Adaptive probabilistic neural networks for pattern classification in time-varying environment
Adaptive probabilistic neural networks for pattern classification in time-varying environment
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DOI：
10.1109/tnn.2004.828757
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发表时间：
2004-07
影响因子：
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通讯作者：
L. Rutkowski
中科院分区：
文献类型：
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作者：
L. Rutkowski
本文提出了一类工作在非平稳环境中的概率神经网络(PNNS)。1)将非平稳环境下的模式分类问题描述为预测问题，并设计了概率神经网络对具有时变概率分布的模式进行分类。我们注意到，非平稳情况下的模式分类问题与预测问题密切相关，因为基于长度为n的学习序列，应该对时刻n+k，k/spl ges/1的模式进行分类。2)在文献中首次给出了时变环境下PNN的最优性定义。此外，我们还证明了我们的概率神经网络渐近逼近贝叶斯最优(时变)决策面。3)研究了所构造的PNN的收敛速度。4)详细设计了基于Parzen核和多元Hermite级数的概率神经网络。
In this paper, we propose a new class of probabilistic neural networks (PNNs) working in nonstationary environment. The novelty is summarized as follows: 1) We formulate the problem of pattern classification in nonstationary environment as the prediction problem and design a probabilistic neural network to classify patterns having time-varying probability distributions. We note that the problem of pattern classification in the nonstationary case is closely connected with the problem of prediction because on the basis of a learning sequence of the length n, a pattern in the moment n+k, k/spl ges/1 should be classified. 2) We present, for the first time in literature, definitions of optimality of PNNs in time-varying environment. Moreover, we prove that our PNNs asymptotically approach the Bayes-optimal (time-varying) decision surface. 3) We investigate the speed of convergence of constructed PNNs. 4) We design in detail PNNs based on Parzen kernels and multivariate Hermite series.