<P><STRONG><FONT size=3>Nonlinear Time Series Analysis (Paperback)</FONT></STRONG></P>
<P>by <a href="http://www.amazon.com/exec/obidos/search-handle-url/104-4698406-1830342?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Holger%20Kantz" target="_blank" ><FONT color=#003399>Holger Kantz</FONT></A> ，<a href="http://www.amazon.com/exec/obidos/search-handle-url/104-4698406-1830342?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Thomas%20Schreiber" target="_blank" ><FONT color=#003399>Thomas Schreiber</FONT></A> </P>
<LI><B>Paperback:</B> 386 pages
<LI><B>Publisher:</B> Cambridge University Press; 2 edition (January 26, 2004)
<LI><B>Language:</B> English </LI>
<LI><STRONG>Review<BR></STRONG>"...original, fundamentally honest, and very useful and valuable...an indispensable tool for [those] confronted with the analysis of possibly chaotic signals." Journal of Biological Sciences<BR><BR>"The book is a good reference to the current state of the art from the nonlinear dynamics community and is importnant reading for anyone faced with interpreting irregular time series." Contemporary Physics, Professor R.S. MacKay <BR><BR><B>Book Description</B><BR>The time variability of many natural and social phenomena is not well described by standard methods of data analysis. However, nonlinear time series analysis uses chaos theory and nonlinear dynamics to understand seemingly unpredictable behavior. The results are applied to real data from physics, biology, medicine, and engineering in this volume. Researchers from all experimental disciplines, including physics, the life sciences, and the economy, will find the work helpful in the analysis of real world systems. First Edition Hb (1997): 0-521-55144-7 First Edition Pb (1997): 0-521-65387-8 </LI>
<LI><STRONG>Contents</STRONG></LI>
<P><STRONG>Part I.</STRONG> <BR>Basic Topics: <BR>1. Introduction: why nonlinear methods?; <BR>2. Linear tools and general considerations; <BR>3. Phase space methods; <BR>4. Determinism and predictability; <BR>5. Instability: Lyapunov exponents; <BR>6. Self-similarity: dimensions; <BR>7. Using nonlinear methods when determinism is weak; <BR>8. Selected nonlinear phenomena; <BR><STRONG>Part II. Advanced Topics:</STRONG> <BR>9. Advanced embedding methods; <BR>10. Chaotic data and noise; <BR>11. More about invariant quantities; <BR>12. Modelling and forecasting; <BR>13. Non-stationary signals; <BR>14. Coupling and synchronisation of nonlinear systems; <BR>15. Chaos control; <BR><STRONG>Appendix A:</STRONG> using the TISEAN programs; <BR><STRONG>Appendix B:</STRONG> description of the experimental data sets; <BR><STRONG>References; <BR>Index.</STRONG></P>
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