Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Introduction to the theory of neural computation
Introduction to the theory of neural computation
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Robot Dynamics and Control
VHDL: Analysis and Modeling of Digital Systems
VHDL: Analysis and Modeling of Digital Systems
Dynamically Reconfigurable Hardware - A New Perspective for Neural Network Implementations
FPL '02 Proceedings of the Reconfigurable Computing Is Going Mainstream, 12th International Conference on Field-Programmable Logic and Applications
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
Parametric identification of robotic systems with stable time-varying Hopfield networks
Neural Computing and Applications
Hopfield Neural Networks for Parametric Identification of Dynamical Systems
Neural Processing Letters
New emulated discrete model of CNN architecture for FPGA and DSP applications
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
FPGA implementation of hopfield networks for systems identification
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Scaling analysis of a neocortex inspired cognitive model on the Cray XD1
The Journal of Supercomputing
A context switching streaming memory architecture to accelerate a neocortex model
Microprocessors & Microsystems
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The aim of this contribution is to implement a hardware module that performs parametric identification of dynamical systems. The design is based upon the methodology of optimization with Hopfield neural networks, leading to an adapted version of these networks. An outstanding feature of this modified Hopfield network is the existence of weights that vary with time. Since weights can no longer be stored in read-only memories, these dynamic weights constitute a significant challenge for digital circuits, in addition to the usual issues of area occupation, fixed-point arithmetic and nonlinear functions computations. The implementation, which is accomplished on FPGA circuits, achieves modularity and flexibility, due to the usage of parametric VHDL to describe the network. In contrast to software simulations, the natural parallelism of neural networks is preserved, at a limited cost in terms of circuitry cost and processing time. The functional simulation and the synthesis show the viability of the design. In particular, the FPGA implementation exhibits a reasonably fast convergence, which is required to produce accurate parameter estimations. Current research is oriented towards integrating the estimator within an embedded adaptive controller for autonomous systems.