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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Nonlinear system identification based on internal recurrent neural networks
Gheorghe Puscasu1, Bogdan Codres, Alexandru Stancu
1Faculty of Computer Science, "Dunarea de Jos" University of Galaţi, Str. Domneasca No.111, 800211, Romania. gpuscasu@ugal.ro
Abstract:
A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.
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