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1.

図書

図書
[edited by] S.S. Iyengar and Alberto Elfes
出版情報: Los Alamitos, Calif. : IEEE Computer Society Press, 1991  viii, 527 p. ; 29 cm
シリーズ名: IEEE Computer Society Press tutorial ; . Autonomous mobile robots ; v. 2
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2.

図書

図書
[edited by] S.S. Iyengar and Alberto Elfes
出版情報: Los Alamitos, Calif. : IEEE Computer Society Press, c1991  viii, 541 p. ; 29 cm
シリーズ名: IEEE Computer Society Press tutorial ; . Autonomous mobile robots ; v. 1
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3.

図書

図書
editor, T.F. Gonzalez . editor, S.S. Iyengar
出版情報: Anaheim, Calif. : ACTA Press, c2008  iv, 475 p. ; 28 cm
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4.

図書

図書
S. Sitharama Iyengar, E.C. Cho, Vir V. Phoha
出版情報: Boca Raton : Chapman & Hall/CRC, c2002  258 p. ; 25 cm
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目次情報: 続きを見る
Mathematical Preliminaries / I:
Sets / 1.1:
Functions / 1.2:
Sequences and Series / 1.3:
Complex Numbers / 1.4:
Linear Spaces / 1.5:
Matrices / 1.6:
Hilbert Spaces / 1.7:
Topology / 1.8:
Measure and Integral / 1.9:
Fourier Series / 1.10:
Exercises / 1.11:
Wavelets / 2:
Introduction / 2.1:
Dilation and Translation / 2.2:
Inner Product / 2.3:
Haar Wavelet / 2.4:
Multiresolution Analysis / 2.5:
Continuous Wavelet Transform / 2.6:
Discrete Wavelet Transform / 2.7:
Fourier Transform / 2.8:
Discrete Fourier Transform / 2.9:
DFT of Finite Sequences / 2.10:
Convolution / 2.11:
Neural Networks / 2.12:
McCulloch-Pitts Model of Neuron / 3.1:
Optimal Network Structure / 3.1.2:
Single Layer Perceptron / 3.1.3:
Multilayer Perceptrons / 3.2:
Adaptation Procedure of Rosenblatt / 3.2.1:
Backpropagation Learning / 3.2.2:
Hebbian Learning / 3.3:
Hebb's Rule / 3.3.1:
Hebbian Learning Applied to Multiple Input PEs / 3.3.2:
Oja's Rule / 3.3.3:
Sanger's Rule / 3.3.4:
Principal Component Analysis (PCA) / 3.3.5:
Anti-Hebbian Learning / 3.3.6:
Competitive and Kohonen Networks / 3.4:
Competitive Learning / 3.4.1:
Kohonen Self-Organizing Map / 3.4.2:
Grossberg's Instar-Outstar Network / 3.4.3:
Adaptive Resonance Theory (ART) / 3.4.4:
Recurrent Neural Networks / 3.5:
Hopfield Networks / 3.5.1:
Wavelet Networks / 3.6:
What are Wavelet Networks? / 4.1:
Dyadic Wavelet Network / 4.3:
Theory of Wavelet Networks / 4.4:
Wavelet Network Structure / 4.5:
Wavelet Network Algorithm / 4.5.1:
Multidimensional Wavelets / 4.6:
Learning in Wavelet Networks / 4.7:
Initialization of Wavelet Networks / 4.8:
Properties of Wavelet Networks / 4.9:
Scaling at Higher Dimensions
Recurrent Learning / 4.11:
Neural Structures / 5.1:
Learning Problems / 5.2.2:
On-line Learning Approaches: Recurrent Backpropagation / 5.2.3:
Recurrent Wavelets / 5.3:
Wavelets in Function Approximation / 5.3.1:
Recurrent Wavelet Networks / 5.3.2:
Numerical Experiments / 5.4:
Case 1 / 5.4.1:
Case 2 / 5.4.2:
Concluding Remarks / 5.5:
Separating Order from Disorder / 5.6:
Order within Disorder / 6.1:
Wavelet Networks: Trading Advisors / 6.2:
Comparison Results / 6.3:
Conclusions / 6.4:
Radial Wavelet Neural Networks / 6.5:
Data Description and Preparation / 7.1:
Classification Systems / 7.3:
Minimum Distance Classifier / 7.3.1:
Wavelet Neural Network Classifier / 7.3.2:
Results / 7.4:
Predicting Chaotic Time Series / 7.5:
Nonlinear Prediction / 8.1:
Short-Term Prediction / 8.3:
Parameter-Varying Systems / 8.5:
Long-Term Prediction / 8.6:
Appendix / 8.7:
Acknowledgments / 8.9:
Figures / 8.10:
Concept Learning / 8.11:
Background / 9.1:
Preliminaries / 9.2:
PAC Learning / 9.3.1:
Approximation by Wavelet Networks / 9.3.2:
Stochastic Approximation Algorithms / 9.3.3:
Learning Algorithms / 9.4:
Basic Algorithm / 9.4.1:
Batching Algorithm / 9.4.2:
Summary / 9.5:
Bibliography / 9.6:
Index / 10.1:
Mathematical Preliminaries / I:
Sets / 1.1:
Functions / 1.2:
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