A model for intersegmental coordination in the leech nerve cord
Biological Cybernetics
Biological Cybernetics - Special Issue: Dynamic Principles
Trajectory Generation Using GA for an 8 DOF Biped Robot with Deformation at the Sole of the Foot
Journal of Intelligent and Robotic Systems
A bio-inspired approach for online trajectory generation of industrial robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
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In this paper, we suggest a new supervised learning method called Fourier based automated learning central pattern generators (FAL-CPG), for learning rhythmic signals. The rhythmic signal is analyzed with Fourier analysis and fitted with a finite Fourier series. CPG parameters are selected by direct comparison with the Fourier series. It is shown that the desired rhythmic signal is learned and reproduced with high accuracy. The resulting CPG network offers several advantages such as, modulation and robustness against perturbation. The proposed learning method is simple, straightforward and efficient. Furthermore, it is suitable for on-line applications. The effectiveness of the proposed method is shown by comparison with four other supervised learning methods as well as an industrial robotic trajectory following application.