Product Description

The first book to offer practical applications of neural networks to solve problems in digital signal processing and imaging. A highly practical book with a minimum of math and a wealth of examples. Disk includes a complete program for training, testing, and using neural networks along with C++ subroutines for all techniques discussed and source for the book's example code.

Product Details

• Amazon Sales Rank: #1555656 in Books
• Published on: 1994-07
• Original language: English
• Number of items: 1
• Binding: Paperback
• 417 pages

From the Publisher
Demonstrates how neural networks can be used to aid in the solution of digital signal processing (DSP) or imaging problems. A large section is devoted to the design and training of complex-domain multiple-layer feedforward networks (MLFNs)--all essential equations are presented and justified. Reviews the most popular signal- and image-processing algorithms, emphasizing those that are particularly suitable for union to complex-domain neural networks. Features a wide variety of problems for which complex-domain networks significantly outperform their real-domain counterparts. The accompanying disk includes complete source code for algorithms discussed with full source for program examples.

Customer Reviews

Best C++ Source Code available for MLFN.
"The principal focus of this book is the multiple-layer feedforward network (MLFN)." This network is the most common and most used type of neural network. I have all of Masters books and this is his best presentation and source code for the MLFN. Chaper 2. of the book is about a complex number version of the MLFN, but it also includes the common real number versions. Other chapters on Data Preparation, Frequency-Domain, Time/Frequency Localization (Gabor Transform, Fourier Transform, Morlet Wavelets) and applications.

The C++ source code is easy to compile, understand and use. Includes simulated annealing, conjugate gradient algorithms and hybrid learning methods.