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FPGA Implementation of Hopfield Networks for Systems Identification

机译:用于系统识别的Hopfield网络的FPGA实现

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This contribution presents the hardware implementation of a neural system, which is a variant of a Hopfield network, modified to perform parametric identification of dynamical systems, so that the resulting network possess time-varying weights. The implementation, which is accomplished on FPGA circuits, is carefully designed so that it is able to deal with these dynamic weights, as well as preserve the natural parallelism of neural networks, at a limited cost in terms of occupied area and processing time. The design achieves modularity and flexibility, due to the usage of parametric VHDL to describe the network. The functional simulation and the synthesis show the viability of the design, whose refinement will lead to the development of an embedded adaptive controller for autonomous systems.
机译:此贡献介绍了神经系统的硬件实现,该神经系统是Hopfield网络的一种变体,经过修改可以执行动力学系统的参数识别,因此所得网络具有随时间变化的权重。在FPGA电路上完成的实现经过精心设计,以使其能够处理这些动态权重,并在占用面积和处理时间方面以有限的成本保持神经网络的自然并行性。由于使用参数VHDL来描述网络,因此该设计实现了模块化和灵活性。功能仿真和综合表明了该设计的可行性,其改进将导致开发用于自治系统的嵌入式自适应控制器。

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