Topology-conserving maps for learning visuo-motor-coordination
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Development of quantum-based adaptive neuro-fuzzy networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-elitist immune clonal quantum clustering algorithm
Neurocomputing
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Visual–Motor Coordination is a problem considered analogous to the hand-eye coordination in biological systems. In this work we propose a novel approach to this problem using Quantum Clustering and an extended Kohonen's Self-Organizing Feature Map (K-SOFM). This facilities the use of the method in varying workspaces by considering the joint angles of the robot arm. Unlike previous work, where a fixed topology for the input space is considered, the proposed approach determines a topology as the workspace varies. Quantum Clustering is a method which constructs a scale-space probability function and uses the Schroedinger equation and its lowest eigenstate to obtain a potential whose minimum gives the cluster centers. It transforms the input space into a Hilbert space, where it searches for its minimum. The motivation of this work is to identify the implicit relationship existing between the end-effector positions and the joint angles through Quantum Clustering and Neural Network methods to fine-tune the system to correctly identify the mapping.