The society of mind
Evolutionary neurocontrollers for autonomous mobile robots
Neural Networks - Special issue on neural control and robotics: biology and technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolution of Controllers from a High-Level Simulator to a High DOF Robot
ICES '00 Proceedings of the Third International Conference on Evolvable Systems: From Biology to Hardware
Biologically Inspired Neural Controllers for Motor Control in a Quadruped Robot
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
Incremental Evolution of Complex General Behavior
Incremental Evolution of Complex General Behavior
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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Two-link robot arm model is extensively used in literatures for that it is simple enough to simulate conveniently, yet contains all the nonlinear terms arising in general n-link manipulators. And neural networks are reported to be computationally efficient compared with traditional PID control and adaptive control. However, when a neural network is applied, one of the key step is to choose the optimal number of neurons. In this paper, a relative large number of neurons are initially used, which is pruned during the training. The conic sector theory is introduced in the design of this robust neural control system, which aims at providing guaranteed boundedness for both the input-output(I/O) signals and the weights of the neural network.