Neural network design
GANNet: A Genetic Algorithm for Optimizing Topology and Weights in Neural Network Design
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
Information Sciences: an International Journal
Neural network based posture control of a human arm model in the sagittal plane
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
Journal of Intelligent and Robotic Systems
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In this paper the trajectory tracking control of a human arm moving on the sagittal plane is investigated by an interdisciplinary approach with the combination of neural network mapping, evolutionary computation, and dynamic system control. The arm in the study is described by a musculoskeletal model with two degrees of freedom and six muscles, and the control signal is applied directly in the muscle space. A new control system structure is proposed to manipulate the complicated nonlinear dynamical arm motion. To design the intelligent controller, an evolutionary diagonal recurrent neural network (EDRNN) is integrated with proper performance indices, in which genetic algorithm (GA) and evolutionary program (EP) strategy are effectively integrated with the diagonal recurrent neural network (DRNN). The hybrid GA with EP strategy is applied to optimize the DRNN architecture and an adaptive dynamic back-propagation (ADBP) algorithm with momentum for the multi-input multi-output (MIMO) systems is used to obtain the network weights. The effectiveness of the control scheme is demonstrated through a simulated case study.