Determining stimulation design parameters using artificial neural networks optimized with a genetic algorithm
    2.
    发明申请
    Determining stimulation design parameters using artificial neural networks optimized with a genetic algorithm 审中-公开
    使用遗传算法优化的人工神经网络确定刺激设计参数

    公开(公告)号:US20090182693A1

    公开(公告)日:2009-07-16

    申请号:US11986763

    申请日:2008-01-14

    CPC classification number: G06N3/086

    Abstract: A method for generating an artificial neural network ensemble for determining stimulation design parameters. A population of artificial neural networks is trained to produce one or more output values in response to a plurality of input values. The population of artificial neural networks is optimized to create an optimized population of artificial neural networks. A plurality of ensembles of artificial neural networks is selected from the optimized population of artificial neural networks and optimized using a genetic algorithm having a multi-objective fitness function. The ensemble with the desired prediction accuracy based on the multi-objective fitness function is then selected.

    Abstract translation: 一种用于生成用于确定刺激设计参数的人造神经网络集合的方法。 训练人口神经网络的群体以响应于多个输入值产生一个或多个输出值。 人工神经网络的人口被优化,以创建人工神经网络的优化人口。 从人造神经网络的优化群体中选择多个人造神经网络集合,并使用具有多目标适应度函数的遗传算法进行优化。 然后选择具有基于多目标适应度函数的所需预测精度的集合。

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