Strong convergence of the recursive parzen-type probabilistic neural network handling nonstationary noise

Authors:
Lena Pietruczuk;Yoichi Hayashi
Affiliations:
Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland;Department of Computer Science, Meiji University, Kawasaki, Japan
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Year:
2012

Citing 10
Cited 0

Sequential pattern recognition procedures derived from multiple Fourier series

Pattern Recognition Letters
Probabilistic neural networks

Neural Networks
Connectionist Structures of Type 2 Fuzzy Inference Systems

PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
Data Streams: Models and Algorithms (Advances in Database Systems)

Data Streams: Models and Algorithms (Advances in Database Systems)
A color image segmentation approach for content-based image retrieval

Pattern Recognition
Modular type-2 neuro-fuzzy systems

PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
On Bayes Risk Consistent Pattern Recognition Procedures in a Quasi-Stationary Environment

IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting ensemble of relational neuro-fuzzy systems

ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Multiple Fourier series procedures for extraction of nonlinearregressions from noisy data

IEEE Transactions on Signal Processing
Identification of MISO nonlinear regressions in the presence of a wide class of disturbances

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

A recursive version of the Parzen-type general regression neural network is studied. Strong convergence is established assuming time-varying noise. Experimental results are discussed in details.