Optimum Training Schemes for Recurrent Neural Network Nonlinear Filters

9616391 Olurotimi递归神经网络(RNN)是一种非线性动态滤波器，J.Lo已经证明它们能够收敛到信号过程的最小方差滤波器。然而，与利用自上而下的参数设计来实现过滤器的传统过滤技术不同，RNN方法是一种数据驱动的综合方法。几个普遍的逼近定理证明了神经网络方法的合理性，这些定理确保了神经网络形式在理论上足以实现这些任务。然而，设计者和用户都熟悉的神经网络设计的一个特征是，可以构建不同的(例如，在权重上)网络来解决相同的问题。为了解决这个问题，这个项目将采用新的概念和关于RNN在噪声中的行为的量化结果。认识到LO存在的重要结果，并利用Olurotimi和Das的最新结果，PI开发了一种改进的训练方法。由此得到的Werbos的有序导数训练方案搜索不一定是任何最优权集，而是最优权集的受限类，这也提高了估计器的效率。本文的研究将使S设计方案有望显著缩短非线性随机神经网络滤波器的设计时间。

9616391 Olurotimi Recurrent neural networks (RNN) are nonlinear dynamical filters, and it has been shown by J. Lo that they are capable of converging to the minimum variance filter for a signal process. However, unlike conventional filtering techniques that utilize top-down, parameteric design to realize the filters, the RNN approach is a data-driven synthesis approach. the neural network approach is justified by several universal approximation theorems that ensure that the neural network form is theoretically sufficient for implementing these tasks. However, one feature of neural network design familiar to designers and users alike is that may different (e.g. in weights) networks can be constructed to solve the same problem. This project will employ novel concepts and quantitative results on the behavior of RNN's in noise in order to address this problem. Recognizing the important existence results of Lo, and using recent results of Olurotimi and Das, the PI develops a modified training measure. The resulting ordered derivatives training scheme of Werbos the searches not for must any optimum weight set, but for the restricted class of optimum weight sets that also increase the estimator efficiency. The proposed research will result in s design scheme expected to significantly reduce the design time of nonlinear RNN filters.