Recurrent Neural Networks for the Processing of Nonlinear, Nonstationary Signals

这个项目将尝试开发更先进的人工神经网络设计，它可以从经验中学习如何预测随着时间的推移过滤输入流。 这项工作将建立在以前的NSF资助下开发的新设计的基础上，这些设计正在非平稳通信信道中的盲去卷积和干扰消除等应用中进行测试（例如，使蜂窝电话更好地工作）、非线性设备的识别和控制、金融时间序列的预测和时变模式的分类，如语音识别和瞬态信号处理（雷达、声纳、生物）。 基本的方法将是加深对这些应用中现有设计的局限性的理论理解，并开发新的通用设计-植根于基础理论的探索-以克服这些局限性。 在优先考虑的理论问题是混合离散和连续变量的上下文中的问题，时间扭曲的问题（例如，不同的扬声器以不同的速度说话），以及随着时间的推移鲁棒性的问题。

This project will attempt to develop more advanced designs for artifical neural networks which can learn from experience how to predict of filter streams of inputs over time. The work will build upon novel designs developed under a previous NSF grant, which are being tested in applications such as blind deconvolution and interference cancelling in nonstationary communication channels (e.g., making cellular phones work better), identification and control of nonlinear plants, prediction of financial time-series and classification of time varying patterns such as speech recognition and transient signal processing (radar, sonar, biological). The basic approach will be to deepen the theoretical understanding of the limitations of the existing designs in these applications, and to develop new general-purpose designs--rooted in an exploration of basic theory--to overcome these limitations. Among the theoretical issues given priority are the problem of mixing discrete and continuous variables in the context, the problem of time-warping (e.g. different speakers speaking at different speeds), and the problems of robustness over time.