The design of a complete expansion that allows for
compact representation of certain relevant classes of signals is a
central problem in signal processing applications. Achieving such a
representation means knowing the signal features for the purpose of
denoising, classification, interpolation and forecasting. Multilayer
Neural Networks are relatively a new class of techniques that are
mathematically proven to approximate any continuous function
arbitrarily well. Radial Basis Function Networks, which make use of
Gaussian activation function, are also shown to be a universal
approximator. In this age of ever-increasing digitization in the
storage, processing, analysis and communication of information,
there are numerous examples of applications where one needs to
construct a continuously defined function or numerical algorithm to
approximate, represent and reconstruct the given discrete data of a
signal. Many a times one wishes to manipulate the data in a way that
requires information not included explicitly in the data, which is
done through interpolation and/or extrapolation.
Tidal data are a very perfect example of time series and many
statistical techniques have been applied for tidal data analysis and
representation. ANN is recent addition to such techniques. In the
present paper we describe the time series representation capabilities
of a special type of ANN- Radial Basis Function networks and
present the results of tidal data representation using RBF. Tidal data
analysis & representation is one of the important requirements in
marine science for forecasting.
[1] Becker T. and Weispfenning V., "Gröbner Bases: A Computational
Approach to Commutative Algebra", New York: Springer-Verlag, 1993.
[2] Proakis J.G., "Digital Communication", 3rd ed., Mcgraw-Hill,
NewYork,1995.
[3] Polikar Robi, "Fundamental Concepts and overview of the wavelet
theory", Sec.edition, 1994
[4] Park J. and Sandberg J.W., "Universal Approximation using radial basis
functions network," Neural Computation, Vol. 3, pp. 246-257, 1991.
[5] Poggio T. and Girosi F., "Networks for approximation and learning,"
proc. IEEE, Vol. 78, no. 9, pp. 1481-1497, 1990.
[6] Haykin S.," Neural Networks: A comprehensive Foundation", Upper
Saddle River, NJ: Prentice hall, 1994.
[7] Broomhead D.S. and D. Lowe., "Multivariate functional interpolation
and adaptive networks", Complex Systems, vol 2, pp.321-355, 1988.
[8] Jin X. C, Ong S.H., and Jayasooriah, "A practical method for estimating
fractal dimension", Pattern recogn. Lett., vol. 16, pp. 457-564,1995.
[9] Govil Rekha, "Neural Networks in Signal rocessing", Studies In
Fuzziness and Soft Computing, Physica Verlag, vol 38 . pp 235-257,
2000.
[1] Becker T. and Weispfenning V., "Gröbner Bases: A Computational
Approach to Commutative Algebra", New York: Springer-Verlag, 1993.
[2] Proakis J.G., "Digital Communication", 3rd ed., Mcgraw-Hill,
NewYork,1995.
[3] Polikar Robi, "Fundamental Concepts and overview of the wavelet
theory", Sec.edition, 1994
[4] Park J. and Sandberg J.W., "Universal Approximation using radial basis
functions network," Neural Computation, Vol. 3, pp. 246-257, 1991.
[5] Poggio T. and Girosi F., "Networks for approximation and learning,"
proc. IEEE, Vol. 78, no. 9, pp. 1481-1497, 1990.
[6] Haykin S.," Neural Networks: A comprehensive Foundation", Upper
Saddle River, NJ: Prentice hall, 1994.
[7] Broomhead D.S. and D. Lowe., "Multivariate functional interpolation
and adaptive networks", Complex Systems, vol 2, pp.321-355, 1988.
[8] Jin X. C, Ong S.H., and Jayasooriah, "A practical method for estimating
fractal dimension", Pattern recogn. Lett., vol. 16, pp. 457-564,1995.
[9] Govil Rekha, "Neural Networks in Signal rocessing", Studies In
Fuzziness and Soft Computing, Physica Verlag, vol 38 . pp 235-257,
2000.
@article{"International Journal of Information, Control and Computer Sciences:51044", author = "Ritu Vijay and Rekha Govil", title = "Tidal Data Analysis using ANN", abstract = "The design of a complete expansion that allows for
compact representation of certain relevant classes of signals is a
central problem in signal processing applications. Achieving such a
representation means knowing the signal features for the purpose of
denoising, classification, interpolation and forecasting. Multilayer
Neural Networks are relatively a new class of techniques that are
mathematically proven to approximate any continuous function
arbitrarily well. Radial Basis Function Networks, which make use of
Gaussian activation function, are also shown to be a universal
approximator. In this age of ever-increasing digitization in the
storage, processing, analysis and communication of information,
there are numerous examples of applications where one needs to
construct a continuously defined function or numerical algorithm to
approximate, represent and reconstruct the given discrete data of a
signal. Many a times one wishes to manipulate the data in a way that
requires information not included explicitly in the data, which is
done through interpolation and/or extrapolation.
Tidal data are a very perfect example of time series and many
statistical techniques have been applied for tidal data analysis and
representation. ANN is recent addition to such techniques. In the
present paper we describe the time series representation capabilities
of a special type of ANN- Radial Basis Function networks and
present the results of tidal data representation using RBF. Tidal data
analysis & representation is one of the important requirements in
marine science for forecasting.", keywords = "ANN, RBF, Tidal Data.", volume = "2", number = "12", pages = "4006-4", }