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H∞ state estimation for stochastic recurrent neural networks with randomly occurring nonlinearities and variance constraint

机译：具有随机非线性和方差约束的随机递归神经网络的H ∞状态估计

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摘要

This paper is coped with the H∞ state estimation problem under error variance constraint for discrete stochastic recurrent neural networks (RNNs) with parameter uncertainty and randomly occurring nonlinearities. The activation functions satisfy the sector-bounded condition, and the phenomena of randomly occurring nonlinearities are described by random variables obeying Bernoulli distribution. The major purpose is on the development of a time-varying H∞ state estimation scheme such that some sufficient conditions are derived to ensure both the prescribed H∞ performance requirement and estimation error variance constraint. Accordingly, the desired estimation algorithm can be realized based on recursive linear matrix inequality approach. Finally, an example is presented to show the effectiveness of the main constrained estimation results.
机译：本文应付H ∞ 具有参数不确定性和随机非线性的离散随机递归神经网络（RNN）在误差方差约束下的状态估计问题。激活函数满足扇区边界条件，并且随机变量遵循伯努利分布来描述随机发生的非线性现象。主要目的是开发随时间变化的H ∞ 状态估计方案，以便导出一些足够的条件以确保既定的H ∞ 性能要求和估计误差方差约束。因此，可以基于递归线性矩阵不等式方法来实现期望的估计算法。最后，给出一个例子来说明主要约束估计结果的有效性。

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