基于人工神经网络模型的hsamnon映射混沌脑系统设计与定点FPGA实现

Lei Zhang
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引用次数: 8

摘要

提出了一种基于人工神经网络(ANN)模型的hsamnon混沌系统设计方法,并给出了等效的硬件模型,用于现场可编程门阵列(FPGA)硬件协同仿真。混沌发生器可用于研究脑电图(EEG)捕获的脑活动的混沌行为。采用不同的定点数据格式设计了人工神经网络模型,并通过MATLAB和Simulink仿真对模型进行了评估。混沌水平是通过在选定的系统参数的一定范围内计算李雅普诺夫指数来测量的。在设计人工神经网络模型时,考虑了在现场可编程门阵列(FPGA)器件上采用定点数据格式的硬件实现。
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Artificial neural network model-based design and fixed-point FPGA implementation of hénon map chaotic system for brain research
This paper presents an artificial neural network (ANN) model based design for Hénon chaotic systems, and its equivalent hardware model for hardware co-simulation using Field Programmable Gate Arrays (FPGA). Chaotic generators can be used for the study of chaotic behaviors of brain activities captured by Electroencephalogram (EEG). The ANN model is designed with different fixed-point data format and evaluated using MATLAB and Simulink simulation. The chaotic level is measured by calculating the Lyapunov exponent over a certain range of selected system parameters. The ANN models are designed with the consideration of hardware implementation on Field Programmable Gates Array (FPGA) device using fixed-point data format.
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