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Random shortest path realization method based on hierarchical structure learning automaton

A technology of hierarchical structure and implementation method, applied in the field of information processing, to achieve the effect of high sampling cost, high accuracy and fast speed

Inactive Publication Date: 2017-07-14
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, distributed learning automatons are used to solve the results of random shortest paths, but there is still a lot of room for improvement in speed and accuracy

Method used

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  • Random shortest path realization method based on hierarchical structure learning automaton
  • Random shortest path realization method based on hierarchical structure learning automaton
  • Random shortest path realization method based on hierarchical structure learning automaton

Examples

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Embodiment Construction

[0026] Such as image 3 a~ image 3 As shown in d, this method specifically implements the initialization of the hierarchical structure network in the following manner: with image 3 a as an example, from the source node v s set off at v s Deploy a learning automaton on , the number of behaviors of the learning automaton is equal to v s out degree; from v s Each neighbor node v of 2 Departure, in each v 2 Deploy a learning automaton on each of them, the number of behaviors is equal to v 2 out degree; then from v 2 Each neighbor node v of 3 Start, deploy the learning automata layer by layer in the same way until the target node v d The deployment of the learning automata is completed; finally delete the nodes that have not deployed the learning automata, thus forming a hierarchical network of learning automata, such as image 3 As shown in b; each learning automaton completes the initialization work independently, and initializes their respective probability vectors a...

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Abstract

The invention discloses a random shortest path realization method based on a hierarchical structure learning automaton. The method comprises the following steps: learning automaton deployment: deploying the learning automatons beginning from a source node and ending at a node of a target stage through a dynamical network; initializing: initializing own probability vector of each learning automaton; path selection: selecting nodes layer by lays from a father node so as to form the current path; environment feedback: comparing a cost function of the current path with a mean of the current sampling path to obtain punishment or award; learning process: updating own probability vector according to a learning algorithm by each learning automaton on the selected path; judging the updating ending process layer by layer, and ending the step if the updating is ended, otherwise, updating a father node return path to select the continuous execution.

Description

technical field [0001] The invention relates to a technique in the field of information processing, in particular to a method for realizing a random shortest path based on a hierarchical structure learning automaton. Background technique [0002] The shortest path problem refers to the problem of determining the minimum edge length (weight, cost, etc.) under the premise of knowing the source node and the target node, which can be divided into deterministic shortest path and random shortest path. The deterministic shortest path problem is the shortest path problem with fixed side lengths, and the random shortest path refers to the problem that allows the side length to be randomly variable in the shortest path problem. Considering the dynamics of the actual network, compared with the deterministic shortest path, the random shortest path has a wider application in real problems. [0003] At present, the algorithms for solving the random shortest path can be roughly divided in...

Claims

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Application Information

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IPC IPC(8): H04L12/721H04L12/733H04L12/801H04L45/122
CPCH04L45/122H04L45/36H04L47/29
Inventor 李生红郭颖马颖华汤璐
Owner SHANGHAI JIAO TONG UNIV
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