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Signal and Image Processing with Neural Networks: A C++ Sourcebook, by Timothy Masters
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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.
- Sales Rank: #399702 in Books
- Published on: 1994-07-25
- Original language: English
- Number of items: 1
- Dimensions: 9.17" h x .91" w x 7.44" l, .0 pounds
- 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.
Most helpful customer reviews
14 of 14 people found the following review helpful.
Best C++ Source Code available for MLFN.
By J. Wrenholt
"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.
See all 1 customer reviews...
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