A New Connectionist Model for Time-Varying Signal Classification

该项目将寻求一个统一的 连接主义框架 时变信号分类 基于伽马模型， (de弗里斯和普林西比，1990，1991， 1992年）。 伽马模型扩展了 传统的加法模型，因为 将输出激活乘以 权重被临时的 卷积，在计算上产生 改进的加法模型 本研究 将有理论和实践 件. 关于时间的作用的新见解 将寻求权重，使用理论 递归估计。 同样地确定 范式来研究神经系统 网络代表了 输入信号将被寻找，使用 希尔伯特空间 伽马存储器 投射出一个无约束的向量 一个预先定义的子空间。 同样的原则将被扩展到 学习，产生反向传播 有记忆的穿越时间算法 与长度无关的要求 输入信号的最大值。 作为一个应用，伽马网络 将在多处理器中实现 语音识别系统。 私家侦探 将伽马模型与两个 最好的范例，朗的 时滞神经网络和Tank神经网络 和霍普菲尔德浓度的时间 网络，在性能方面， 网络复杂性和学习 有效性

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.