Details
| Original language | English |
|---|---|
| Pages (from-to) | 535-548 |
| Number of pages | 14 |
| Journal | Cybernetics and Systems |
| Volume | 38 |
| Issue number | 5-6 |
| Publication status | Published - 7 Jun 2007 |
Abstract
In this article we evaluate and compare diverse methodologies for designing low-noise tread profiles. Finding a low noise tread profile under given constraints can be described as a search in search space which is typically of the order of a 50- to 70-dimensional vector space. A complete search for the optimal tread profile is not possible even with today's computers. Thus in this work we compare the feasibility of three classes of algorithms for tread profile construction. First, we discuss approaches of speeding up the generation and analysis of tread profiles. Second we use two algorithms for iterative construction of large tread profiles out of several smaller tread profiles known to be of good quality. One of these algorithms is based on Neural Networks. Third, we evaluate heuristic optimization algorithms such as Genetic Algorithms and Simulated Annealing. Last we compare suitability and efficiency of our approaches.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Artificial Intelligence
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In: Cybernetics and Systems, Vol. 38, No. 5-6, 07.06.2007, p. 535-548.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Neural networks and optimization algorithms applied for construction of low noise tread profiles
AU - Becker, Matthias
AU - Szczerbicka, Helena
AU - Thomas, Michael
PY - 2007/6/7
Y1 - 2007/6/7
N2 - In this article we evaluate and compare diverse methodologies for designing low-noise tread profiles. Finding a low noise tread profile under given constraints can be described as a search in search space which is typically of the order of a 50- to 70-dimensional vector space. A complete search for the optimal tread profile is not possible even with today's computers. Thus in this work we compare the feasibility of three classes of algorithms for tread profile construction. First, we discuss approaches of speeding up the generation and analysis of tread profiles. Second we use two algorithms for iterative construction of large tread profiles out of several smaller tread profiles known to be of good quality. One of these algorithms is based on Neural Networks. Third, we evaluate heuristic optimization algorithms such as Genetic Algorithms and Simulated Annealing. Last we compare suitability and efficiency of our approaches.
AB - In this article we evaluate and compare diverse methodologies for designing low-noise tread profiles. Finding a low noise tread profile under given constraints can be described as a search in search space which is typically of the order of a 50- to 70-dimensional vector space. A complete search for the optimal tread profile is not possible even with today's computers. Thus in this work we compare the feasibility of three classes of algorithms for tread profile construction. First, we discuss approaches of speeding up the generation and analysis of tread profiles. Second we use two algorithms for iterative construction of large tread profiles out of several smaller tread profiles known to be of good quality. One of these algorithms is based on Neural Networks. Third, we evaluate heuristic optimization algorithms such as Genetic Algorithms and Simulated Annealing. Last we compare suitability and efficiency of our approaches.
UR - http://www.scopus.com/inward/record.url?scp=34250172727&partnerID=8YFLogxK
U2 - 10.1080/01969720701345993
DO - 10.1080/01969720701345993
M3 - Article
AN - SCOPUS:34250172727
VL - 38
SP - 535
EP - 548
JO - Cybernetics and Systems
JF - Cybernetics and Systems
SN - 0196-9722
IS - 5-6
ER -