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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis
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A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis

机译：基于时间序列判别分量分析的降维递归概率神经网络

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摘要

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.
机译：本文提出了一种基于时间序列判别分量分析（TSDCA）的概率神经网络（NN），可用于对高维时间序列模式进行分类。 TSDCA涉及使用一组正交变换将高维时间序列压缩到低维空间中，以及基于连续密度隐藏马尔可夫模型和在降维空间中表达的高斯混合模型来计算后验概率。可以将分析合并到一个称为时间序列判别组件网络（TSDCN）的NN中，以便通过基于时间的学习算法，通过反向传播，通过降噪和分类参数可以同时获得，作为网络系数。拉格朗日乘数法。 TSDCN被认为可以对高维时间序列模式进行高精度分类，并减少网络训练所需的计算时间。在研究过程中进行的实验中，TSDCN对于高维人工数据和脑电图信号的有效性得到了证明。

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