A New Connectionist Model for Time-Varying Signal Classification

这个项目将寻求一个统一的连接论框架，用于基于(De Vries and普林西比，1990,1991)提出的伽马模型对时变信号进行分类，1992年)。伽马模型扩展了传统的加法模型，因为输出激活与权重的乘法被时间卷积取代，产生了计算上改进的加法模型。本研究将包括理论和实践两个部分。使用递归估计理论，将寻求对时间权重的作用的新见解。同样，将使用希尔伯特空间来寻找研究神经网络如何表示输入信号历史的范例。伽马存储器将无约束维度的矢量投影到预定义子空间上。同样的原理将被扩展到学习，产生具有独立于输入信号长度的存储需求的时间反向传播算法。作为应用，伽马网络将在用于语音识别的多处理器系统中实现。私家侦探会将伽马模型与两种最好的范例进行对比，Lang的时延神经网络和时间网络中的Tank和Hopfield集中度，就性能而言，网络复杂性和学习效率。

This project will seek a unifying connectionist framework for the classification of time varying signals based on the gamma model proposed by (de Vries and Principe, 1990, 1991, 1992). The gamma model extends the conventional additive model because the multiplication of output activations with weights is subsituted by a temporal convolution, yielding a computationally improved additive model. This research will have theoretical and practical components. New insights on the role of temporal weights will be sought, using the theory of recursive estimation. Likewise, a paradigm to study how the neural network is representing the history of the input signal will be sought, using Hilbert spaces. The gamma memory projects a vector of unconstrained dimension onto a predefined subspace. This same principle will be extended to learning, producing a backpropagation through time algorithm that has memory requirements independent of the length of the input signal. As an application, the gamma network will be implemented in a multiprocessor system for speech recognition. The P.I. will contrast the gamma model with two of the best available paradigms, Lang's time delay neural network and Tank's and Hopfield concentration in time networks, in terms of performance, network complexity and learning effectiveness.