An Optimal Estimation Method for Complex Networks with Random Occurrence Couplings

A complex network and state estimation technology, applied in data exchange network, digital transmission system, electrical components, etc., can solve the problems of low estimation performance accuracy, inability to deal with measurement loss and transmission data loss at the same time, to ensure estimation error, Easy to solve and realize, guarantee the effect of minimization

Active Publication Date: 2021-07-27
HARBIN UNIV OF SCI & TECH
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  • Abstract
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  • Application Information

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Problems solved by technology

[0006] The invention solves the problem that the existing state estimation method cannot simultaneously deal with the time-delay complex network with random coupling with measurement loss phenomenon and inaccurate occurrence probability, resulting in low estimation performance accuracy, transmission data loss, transmission failure, and coupling node failure. In the case of receiving other node information at the same time, it leads to the problem of low estimation performance accuracy, and proposes a complex network optimization estimation method with stochastic coupling

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  • An Optimal Estimation Method for Complex Networks with Random Occurrence Couplings
  • An Optimal Estimation Method for Complex Networks with Random Occurrence Couplings
  • An Optimal Estimation Method for Complex Networks with Random Occurrence Couplings

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specific Embodiment approach 1

[0032] Specific implementation mode one: combine figure 1 Describe this embodiment, the specific process of a complex network optimization estimation method with stochastic coupling in this embodiment is:

[0033] The complex network may be a network composed of satellites, robots, spacecraft or radars;

[0034] Step 1. Establishing a dynamic model of a complex network with stochastic coupling and time-delay with measurement loss phenomenon and inaccurate occurrence probability;

[0035] Step 2. Under the event-triggered protocol, perform state estimation on the stochastic coupled time-delay complex network dynamic model with measurement loss phenomenon and inaccurate occurrence probability established in step 1;

[0036] Step 3. Calculate the upper bound of the one-step prediction error covariance matrix Σ of the state estimation of each node of the complex network i,k+1|k ;

[0037] Step 4. According to the upper bound of the one-step forecast error covariance matrix obta...

specific Embodiment approach 2

[0042] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that the measurement loss phenomenon (λ i,k ) and inaccurate occurrence probability (γ i,k ) stochastic coupled time-delay complex network dynamic model;

[0043] The state-space form of a stochastic coupled time-delay complex network dynamic model with measurement loss and inaccurate probability is:

[0044]

[0045] the y i,k =λ i,k C i,k x i,k +ν i,k ,i=1,2,...,N, (2)

[0046]

[0047] In the formula,

[0048] are the state variables of the i-th node of the complex network at the kth, k+1 and k-d moments; d is a fixed network delay; is the real field of the state of the complex network dynamic model, n is the dimension; x j,k is the state variable of the jth node of the complex network at the kth moment; is the measurement output of the i-th node at the k-th moment; is the real number field output by the complex network dynamic model, and p is the dimensio...

specific Embodiment approach 3

[0055] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that the event-triggered protocol in Step 2 establishes a random coupling time-delay complex network with measurement loss phenomenon and inaccurate occurrence probability in Step 2. The dynamic model is used for state estimation; the specific process is:

[0056] First, for the i-th node, select the following event trigger formula:

[0057]

[0058] in is the measurement output of the i-th node at the last trigger time, is the corresponding last trigger time value, δ i >0 is a known adjustment threshold, T is the transpose; then the next event of the i-th node triggers the sequence Generated iteratively by:

[0059]

[0060] in is a set of positive integers, and inf{} is a lower limit function;

[0061] After the event trigger mechanism, the actual measurement passed to the filter is

[0062]

[0063] When the next event trigger sequence does not arrive, the actual measure...

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Abstract

An optimization estimation method for a complex network with random coupling, the invention relates to an optimization estimation method for a complex network with random coupling. The invention solves the problem that the existing state estimation method cannot simultaneously deal with the time-delay complex network with random coupling with measurement loss phenomenon and inaccurate occurrence probability, resulting in low estimation performance accuracy, transmission data loss, transmission failure, and coupling node failure. In the case of receiving other node information at the same time, it leads to the problem of low accuracy of estimated performance. The process is as follows: 1. Establish a dynamic model of a complex network with random coupled time-delay; 2. Estimate the state of the dynamic model under the event-triggered protocol; 3. Calculate ∑ i,k+1|k ; Four, calculate K i,k+1 ; 5. Get the judgment whether k+1 reaches M, if k+1<M, execute 6, otherwise end; 6. Calculate ∑ i,k+1|k+1 ; Otherwise, k=k+1, execute two until k+1=M is ​​satisfied. The invention is used in the field of complex network optimization estimation.

Description

technical field [0001] The invention relates to an optimal estimation method for a complex network with stochastic coupling. Background technique [0002] State estimation of complex networks is an important research problem in control systems and has been widely used in signal estimation tasks in engineering, power grids, social networks and other fields. [0003] When the network is congested, it will often lead to the occurrence of measurement loss. In addition, in practical applications, the random occurrence of coupling between nodes is caused by network switching. Therefore, it is necessary to design a state estimation method that is applicable to these network-induced phenomena, especially when the probability of random coupling is uncertain; [0004] Existing state estimation methods cannot simultaneously deal with stochastically coupled time-delay complex networks with measurement loss and inaccurate occurrence probability, resulting in low estimation performance a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/24
CPCH04L41/142H04L41/145
Inventor 胡军张红旭武志辉刘凤秋张昌露
Owner HARBIN UNIV OF SCI & TECH
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