Neural Networks

Traditional DSP is based on algorithms, changing data from one form to another through step-bystep
procedures. Most of these techniques also need parameters to operate. For example:
recursive filters use recursion coefficients, feature detection can be implemented by correlation
and thresholds, an image display depends on the brightness and contrast settings, etc.
Algorithms describe what is to be done, while parameters provide a benchmark to judge the data.
The proper selection of parameters is often more important than the algorithm itself. Neural
networks take this idea to the extreme by using very simple algorithms, but many highly
optimized parameters. This is a revolutionary departure from the traditional mainstays of science
and engineering: mathematical logic and theorizing followed by experimentation. Neural networks
replace these problem solving strategies with trial & error, pragmatic solutions, and a "this works
better than that" methodology. This chapter presents a variety of issues regarding parameter
selection in both neural networks and more traditional DSP algorithms.
CHAPTER
Neural Networks (and more!)
26

Traditional DSP is based on algorithms, changing data from one form to another through step-by-
step procedures. Most of these techniques also need parameters to operate. For example:
recursive filters use recursion coefficients, feature detection can be implemented by correlation
and thresholds , an image display depends on the brightness and contrast settings, etc.
Algorithms describe what is to be done, while parameters provide a benchmark to judge the data.
The proper selection of parameters is often more important than the algorithm itself. Neural
networks take this idea to the extreme by using very simple algorithms, but many highly
optimized parameters. This is a revolutionary departure from the traditional mai