Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Neural network for robotic control
Neural network for robotic control
Adaptive inverse control
A Kendama learning robot based on bi-directional theory
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Neural Processing Letters
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Visually guided movements: learning with modular neural maps in robotics
Neural Networks - Special issue on neural control and robotics: biology and technology
Self-Organizing Maps
Functional Networks with Applications: A Neural-Based Paradigm
Functional Networks with Applications: A Neural-Based Paradigm
Modular neural architectures for robotics
Biologically inspired robot behavior engineering
Adaptive mixtures of local experts
Neural Computation
IEEE Transactions on Neural Networks
Towards biomimetic neural learning for intelligent robots
Biomimetic Neural Learning for Intelligent Robots
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This chapter explores modular learning in artificial neural networks for intelligent robotics. Mainly inspired from neurobiological aspects, the modularity concept can be used to design artificial neural networks. The main theme of this chapter is to explore the organization, the complexity and the learning of modular artificial neural networks. A robust modular neural architecture is then developed for the position/orientation control of a robot manipulator with visual feedback. Simulations prove that the modular learning enhances the artificial neural networks capabilities to learn and approximate complex problems. The proposed bidirectional modular learning architecture avoids the neural networks well-known limitations. Simulation results on a 7 degrees of freedom robot-vision system are reported to show the performances of the modular approach to learn a high-dimensional nonlinear problem. Modular learning is thus an appropriate solution to robot learning complexity due to limitations on the amount of available training data, the real-time constraint, and the real-world environment.