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Feature-Oriented Hybrid Neural Adaptive Systems and Applications

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Intelligent Systems and Interfaces

Part of the book series: International Series in Intelligent Technologies ((ISIT,volume 15))

Abstract

In this chapter, we explain and illustrate the“feature space processing” paradigm, in relation to artificial neural network filtering and prediction. We use hybrid neural processors that explicitly include feature space representations of the data sets to illustrate this paradigm. A general feature space processing system is presented and several implementations are proposed. In order to outline the feature space processing concepts, both filtering and the prediction tasks are undertaken. In these cases, the input and the output lie in the same space. This facilitates the comparison of data vs. data plus feature space models. We focus on improvements to the neural model with respect to a reference feature space characterization. The resulting models perform similarly in the sample space and display a better feature space behavior. The modeling accuracy of the proposed neural hybrid systems is illustrated in two signal processing applications: spectrogram filtering, and tremor signal prediction.

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References

  • Alesch, F., Pinter, M.M., Helscher, R.J., Fertl, L., Benabid, A.L., Koos, W.T. (1995). “Stimulation of the ventral intermediate thalamic nucleus in tremor dominated Parkinson’ s disease and essential tremor.” Acta Neurochir. (Wien), 136 (1–2), 75–81.

    Article  Google Scholar 

  • Anouti, A., Koller WC. (1995).“Tremor Disorders. Diagnosis and Management.” West J. Med., 162(6), 510–3.

    Google Scholar 

  • Bain, P.G., Findley, L.J., Britton, T.C., Rothwell, J.C., Gresty, M.A., Thompson, P.D., Marsden, CD. (1995).“Primary writing tremor.” Brain, 118(Pt 6), 1461–1472.

    Article  Google Scholar 

  • Bonciu, C, Teodorescu, H.N. (1997).“Feature space Filtering With RBF Networks.” Proc. of the EUFIT97 Conf, Aachen, Germany, 241–249.

    Google Scholar 

  • Bonciu, C, Teodorescu, H.N. (1998).“Feature space Neural Systems for Identification and Control”, World Multiconf. on Systemics, Cybernetics and Informatics SCI’ 98, Orlando, Florida, 507–514.

    Google Scholar 

  • Britton, T.C. (1995).“Essential tremor and its variants.” Curr. Opin. Neurol., 8(4), 314–319.

    Article  Google Scholar 

  • Elble, R.J., Brilliant, M., Leffler, K., Higgins, C. (1996).“Quantification of essential tremor in writing and drawing.” Mov. Disord., 11(1), 70–8.

    Article  Google Scholar 

  • Ferraz, H.B., De Andrade, L.A., Silva, S.M., Borges, V., Rocha, M.S. (1994)“Postural tremor and dystonia. Clinical aspects and physiopathological considerations.” Arq. Neuropsiquiatr., 52(4),466–470.

    Article  Google Scholar 

  • Girossi, F., Poggio, T. (1990).“Networks and the Best Approximation Property.” Biological Cybernetics, 63, 169–176.

    Article  MathSciNet  Google Scholar 

  • Hanna, M., Mills, K., Pazdera, L., Newsom-Davis, J. (1997).“Primary orthostatic tremor with prominent muscle hypertrophy.” Neurology, 49(3), 872–874.

    Article  Google Scholar 

  • Haykin, S. (1994). Neural Networks: A Comprehensive Foundation, IEEE Press.

    Google Scholar 

  • Hebb, D.O. (1949), The Organization of Behaviour, Jon Wiley, New York.

    Google Scholar 

  • Miccelli, C.A. (1986).“Interpolation of Scattered Data: Distance Matrices and Conditionally Positive Definite Functions.” Constructive Approximations, 2(1), 11–22.

    Article  Google Scholar 

  • Minsky, M. (1992).“Future Models for Mind-Machines.” Proc. Int. Symposium on Natural Language Understanding and Al (ISKIT’ 92), Iizuka, Japan, 1–6.

    Google Scholar 

  • Mlynek, D., Teodorescu, H.N., Ropota, I., Teodorescu, C, Ciorap, R. (1997).“Fuzzy Control of Electrical Stimulation in Rehabilitation: A Dynamics Control.” Proc. Int. Conf EUFIT97, Aachen, Germany. 246–249.

    Google Scholar 

  • Nerrand, O., Roussel-Ragot, P., Urbani, D., Personnaz, L., Dreyfus, G. (1994).“Training Recurrent Neural Networks: Why and How? An Illustration in Dynamical Process Modelling.” IEEE Transactions on Neural Networks, 5(2). 178–184.

    Article  Google Scholar 

  • Park, J., Sandberg, I.W. (1993).“Approximation and Radial Basis Functions.” Neural Computation, 5(2), 305–316.

    Article  Google Scholar 

  • Pineda, F.J. (1988).“Dynamics and Architecture for Neural Computation.” Journal of Complexity 4, 216–275.

    Article  MathSciNet  MATH  Google Scholar 

  • Poggio, T. Girossi, F. (1990).“Networks for Approximation and Learning.” Proceedings of the IEEE, 78(9), 1481–1497.

    Article  Google Scholar 

  • Powell, M.J.D. (1987).“Radial Basis Functions for Multivariable Interpolation: A Review”, in Mason, J.C., Cox, M.G. (eds.), Algorithms for Approximations, Clarendon Press, Oxford.

    Google Scholar 

  • Rosenblatt, F. (1958).“The Perceptron: A Probabilistic Model For Information Storage and Organization In The Brain.” Psychological Review, 65(6), 386–408.

    Article  MathSciNet  Google Scholar 

  • Sanger, T.D. (1989).“Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural Network.” Neural Networks, 2,459–473.

    Article  Google Scholar 

  • Teodorescu, H.N. (1986).“Pattern-Oriented, Robust Filtering in Pattern Electrocardiography: A New Method.” 13th Int. Congress on Electrocardiology, George Washington University, Medical Center (summary in the Book of Abstracts).

    Google Scholar 

  • Teodorescu, H.N. (1987).“Robustness In Terms of Fuzziness,” Proc. 2 nd. IFSA Congress, Tokyo, 2,733–736.

    Google Scholar 

  • Teodorescu, H.N. (1988a).“Pattern-Oriented Robust Fuzzy Filtering,” Proc. of IIZUKA Conf. on Fuzzy Systems, Kyushu, Japan, 57–58.

    Google Scholar 

  • Teodorescu, H.N. (1988b).“Pattern-Oriented Robust Fuzzy Filtering — Digital Methods,” Proc. of IIZUKA Conf. on Fuzzy Systems, Kyushu, Japan, 59–60.

    Google Scholar 

  • Teodorescu, H.N. (1988c).“Robust Digital Signal Conditioning by Pattern-Oriented Filtering.” MESSCOMP’ 88, Network GmbH, Wiesbaden (Germany), 2B.71–2B.75.

    Google Scholar 

  • Teodorescu, H.N. (1988d).“Pattern-Oriented Filtering of Images.” Archives of the 10 th ISPRS Congress, Kyoto, Japan.

    Google Scholar 

  • Teodorescu, H.N., Yamakawa, T., Uchino, T., Miki, T., Inagawa, N., Sofron, E., Suceveanu, S. (1994).“Pattern Oriented Neuro-Fuzzy Filtering.” Proc. of the 3 rd Int. Conf. on Fuzzy Logic, Neural Nets and Soft Computing, Iizuka, Japan, 657–660.

    Google Scholar 

  • Teodorescu, H.N., Bonciu, C. (1996).“Feedforward Neural Filter with Learning in Features Space. Preliminary Results.” Conference Report of the First Int. Symp. on Neuro-Fuzzy Systems AT96, Lausanne, Switzerland.

    Google Scholar 

  • Teodorescu, H.N., Bonciu, C. (1997).“Learning Algorithm for RBF Networks as Features Extractors.” First Int. Conf. on Conventional & Knowledge-Based Intelligent Electronic Systems KES’ 97, Adelaide, Australia, 201–208.

    Google Scholar 

  • Teodorescu, H.N., Kandel, A., Teodorescu, C, Mlynek, D., Rodriguez, W., Posa, C., Ropota, I. (1998a).“Fuzzy Methods Of Fusing Classic And Chaotic Numerical Parameters in A Hand Tremor Rehabilitation System.” Proc. Int. Conf IIZUKA’ 98, Iizuka, Japan.

    Google Scholar 

  • Teodorescu, H.N. et al (1998b).“Analysis of Chaotic Movements and Fuzzy Assessment of Hands Tremor in Rehabilitation”, Proc. Int. Conf on Conventional & Knowledge-Based Intelligent Engineering Systems KES’ 98, Adelaide, Australia.

    Google Scholar 

  • Teodorescu, H.N., Bonciu, C. (1999a).“Feature space Neural Filters and Controllers”, in Intelligent Adaptive Control. Industrial Applications, Jain, L.C., de Silva, C.W., (eds.), CRC Press, 105–147.

    Google Scholar 

  • Teodorescu, H.N., Bonciu, C. (1999b).“Merging Signal and Pattern Processing: A Neural Hybrid Systems Approach”, Int. Journal of Knowledge-Based Intelligent Engineering Systems, 3(1), 12–19.

    Google Scholar 

  • Teodorescu, H.N. and Kandel, A. (1999c).“Nonlinear Analysis of Tremor and Applications”.Japanese J. Bio-Medical Engineering. (Submitted as invited paper, to appear in 1999)

    Google Scholar 

  • Tickle, A.B., Andrews, R., Golea, M., Diederich, J. (1998).“The Truth Will Come to Light: Directions and Challenges in Extracting the Knowledge Embedded Within Trained Artificial Neural Networks.” IEEE Transactions on Neural Networks 9(6), 1057–1067.

    Article  Google Scholar 

  • Tikhonov, A.V., Arsenin, V.Y. (1977). Solutions of Ill-Posed Problems, W.H. Winston, Washington D.C.

    Google Scholar 

  • Weiss, S.M., Kulikowski, C.A. (1991). Computer systems that learn: Classification and prediction methods from statistics, neural nets, machine learning, and expert systems, Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  • Wilkinson, J.H. (1985). The Algebraic Eigenvalue Problem, Oxford University Press.

    Google Scholar 

  • Yan, W.-O., Helmke, U., Moore, J.B. (1994).“Global Analysis of Oja’ s Flow for Neural Networks.” IEEE Transactions on Neural Networks, 5,674–683.

    Article  Google Scholar 

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Authors
  1. Horia-Nicolai Teodorescu
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  2. Cristian Bonciu
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Editor information

Editors and Affiliations

  1. University of South Florida (USF), USA

    Horia-Nicolai Teodorescu

  2. Swiss Federal Institute of Technology (EPFL), Switzerland

    Daniel Mlynek

  3. University of South Florida, USA

    Abraham Kandel

  4. RWTH, USA

    H.-J. Zimmermann

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© 2000 Springer Science+Business Media Dordrecht

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Teodorescu, HN., Bonciu, C. (2000). Feature-Oriented Hybrid Neural Adaptive Systems and Applications. In: Teodorescu, HN., Mlynek, D., Kandel, A., Zimmermann, HJ. (eds) Intelligent Systems and Interfaces. International Series in Intelligent Technologies, vol 15. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4401-2_6

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  • DOI: https://doi.org/10.1007/978-1-4615-4401-2_6

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-6980-6

  • Online ISBN: 978-1-4615-4401-2

  • eBook Packages: Springer Book Archive

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