1. Advances in Neural Information Processing Systems. Volumes 1-9, Tesauro, Touretzky, and Leen.
2. Fractals in Science. Bunde and Havlin.
3. Fractal Horizons. Pickover.
4. Physics and Fractal Structures. Gouyet.
5. Chaotic Dynamics. Baker and Gollub.
6. Beyond the Third Dimension. Banchoff.
7. Dynamic, Genetic, and Chaotic Programming. Soucek and the IRIS Group.
8. Chaos and Fractals: New Frontiers in Science. Peitgen, Jurgens, and Saupe.
9. Wavelets and Filter Banks. Strang and Nguyen. See their HOMEPAGE
10. The Emperor's New Mind. Penrose.
11. Chaos Under Control. Peak and Frame.
12. Fractals: A Users Guide for the Natural Sciences. Hastings and Sugihara.
13. Fractals, Chaos, and Power Laws: Minutes from and Infinite Paradise. Schroeder.
14. Exploring Complexity. Prigogine.
15. From Clocks to Chaos: The Rhythms of Life. Glass and Mackey.
16. Exploring Chaos. Hall.
17. Chaos. Gleick.
18. Adaptive Signal Processing. Widrow and Stearns.
19. Weather Cycles: Real or Imaginary? Burroughs.
20. The Mathematics of Technical Analysis: Applying Statistics to Trading Stocks, Options, and Futures. Sherry.
21. Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. Peters.
22. Neural Computing: Theory and Practice. Wasserman.
23. Introduction to the Theory of Neural Computing. Hertz, Krogh, and Palmer.
24. Foundations of Neural Networks. Khanna.
25. Introduction to Neural Networks and Expert Systems. California Scientific Software.
26. Turbulent Cascades in Foreign Exchange Markets. Ghashghaie et al. Nature: Volume 381, 767-770, June 27 1996.
27. WaveARX Neural Network Development for System Identification Using a Systematic Design Synthesis. Chen and Bruns. Ind. Eng. Chem. Res: Volume 34, 4420-4435, 1995.
28. Adaptation, 'Animal Spirits', and Self-Organized Criticality. Elliot Middleton. Department of Economics, Finance, and International Business, Metropolitan State University, Minneapolis, MN 55403. (See his web site and market prediction service listed in the links section of this site.)
29. Discrete- vs. Continuos-Time Nonlinear Signal Processing of Cu Electrodissolution Data. Rico-Martinez, Krischer, and Kevrekidis. Chem. Eng. Comm: Volume 118, 25-48, 1992.
30. Forecasting a Chaotic Time Series Using an Improved Metric for Embedding Space. Murray. Physica D: Volume 68, 318-325, 1993.
31. Fascinating Rhythm: A Primer on Chaos Theory and its Application to Cardiology. Denton et al. American Heart Journal: Volume 120(6) part 1. 1419-1440. 1990.
32. Chaos: Principles and Implications in Biology. Tsonis and Tsonis. CABIOS: Volume 5(1), 27-32, 1989.
33. Applications of Chaos Theory to Biology and Medicine. Skinner et al. Integrative Phys. and Behav. Sci. Volume 27(1), 39-53, 1992.
34. Chaos and Fractal Algorithms Applied to Signal Processing and Analsys. Handley et al. Simulation: Volume 60(4), 261-279, 1993.
35. The Economy as an Evolving Complex System. Anderson, Arrow, and Pines.
36. The New Science of Technical Analysis: Using the Statistical Techniques of Neuroscience to Uncover Order and Chaos in the Markets.
37. Multi-effect Decompositions for Financial Data Modeling. Wu and Moody, Advances in Neural Information Processing Systems, Volume 9.
38. Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches. Niranjan. Advances in Neural Information Processing Systems, Volume 9.
39. Multi-Task Learning for Stock Selection. Ghosn and Bengio. Advances in Neural Information Processing Systems, Volume 9.
40. Simulating Neural Networks with Mathematica
41. A Wavelet Tour of Signal Processing
This text for graduate students introduces wavelets and signal processing. Mallat (computer science, New York U.) emphasizes five subjects: the simultaneous analysis of a signal's time and frequency properties, zooming procedures that modify the scale parameter of waveforms, the creation of families of orthogonal bases, the ability of bases to approximate signals, and adaptive nonlinear algorithms. Annotation c. by Book News, Inc., Portland, Or.
Written by two leading authorities in the field, this self-contained volume provides a tutorial on projection methods and how to apply them in science and engineering. It details effective problem-solving strategies, and explores key applications in communication and signal processing, neural networks and pattern recognition, and optics and image processing. This book reviews the fundamentals of vector space theory, covers principles and applications of vector space projections in general, and projections onto convex sets in particular, provides real-world examples solvable on PCs and modest workstations, features more than 100 illustrations, and includes end-of-chapter exercises and references. This useful reference for practicing engineers, scientists, and educators can also be used for graduate-level study in science, mathematics, and engineering. Portions of the book have been used as material in short courses on applications of vector space projections.
43. Wavelet Toolware: Software for Wavelet Training Charles K. Chui Andrew K. Chan C. Steve Liu Amazon.com price: $59.95 In-Stock: Ships within 24 hours Pub. Date: January 1998
From Booknews: This software is designed as a learning and experimentation tool (rather than for use in solving high-precision computational problems), and offers users hands-on practice in the area of wavelet analysis and its applications. It supports several different wavelets, and contains, among other algorithms and codes, the one-dimensional and two-dimensional discrete wavelet transforms and their inverse transforms, as well as computations of the continuous wavelet transform and the short time Fourier transform. Simple signal processing applications, such as thresholding and noise removal are also included as examples of how to use the software. Annotation c. by Book News, Inc., Portland, Or.
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