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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >An Approach to Stable Gradient-Descent Adaptation of Higher Order Neural Units
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An Approach to Stable Gradient-Descent Adaptation of Higher Order Neural Units

机译：一种高阶神经单元的稳定梯度下降自适应方法

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摘要

Stability evaluation of a weight-update system of higher order neural units (HONUs) with polynomial aggregation of neural inputs (also known as classes of polynomial neural networks) for adaptation of both feedforward and recurrent HONUs by a gradient descent method is introduced. An essential core of the approach is based on the spectral radius of a weight-update system, and it allows stability monitoring and its maintenance at every adaptation step individually. Assuring the stability of the weight-update system (at every single adaptation step) naturally results in the adaptation stability of the whole neural architecture that adapts to the target data. As an aside, the used approach highlights the fact that the weight optimization of HONU is a linear problem, so the proposed approach can be generally extended to any neural architecture that is linear in its adaptable parameters.
机译：介绍了一种高阶神经单元（HONU）权重更新系统的稳定性评估，该系统具有神经输入的多项式聚合（也称为多项式神经网络类），用于通过梯度下降法适应前馈和递归HONU。该方法的基本核心是基于权重更新系统的频谱半径，它允许在每个适应步骤中分别进行稳定性监视和维护。确保权重更新系统的稳定性（在每个适应步骤中）自然会导致整个神经体系结构适应目标数据的适应稳定性。顺便说一句，所使用的方法突出了以下事实：HONU的权重优化是线性问题，因此，所提出的方法通常可以扩展到在其自适应参数方面呈线性的任何神经体系结构。

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