Stochastic optimization and the simultaneous perturbation method
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Analog Integrated Circuits and Signal Processing - Special issue on Learning on Silicon
Using a New Model of Recurrent Neural Network for Control
Neural Processing Letters
Neural Control of the Movements of a Wheelchair
Journal of Intelligent and Robotic Systems
A New Learning Algorithm Using Simultaneous Perturbation with Weight Initialization
Neural Processing Letters
Control of a Robotic Wheelchair Using Recurrent Networks
Autonomous Robots
A cascade lattice IIR adaptive filter structure using simultaneous perturbation method
ICCOM'05 Proceedings of the 9th WSEAS International Conference on Communications
A Nonlinear ANC System with a SPSA-Based Recurrent Fuzzy Neural Network Controller
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Implementation of a neuro-fuzzy network with on-chip learning and its applications
Expert Systems with Applications: An International Journal
Learning scheme for complex neural networks using simultaneous perturbation
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Model-Free control of a nonlinear ANC system with a SPSA-Based neural network controller
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Complex-valued neural network using simultaneous perturbation with dynamic tunneling technique
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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This paper describes learning rules using simultaneous perturbation for a neurocontroller that controls an unknown plant. When we apply a direct control scheme by a neural network, the neural network must learn an inverse system of the unknown plant. In this case, we must know the sensitivity function of the plant using a kind of the gradient method as a learning rule of the neural network. On the other hand, the learning rules described here do not require information about the sensitivity function. Some numerical simulations of a two-link planar arm and a tracking problem for a nonlinear dynamic plant are shown