Abstract
Fuzzy logic inspires from the non-deterministic behaviour of human brain computations. The fusion of neural networks and fuzzy logic such as neuro-fuzzy architectures is natural, as both represent elementary inspiration from brain computations involving learning, adaptation and ability to tolerate noise. This chapter focuses on neuro-fuzzy and alike solutions for machine learning from perspective of functionality, architectures and applications.
| Original language | English |
|---|---|
| Title of host publication | Modeling and Optimization in Science and Technologies |
| Publisher | Springer Verlag |
| Pages | 195-213 |
| Number of pages | 19 |
| DOIs | |
| Publication status | Published - Jan 1 2020 |
Publication series
| Name | Modeling and Optimization in Science and Technologies |
|---|---|
| Volume | 14 |
| ISSN (Print) | 2196-7326 |
| ISSN (Electronic) | 2196-7334 |
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ASJC Scopus subject areas
- Modelling and Simulation
- Medical Assisting and Transcription
- Applied Mathematics
Cite this
Deep neuro-fuzzy architectures. / Dorzhigulov, Anuar; James Pappachen, Alex.
Modeling and Optimization in Science and Technologies. Springer Verlag, 2020. p. 195-213 (Modeling and Optimization in Science and Technologies; Vol. 14).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Deep neuro-fuzzy architectures
AU - Dorzhigulov, Anuar
AU - James Pappachen, Alex
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Fuzzy logic inspires from the non-deterministic behaviour of human brain computations. The fusion of neural networks and fuzzy logic such as neuro-fuzzy architectures is natural, as both represent elementary inspiration from brain computations involving learning, adaptation and ability to tolerate noise. This chapter focuses on neuro-fuzzy and alike solutions for machine learning from perspective of functionality, architectures and applications.
AB - Fuzzy logic inspires from the non-deterministic behaviour of human brain computations. The fusion of neural networks and fuzzy logic such as neuro-fuzzy architectures is natural, as both represent elementary inspiration from brain computations involving learning, adaptation and ability to tolerate noise. This chapter focuses on neuro-fuzzy and alike solutions for machine learning from perspective of functionality, architectures and applications.
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U2 - 10.1007/978-3-030-14524-8_15
DO - 10.1007/978-3-030-14524-8_15
M3 - Chapter
AN - SCOPUS:85064762401
T3 - Modeling and Optimization in Science and Technologies
SP - 195
EP - 213
BT - Modeling and Optimization in Science and Technologies
PB - Springer Verlag
ER -