Recurrent Neural Networks for the Processing of Nonlinear, Nonstationary Signals

这个项目将尝试为人工神经网络开发更先进的设计，它可以从经验中学习如何随着时间的推移预测输入的过滤器流。这项工作将建立在之前的NSF拨款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解卷积和干扰抵消(例如，使蜂窝电话更好地工作)、非线性对象的识别和控制、金融时间序列的预测以及语音识别和瞬时信号处理(雷达、声纳、生物)等时变模式的分类等应用中进行测试。基本的方法将是加深对现有设计在这些应用中的局限性的理论理解，并开发新的通用设计--植根于对基本理论的探索--以克服这些局限性。在被优先考虑的理论问题中，包括在上下文中混合离散变量和连续变量的问题，时间扭曲(例如，不同的说话者以不同的速度说话)的问题，以及随时间变化的稳健性问题。

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.