Edward J. Rzempoluck

Neural Network Data Analysis Using Simulnet¿

• Broschiertes Buch

Produktbeschreibung

Scope of this Text This text is intended to provide the reader with an introduction to the analysis of numeri cal data using neural networks. Neural networks as data analytic tools allow data to be analyzed in order to discover and model the functional relationships among the recorded variables. Such data may be empirical. It may originate in an experiment in which the values of one or more dependent variables are recorded as one or more independent vari ables are manipulated. Alternatively, the data may be observational rather than empirical in nature, representing historical records of the behavior of some set of variables. An ex ample would be the values of a number of financial commodities, such as stocks or bonds. Finally, the data may originate in a computational model of some physical proc ess. Instead of recording variables of the physical process, the computer model could be run to generate an artificial analog of the physical data. Since data in virtually any native form can be expressed in numerical format, the scope of the analytical techniques and procedures that will be presented in this text is es sentially unlimited. Sources of data include research work in a range of disciplines as di verse as neuroscience, biomedicine, geophysics, psychology, sociology, archeology, eco nomics, and astrophysics. An often fruitful approach to data analysis involves the use of neural network func tions.

Produktdetails

• Verlag: Springer / Springer New York / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4612-7262-5
• Softcover reprint of the original 1st ed. 1998
• Seitenzahl: 240
• Erscheinungstermin: 19. April 2013
• Englisch
• Abmessung: 280mm x 210mm x 14mm
• Gewicht: 598g
• ISBN-13: 9781461272625
• ISBN-10: 1461272629
• Artikelnr.: 39494177

Inhaltsangabe

Scope of this Text.- What Is Expected from the Reader.- An Outline.- Computer Requirements.- 1 The Simulnet Desktop.- Desktop Components.- 2 Data Analysis.- The Substantive Question.- Neural Network Analysis.- Genetic Algorithms and Neural Networks.- The Probabilistic Network.- The Vector Quantizer Network.- Assessing the Significance of Network Results.- Network Application Examples.- Fractal Dimension Analysis.- Fourier Analysis.- Eigenvalue Analysis.- Coherence and Phase Analysis.- Mutual Information Analysis.- Correlation and Covariance Analysis.- 3 Acquiring and Conditioning Network Data.- Data Specification.- Data Collection.- Data Inspection.- Data Conditioning.- Detrend-Order 0.- Standardize Columns.- Frequency Filtering.- Principal Component Analysis.- Principal Component Data Reduction.- 4 A Data Analysis Protocol.- A Preprocessing Checklist.- Analyzing Experimental Data.