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A new inconsistent context fusion algorithm based on BP neural network and modified DST.

Hongji Xu1, Shi Li1, Shidi Fan1

  • 1School of Information Science and Engineering, Shandong University, Qingdao 266237, China.

Mathematical Biosciences and Engineering : MBE
|March 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for fusing inconsistent context information from multiple sensors. The approach effectively reduces uncertainty in smart systems, improving recognition accuracy in applications like personal identity verification.

Keywords:
Dempster-Shafer theory (DST)adaptive serviceback propagation (BP) neural networkcontext-aware computinginconsistent context fusion

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Fusion

Background:

  • Context-aware systems rely on diverse sensor data, leading to uncertainty and inconsistency.
  • Inconsistent multi-source information poses challenges for accurate system entity recognition and service delivery.
  • Addressing information inconsistency is crucial for reliable smart city and intelligent agriculture applications.

Purpose of the Study:

  • To propose a novel algorithm for inconsistent context fusion.
  • To enhance the accuracy of system entity recognition in uncertain environments.
  • To effectively manage and process heterogeneous context information from multiple sources.

Main Methods:

  • Developed a fusion algorithm combining back propagation (BP) neural networks and a modified Dempster-Shafer theory (DST) combination rule.
  • Utilized BP neural networks for effective recognition of entity situations.
  • Applied the modified DST combination rule for legitimate and meaningful fusion of recognition results.

Main Results:

  • The proposed algorithm demonstrates good performance in eliminating inconsistency and obtaining accurate recognition results.
  • Experiments in personal identity verification (PIV) scenarios show effectiveness across different error rates of context sources.
  • The fusion algorithm successfully handles uncertainty arising from dynamic, asynchronous, and heterogeneous context providers.

Conclusions:

  • The developed BP neural network and modified DST-based algorithm effectively fuses inconsistent context information.
  • The approach significantly reduces uncertainty, leading to improved recognition accuracy in complex systems.
  • This method offers a robust solution for enhancing context-aware systems in various applications.