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Related Experiment Video

Updated: May 22, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

A general CPL-AdS methodology for fixing dynamic parameters in dual environments.

De-Shuang Huang¹, Wen Jiang

¹School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China. dshuang@tongji.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|May 8, 2012

Summary

A new formula enhances the Continuous Point Location with Adaptive d-ary Search (CPL-AdS) strategy for efficient point location problems. This approach overcomes limitations with high-dimensional features, improving convergence and applicability.

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Last Updated: May 22, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Area of Science:

Computer Science
Algorithms
Computational Geometry

Background:

The Continuous Point Location with Adaptive d-ary Search (CPL-AdS) strategy is efficient for stochastic point location (SPL) problems.
A key limitation of CPL-AdS arises with high-dimensional features (large d), rendering its decision table for elimination impractical.
Larger feature dimensions (d) in CPL-AdS generally promote faster convergence and reduce oscillation.

Purpose of the Study:

To address the bottleneck in CPL-AdS caused by high-dimensional features.
To introduce a generalized universal decision formula for improved point location strategies.
To extend the CPL-AdS strategy for tracking dynamic parameters in diverse environments and explore learning automata impacts.

Main Methods:

Development of a generalized universal decision formula to overcome the decision table limitation in CPL-AdS.
Extension of the CPL-AdS strategy with a formula capable of tracking an unknown dynamic parameter (λ).
Integration of various learning automata within the generalized CPL-AdS method to assess learning algorithm efficiency.

Main Results:

The proposed universal decision formula effectively resolves the bottleneck associated with high-dimensional features in CPL-AdS.
The generalized CPL-AdS strategy successfully tracks dynamic parameters in both informative and deceptive environments.
Extensive experiments confirm the efficiency and feasibility of the developed approaches.

Conclusions:

The introduced universal decision formula significantly enhances the CPL-AdS strategy, broadening its applicability.
The generalized CPL-AdS method offers robust dynamic parameter tracking, valuable for theoretical and practical applications.
The study demonstrates the potential of enhanced CPL-AdS strategies for efficient data searching and point location problems.