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首页> 外文期刊>Proceedings of the IEEE >A signal processing framework based on dynamic neural networks with application to problems in adaptation, filtering, and classification
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A signal processing framework based on dynamic neural networks with application to problems in adaptation, filtering, and classification

机译：基于动态神经网络的信号处理框架及其在适应，滤波和分类中的应用

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摘要

We present a coherent neural net based framework for solving various signal processing problems. It relies on the assertion that time-lagged recurrent networks possess the necessary representational capabilities to act as universal approximators of nonlinear dynamical systems. This applies to system identification, time-series prediction, nonlinear filtering, adaptive filtering, and temporal pattern classification. We address the development of models of nonlinear dynamical systems, in the form of time-lagged recurrent neural nets, which can be used without further training. We employ a weight update procedure based on the extended Kalman filter (EKF). Against the tendency for a net to forget earlier learning as it processes new examples, we develop a technique called multistream training. We demonstrate our framework by applying it to 4 problems. First, we show that a single time-lagged recurrent net can be trained to produce excellent one-time-step predictions for two different time series and also to be robust to severe errors in the input sequence. Second, we model stably a complex system containing significant process noise. The remaining two problems are drawn from real-world automotive applications. One involves input-output modeling of the dynamic behavior of a catalyst-sensor system which is exposed to an operating engine's exhaust stream, the other the real-time and continuous detection of engine misfire.
机译：我们提出了一个基于神经网络的框架，用于解决各种信号处理问题。它依赖于这样的主张，即时滞递归网络具有必要的表示能力，可以充当非线性动力系统的通用逼近器。这适用于系统识别，时间序列预测，非线性滤波，自适应滤波和时间模式分类。我们以时滞递归神经网络的形式解决非线性动力学系统模型的开发问题，而无需进一步培训就可以使用它。我们采用基于扩展卡尔曼滤波器（EKF）的权重更新过程。面对网络在处理新示例时会忘记早期学习的趋势，我们开发了一种称为多流训练的技术。我们通过将其应用于4个问题来演示我们的框架。首先，我们表明可以训练单个时滞递归网络，以针对两个不同的时间序列生成出色的一次步长预测，并且对于输入序列中的严重错误也具有较强的鲁棒性。其次，我们稳定地建模包含大量过程噪声的复杂系统。剩下的两个问题来自现实的汽车应用。一种涉及对暴露于运行中的发动机排气流中的催化剂传感器系统的动态行为进行输入-输出建模，另一种涉及对发动机失火的实时和连续检测。

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