the steps: determining corresponding information
entropies thereof according to a plurality of evidences, wherein the evidences are
obtained by a sensor network; conducting normalization on the information entropies
to obtain a weight of evidence; calculating a new evidence after weighted allocation;
and determining an evidence reasoning result by a preset evidence combination rule
according to the new evidence after weighted allocation. By modifying the evidence
combination rule in the embodiment, the evidence reasoning method based on
information entropy weighted allocation is provided, such that an evidence reasoning
effect under a highly conflicted condition is improved.
Drawings of Description
Determining corresponding information entropies thereof S20
according to a plurality of evidences, wherein the evidences are
obtained by a sensor network
Conducting normalization on the information entropies to obtain S40:
a weight of evidence
Calculating a new evidence after weighted allocation M
Determining an evidence reasoning result by a preset evidence s
combination rule according to the new evidence after weighted
FIG. 1
Evidence reasoning apparatus
Information entropy acquisition
module20
Weight-of-evidence calculating
module 40
First calculating module 60
Second calculating module 80
Fig. 2
Description
Drawings of Description
Determining corresponding information entropies thereof S20 according to a plurality of evidences, wherein the evidences are obtained by a sensor network
Conducting normalization on the information entropies to obtain S40: a weight of evidence
Calculating a new evidence after weighted allocation M
Determining an evidence reasoning result by a preset evidence s combination rule according to the new evidence after weighted
FIG. 1
Evidence reasoning apparatus
Information entropy acquisition module20
Weight-of-evidence calculating module 40
First calculating module 60
Second calculating module 80
Fig. 2
TECHNICAL FIELD The present invention relates to the technical field of information infusion, in
particular to an evidence reasoning method and apparatus based on information
entropy weighted allocation, an electronic device and a storage medium.
In the field of information fusion of a plurality of sensors, information is of
uncertainty to a certain extent, and it is necessary to process these uncertain
information by a fusion system so as to complete target recognition and decision
making analysis. At present, an achievement for solving uncertain problem reasoning
primarily includes Bayesian reasoning, Bayesian network, fuzzy logic reasoning,
rough set theory, DS evidence theory and the like. Featuring in representation,
measurement, fusion and the like on uncertain problem, the DS evidence theory is
widely applied to the field of uncertainty reasoning and judgment such as target
recognition, decision-making analysis and information fusion. A Dempster synthetic
rule is a core of the DS evidence theory and the system can recognize the target
exactly by means of the synthetic rule under a non-critical conflict condition. It is
found that when evidences conflict severely, the synthesized result is of paradox.
Although the evidence theory is widely applied to modem engineering, it will
obtain a result which is not in accordance with recognition of a factor or a person
when high conflict is processed as it has limitation, for example, the theory is only
suitable for a condition without conflict among the evidences or low conflict exists
among the evidences. Thus, by modifying the evidence combination rule, the
evidence reasoning method based on information entropy weighted allocation is
provided, such that an evidence reasoning effect under a highly conflicted condition is
improved.
It is thereof an object of the present invention to provide an evidence reasoning
method based on information entropy weighted allocation to solve the problem that it
will obtain a result which is not in accordance with recognition of a factor or a person
when high conflict is processed.
In the first aspect, the embodiment of the present invention provides an evidence
reasoning method based on information entropy weighted allocation. The evidence
reasoning method includes:
determining corresponding information entropies thereof according to a plurality
of evidences, wherein the evidences are obtained by a sensor network;
conducting normalization on the information entropies to obtain a weight of
evidence;
calculating a new evidence after weighted allocation; and
determining an evidence reasoning result by a preset evidence combination rule
according to the new evidence after weighted allocation.
Further, determining corresponding information entropies thereof according to a
plurality of evidences includes:
taking the information entropies as a weight for measuring the evidence, Si
represents the information entropy of the evidence i:
S! =m(A log 2rm(A0)
wherein AC, t represents a type number of evidence judging results, m(A)
represents a degree of trust of the evidence to an event A, n evidences under a
recognition frame®={01,02,...,0N) are respectively Ei, E 2 ... , E., basic probability
assignment functions corresponding thereto are respectively mi, M2..., mn, focal
elements are respectively Ai, wherein i is equal to 1,2,...,N, 0 represents a null set,
and the evidence combination rule is:
~n(Ay={~fl~=A(A.)m() Ao
wherein
K is a conflict coefficient.
Further, conducting normalization on the information entropies to obtain a
weight of evidence includes:
conducting normalization on Si to obtain the weight Wi of evidence;
Wi=Si/max(Si),
wherein 1 is equal to 1, 2,..., N.
Further, calculating a new evidence after weighted allocation includes:
m(A)=10zi(A) AczeA~e
wherein ACO, n evidences under a recognition frame 0={01, 02,..., ON) are
respectively Ei, E2 ... , E., W 1 is the weight of evidence and 0 represents a null set.
Further, the preset evidence combination rule R is:
R=(1-W2)m'1(A)+(l-Wi)m'2(A)
Further, determining an evidence reasoning result by a preset evidence
combination rule according to the new evidence after weighted allocation includes:
conducting normalization on the determined m'(A) to obtain a final result m(A).
In the second aspect, the embodiment of the present invention provides an
evidence reasoning apparatus based on information entropy weighted allocation. The
evidence reasoning apparatus includes:
an information entropy acquisition module for determining corresponding
information entropies thereof according to a plurality of evidences, wherein the
evidences are obtained by a sensor network;
a weight-of-evidence calculating module for conducting normalization on the
information entropies to obtain a weight of evidence;
a first calculating module for calculating a new evidence after weighted
allocation; and
a second calculating module for determining an evidence reasoning result by a
preset evidence combination rule according to the new evidence after weighted
allocation.
In the third aspect, one or more embodiments of the present invention provide
electronic device, including:
a processor, and a memory for storing a processor executable instruction,
wherein the processor implements the method by operating the executable
instruction.
In the fourth aspect, one or more embodiments of the present invention provide a
computer readable medium, having computer readable instructions stored therein,
wherein the method is realized when the instructions are executed by the processor.
The present invention has the beneficial effects:
Provided are an evidence reasoning method and apparatus based on information
entropy weighted allocation, an electronic device and a storage medium. The
prediction method comprises the steps: determining corresponding information
entropies thereof according to a plurality of evidences, wherein the evidences are
obtained by a sensor network; conducting normalization on the information entropies
to obtain a weight of evidence; calculating a new evidence after weighted allocation;
and determining an evidence reasoning result by a preset evidence combination rule
according to the new evidence after weighted allocation. By modifying the evidence
combination rule, the evidence reasoning method based on information entropy
weighted allocation is provided, such that an evidence reasoning effect under a highly
conflicted condition is improved.
In order to describe the specific embodiments of the present invention or the
technical schemes in the prior art clearly, brief introduction is made below in
accordance with drawings needed to be used in the specific embodiments or the
description in the prior art. It is apparent that the drawings in the description below
are some embodiments of the present invention. Those skilled in the art further can
obtain other drawings in accordance with the drawings without creative efforts.
Fig.1 is a flow chart of an evidence reasoning method based on information
entropy weighted allocation shown in the embodiment of the present invention;
Fig. 2 is a structure diagram of an evidence reasoning apparatus based on
information entropy weighted allocation shown in the embodiment of the present
invention;
Fig. 3 is a structure diagram of an electronic device shown in one embodiment of
the present invention.
Clear and intact description on the technical schemes of the present invention is
made below in accordance with the embodiments, and it is apparent that the described
embodiments are a part of embodiments of the present invention rather than all
embodiments. On a basis of the embodiments in the present invention, all other
embodiments obtained by those skilled in the technical field without creative efforts
fall into the scope of protection of the present invention.
It is thereof an object of the present invention to provide an evidence reasoning
method based on information entropy weighted allocation to solve the problem that it
will obtain a result which is not in accordance with recognition of a factor or a person
when high conflict is processed.
Fig.1 is a flow chart of an evidence reasoning method based on information
entropy weighted allocation shown in the one embodiment of the present invention.
As shown in the Fig. 1, the method includes:
S20, corresponding information entropies thereof are determined according to a
plurality of evidences, wherein the evidences are obtained by a sensor network;
S40, normalization is conducted on the information entropies to obtain a weight
of evidence;
S60, a new evidence after weighted allocation is calculated; and
S80, an evidence reasoning result is determined by a preset evidence
combination rule according to the new evidence after weighted allocation.
The embodiment provides an evidence reasoning method based on information
entropy weighted allocation. The evidence reasoning method comprises the steps:
determining corresponding information entropies thereof according to a plurality of
evidences; further, conducting normalization on the information entropies to obtain a
weight of evidence; calculating a new evidence after weighted allocation; and
determining an evidence reasoning result by a preset evidence combination rule
according to the new evidence after weighted allocation. According to the
embodiment, by modifying the evidence combination rule in the embodiment, the
evidence reasoning method based on information entropy weighted allocation is
provided, such that an evidence reasoning effect under a highly conflicted condition is
improved.
Specifically, the information entropies are taken as a weight for measuring
evidences. The information entropies are measurements for depicting amount of
information or uncertainty. When probability is distributed uniformly, the smaller the
limitation is, the larger the uncertainty of the system is and the information entropies
are the maximum values. Similarly, by means of a characteristic that the information
entropies can represent uncertainty of the system, the information entropies are used
for measuring a measure of the evidence inducing conflict, thereby obtaining the
degree of trust of the evidence space to the evidence.
Specifically, determining corresponding information entropies thereof according
to a plurality of evidences includes:
Si represents the information entropy of the evidence i:
wherein ACO, t represents a type number of evidence judging results, m(A)
represents a degree of trust of the evidence to an event A, n evidences under a
recognition frame 0={01, 02,..., ON) are respectively E1 , E 2 ... , E., basic probability
assignment functions corresponding thereto are respectively mi, M2..., In, focal
elements are respectively Ai, wherein i is equal to 1,2,...,N, 0 represents a null set,
and the evidence combination rule is:
0 A=0
wherein
K is a conflict coefficient.
Further, conducting normalization on the information entropies to obtain a
weight of evidence includes:
conducting normalization on Si to obtain the weight Wi of evidence;
Wi=Si/max(Si),
wherein 1 is equal to 1, 2,..., N.
Specifically, calculating a new evidence after weighted allocation includes:
0 A=0 Mr(A)=Wp(A) A;;(,A *0 -W A=(
wherein A90, n evidences under a recognition frame ={01i, 02,..., ON are
respectively Ei, E 2 ... , En, W1 is the weight of evidence and 0 represents a null set.
Specifically, the preset evidence combination rule R is:
R=(1-W2)m'i(A)+(1-Wi)m'2(A)
Specifically, determining an evidence reasoning result by a preset evidence
combination rule according to the new evidence after weighted allocation includes:
normalization is conducted on the determined m'(A) to obtain a final result m(A).
Based on a same concept of the present invention, the embodiment of the present
invention further provides an evidence reasoning apparatus based on information
entropy weighted allocation. The evidence reasoning apparatus can be used for
realizing the method described in the embodiment and is as described in the
embodiment below. As the principle of the evidence reasoning apparatus based on
information entropy weighted allocation to solve the problem is similar to that of the
evidence reasoning method based on information entropy weighted allocation,
implementation of the evidence reasoning apparatus based on information entropy
weighted allocation can refer to implementation of the evidence reasoning method
based on information entropy weighted allocation, and repetition is not described
herein. The terms "unit" or "module" used below can realize combination of software
and/or hardware with predetermined functions. Although the system described in the
embodiment below is realized with excellent software, implementation of
combination of software and hardware may be probable and may be conceived.
Specifically, Fig. 2 is a structure diagram of an evidence reasoning apparatus
based on information entropy weighted allocation shown in the embodiment of the
present invention. As shown in the Fig. 2, the evidence reasoning apparatus includes:
an information entropy acquisition module 20 for determining corresponding
information entropies thereof according to a plurality of evidences, wherein the
evidences are obtained by a sensor network;
a weight-of-evidence calculating module 40 for conducting normalization on the
information entropies to obtain a weight of evidence;
a first calculating module 60 for calculating a new evidence after weighted
allocation; and
a second calculating module 80 for determining an evidence reasoning result by
a preset evidence combination rule according to the new evidence after weighted
allocation.
Specifically, determining corresponding information entropies thereof by the
information entropy acquisition module 20 according to a plurality of evidences
includes:
Si represents the information entropy of the evidence i:
wherein ACO, t represents a type number of evidence judging results, m(A)
represents a degree of trust of the evidence to an event A, n evidences under a
recognition frame O={Oi, 02,..., ON) are respectively E1, E 2 ... , En, basic probability
assignment functions corresponding thereto are respectively mi, m2..., m., focal
elements are respectively Ai, wherein i is equal to 1, 2,..., N, 0 represents a null set,
and the evidence combination rule is:
0 A=0
wherein
K is a conflict coefficient.
Specifically, conducting normalization on the information entropies by the
weight-of-evidence calculating module 40 to obtain a weight of evidence includes:
normalization is conducted on Si to obtain the weight Wi of evidence:
Wi=Si/max(Si),
wherein 1 is equal to 1, 2,...,N.
Specifically, calculating a new evidence after weighted allocation includes:
0 A=0 M (A) = Wpm(A) A g;,A *0 - W A=
( wherein AC, n evidences under a recognition frame O={01, 02,..., ONJ are
respectively E 1, E 2 ... , E., W1 is the weight of evidence and 0 represents a null set.
Specifically, the preset evidence combination rule R is:
R=(1-W2)m'l(A)+(1-W1)m'2(A)
Specifically, determining an evidence reasoning result by the second calculating
module 80 by a preset evidence combination rule according to the new evidence after
weighted allocation includes:
normalization is conducted on the determined m'(A) to obtain a final result m(A).
The embodiment provides the evidence reasoning apparatus based on
information entropy weighted allocation. The evidence reasoning apparatus is
structured such that the information entropy acquisition module 20 determines
corresponding information entropies thereof according to a plurality of evidences; the
weight-of-evidence calculating module 40 conducts normalization on the information
entropies to obtain a weight of evidence; the first calculating module 60 calculates a
new evidence after weighted allocation; and the second calculating module 80
determines an evidence reasoning result by a preset evidence combination rule
according to the new evidence after weighted allocation. By modifying the evidence
combination rule in the embodiment, the evidence reasoning method based on
information entropy weighted allocation is provided, such that an evidence reasoning
effect under a highly conflicted condition is improved.
The embodiment of the present invention further provides an electronic device.
Fig. 3 depicts the structural schematic diagram of the electronic device capable of
applying the embodiment of the present invention. As shown in the Fig. 3, the
electronic device of a computer includes a central processing unit (CPU) 301 capable
of executing various proper actions and processing in accordance with a program
stored in a read-only memory (ROM) 302 or a program loaded from a storage portion
308 to a random access memory (RAM) 303.In the RAM 303, various programs and
data oCPU30KROM302 needed by system operation are further stored, and the
RAM303 is connected each other via a bus 304.An input/output (1/0) interface 305 is
further connected to the bus 304.
The parts below are connected to the I/O interface 305, including an input
portion 306 of a keyboard, a mouse and the like; an output portion 307 including, for
example, a cathode-ray tube (CRT), a liquid crystal display (LCD), a loudspeaker and
the like; a storage portion 308 including a hard disc and the like; and a communication
portion 309 including a network interface card such as an LAN card and a modulator
demodulator. The communication portion 309 executes communication processing via
a network, for example, Internet. The driver 310 is further connected to the I/O
interface 305 as needed. A detachable medium 311, for example, a magnetic disc, an
optical disk, a magnetic optical disk, a semiconductor memory and the like is mounted
on the driver 310 as needed, such that it is convenient to mount a computer program
read thereon in the storage portion 308 as needed.
The flow chart and the diagram in the drawings depict system structures,
functions and operations which may be realized in accordance with systems, methods
and computer program products of various embodiments of the present invention. In
this regard, each block in the flow chart or the diagram can represent a portion of a
module, a program segment or a code, and the part of the module, the program
segment or the code includes one or more executable instructions which realize
regulated logic functions. It should be further noted that in some implementations as
substitutions, the functions annotated in the blocks can occur in sequence different
from that in the drawings. For example, two blocks represented by connection can be
substantially and parallelly executed actually and they sometimes may be executed in
an opposite sequence, which is dependent on the functions involved. It should be
further noted that combination of each block in the diagram and/or flow chart and the
block in the diagram and/or flow chart can be implemented by executing a dedicated
hardware-based system with regulated functions or operations or can be implemented
by combining dedicated hardware and computer instructions.
The present invention further provides a computer readable storage medium
which can be the computer readable storage medium included in the evidence
reasoning apparatus based on information entropy weighted allocation in the
embodiment or the computer readable storage medium which exists independently
and is not assembled in the electronic device. The computer readable storage medium
stores one or more programs, and the programs are used by one or more processors to
execute the evidence reasoning method based on information entropy weighted
allocation described in the present invention.
Finally, it should be noted that the above embodiments are only used to explain
the technical scheme of the present invention and shall not be construed as limitation.
Despite reference to the aforementioned embodiments to make a detailed description
for the present invention, it will be understood by those skilled in the art that they still
can modify the technical scheme recorded by the aforementioned embodiments or
make equivalent substitutions on part of technical features therein. Such modifications
or substitutions do not deviate the nature of the technical scheme from the spirit and
scope of the technical scheme embodied in the embodiments according to the present
invention.
Claims (9)
1. An evidence reasoning method based on information entropy weighted
allocation, characterized in that the evidence reasoning method comprises the steps:
determining corresponding information entropies thereof according to a plurality of
evidences, wherein the evidences are obtained by a sensor network; conducting
normalization on the information entropies to obtain a weight of evidence;
calculating a new evidence after weighted allocation; and
determining an evidence reasoning result by a preset evidence combination rule
according to the new evidence after weighted allocation.
2. The evidence reasoning method according to claim 1, characterized in that
determining corresponding information entropies thereof according to a plurality of
evidences comprises:
taking the information entropies as a weight for measuring the evidence, Si
represents the information entropy of the evidence I:
= -m(. 0)log n(A)
wherein ACO, t represents a type number of evidence judging results, m(A)
represents a degree of trust of the evidence to an event A, n evidences under a
recognition frame O={Oi, 02,..., ONJ are respectively E1, E 2 ... , En, basic probability
assignment functions corresponding thereto are respectively mi, m2..., mn, focal
elements are respectively Ai, wherein i is equal to 1, 2,..., N, 0 represents a null set,
and the evidence combination rule is:
0 A=0
wherein
K is a conflict coefficient.
3. The evidence reasoning method according to claim 2, characterized in that
conducting normalization on the information entropies to obtain a weight of evidence
comprises:
conducting normalization on Si to obtain the weight Wi of evidence:
Wi=Si/max(Si),
wherein 1 is equal to 1, 2,..., N.
4. The evidence reasoning method according to claim 2, characterized in that
calculating a new evidence after weighted allocation comprises:
I A=0 m'(A)={W 1m(A) A c6: , A #E4
wherein AC, n evidences under a recognition frame O={0, 02,..., ONJ are
respectively Ei, E 2 ... , En, W1 is the weight of evidence and 0 represents a null set.
5. The evidence reasoning method according to claim 1, characterized in that the
preset evidence combination rule R is:
R=(1-W2)m'1(A)+(1-W1)m'2(A).
6. The evidence reasoning method according to claim 1, characterized in that
determining an evidence reasoning result by a preset evidence combination rule
according to the new evidence after weighted allocation comprises:
conducting normalization on the determined m'(A) to obtain a final result m(A).
7. An evidence reasoning apparatus according to claim 1, wherein the evidence
reasoning apparatus comprises:
determining corresponding information entropies thereof according to a plurality
of evidences, wherein the evidences are obtained by a sensor network;
conducting normalization on the information entropies to obtain a weight of
evidence;
calculating a new evidence after weighted allocation; and
determining an evidence reasoning result by a preset evidence combination rule
according to the new evidence after weighted allocation.
8. An electronic device, comprising:
a processor, and a memory for storing a processor executable instruction,
wherein the processor implements the method according to any one of claims 1-6
by operating the executable instruction.
9. A computer readable medium, having computer readable instructions stored
therein, wherein the method according to any one of claims 1-6 is realized when the
instructions are executed by the processor.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110133888.6 | 2021-02-01 | ||
| CN202110133888.6A CN112819163A (en) | 2021-02-01 | 2021-02-01 | Evidence reasoning method and device based on information entropy weighting distribution |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2021104642A4 true AU2021104642A4 (en) | 2021-09-23 |
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ID=75860692
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| AU2021104642A Active AU2021104642A4 (en) | 2021-02-01 | 2021-07-28 | Evidence reasoning method and apparatus based on information entropy weighted allocation |
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| Country | Link |
|---|---|
| CN (1) | CN112819163A (en) |
| AU (1) | AU2021104642A4 (en) |
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2021
- 2021-02-01 CN CN202110133888.6A patent/CN112819163A/en active Pending
- 2021-07-28 AU AU2021104642A patent/AU2021104642A4/en active Active
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| CN112819163A (en) | 2021-05-18 |
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