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Toward the border between neural and Markovian paradigms
P Wilinski1, B Solaiman, A Hillion
1Ecole Nat. Superieure des Telecommun. de Bretagne, Brest.
Summary
This paper introduces hybrid algorithms that merge Markovian and neural paradigms in signal processing. A novel classification system is proposed for these advanced signal processing techniques.
Area of Science:
- Computer Science
- Artificial Intelligence
- Signal Processing
Background:
- Modern signal processing increasingly utilizes hybrid algorithms.
- Existing methods often fall strictly into Markovian or neural categories.
Purpose of the Study:
- To provide an overview of signal processing algorithms bridging Markovian and neural paradigms.
- To propose a systematic classification for these hybrid algorithms.
Main Methods:
- Describing four distinct classes of hybrid Markovian-neural models.
- Focusing on structural and algorithmic modifications.
- Presenting key mathematical formulas without specific applications.
Main Results:
- A new classification system for hybrid signal processing algorithms.
- Detailed descriptions of four classes: integrated, cooperative, unified, and general hybrid models.
- Distinction between structural and algorithmic modifications in hybrid designs.
Conclusions:
- Hybrid algorithms represent a significant trend in signal processing.
- The proposed classification offers a structured understanding of these combined models.
- Further details are available in the provided references.
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