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Novel tracking function of moving target using chaotic dynamics in a recurrent neural network model.
1Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Okayama, 700-8530, Japan, li@chaos.elec.okayama-u.ac.jp.
Cognitive Neurodynamics
|November 13, 2008
Summary
This study demonstrates how chaotic dynamics in recurrent neural networks can control objects to track moving targets. This approach successfully navigates complex, ill-posed problems in two-dimensional space.
Area of Science:
- Artificial Intelligence
- Dynamical Systems
- Robotics
Background:
- Controlling object motion to track targets in 2D space is an ill-posed problem.
- Recurrent neural networks (RNNs) offer potential for complex control tasks.
Purpose of the Study:
- To investigate the application of chaotic dynamics within an RNN for object tracking.
- To explore how chaotic dynamics influences complex motion generation and target acquisition.
Main Methods:
- Implementing chaotic dynamics in an RNN model for real-time motion control.
- Embedding cyclic memory attractors for basic object movements.
- Utilizing adaptive control parameter switching for constrained chaos (chaotic itinerancy).
Main Results:
- The RNN model successfully enabled an object to track a moving target along various trajectories.
- Chaotic dynamics facilitated complex object motions, improving tracking performance.
- Performance evaluation showed a high success rate over 100 trials.
Conclusions:
- Chaotic dynamics is a viable and effective mechanism for enhancing object tracking capabilities in RNNs.
- The study provides insights into the dynamical structure of chaotic dynamics for control applications.
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