Neuro-fuzzy structures and their comparative analyzes
WISICT '05 Proceedings of the 4th international symposium on Information and communication technologies
Pattern Recognition Letters
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Expert Systems with Applications: An International Journal
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Power signal classification using dynamic wavelet network
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Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Non-stationary power signal classification using local linear radial basis function neural networks
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervised Learning Probabilistic Neural Networks
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Expert Systems with Applications: An International Journal
Classification of Arrhythmia Using Hybrid Networks
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Statistical recognition of a set of patterns using novel probability neural network
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Generalized classifier neural network
Neural Networks
The CART decision tree for mining data streams
Information Sciences: an International Journal
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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≥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.