“The ART of adaptive pattern recognizing neural network,” IEEE Comput. Grossberg, “ART2: Self-organization of stable category recognition codes for analog input patterns,” Appl. ![]() Broomhead and D.Lowe, “Radial basis-function, multi-variable functional interpolation and adaptive networks,” Royal Signals Radar Est. Theoretical Phys., Cabridge Univ., Cabridge, U.K., 1985.ĭ.S. Powell, “Radial basis functions for multivariate interpolation: A review,” Tech. McClelland, “Parallel distributed processing, explorations in the microstructure of cognition,” in Foundations, vol 1. Williams, “Learning representation by backpropagating errors,” Nature, vol. This process is experimental and the keywords may be updated as the learning algorithm improves.ĭ.E. These keywords were added by machine and not by the authors. Multivariate Adaptive Regression Spline.The topics covered include dynamic modeling, model based ANN’s, statistical learning, eigen structure based processing and generalization structures. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. They can also adopt their model parameters to statistical change with time. ANN’s can be treated as nonparametric techniques, which can model an underlying process from example data. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. In all such applications signal processing is embedded in the process. ![]() In research & design, control, supervision, maintenance and production, mathematical models and theories are used extensively. Nuclear Engineering has matured during the last decade.
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