Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Adaptive 3D sound systems
Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real-Time Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Adaptive Filtering: Algorithms and Practical Implementation
Adaptive Filtering: Algorithms and Practical Implementation
A tree-structured piecewise linear filter with recursive least-squares adaptation
ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
Nonlinear filters based on combinations of piecewise polynomialswith compact support
IEEE Transactions on Signal Processing
Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
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A Sub-Space Adaptive Filter (SSAF) model is developed using, as a basis, the Modified Probabilistic Neural Network (MPNN) and its extension the Tuneable Approximate Piecewise Linear Regression (TAPLR) model. The TAPLR model can be adjusted by a single smoothing parameter continuously from the best piecewise linear model in each sub-space to the best approximately piecewise linear model over the whole data space. A suitable value in between ensures that all neighbouring piecewise linear models merge together smoothly at their boundaries. This model was developed by altering the form of the MPNN, a network used for general nonlinear regression. The MPNNs special structure allows it to be easily used to model a process by appropriately weighting piecewise linear models associated with each of the network's radial basis functions. The model has now been further extended to allow each piecewise linear model section to be adapted separately as new data flows through it. By doing this, the proposed SSAF model represents a learning/filtering method for nonlinear processes that provides one solution to the stability/plasticity dilemma associated with standard adaptive filters.