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Volumn 59, Issue 3, 1999, Pages 2872-2879

When noise decreases deterministic diffusion

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Indexed keywords


EID: 0001140979     PISSN: 1063651X     EISSN: None     Source Type: Journal    
DOI: 10.1103/PhysRevE.59.2872     Document Type: Article
Times cited : (40)

References (21)
  • 17
    • 0347912061 scopus 로고
    • references therein
    • J.L. Skinner, J. Phys. Chem. 98, 2503 (1994), and references therein.
    • (1994) J. Phys. Chem. , vol.98 , pp. 2503
    • Skinner, J.L.1
  • 19
    • 85037216956 scopus 로고    scopus 로고
    • Let (Formula presented) be the average gain of iteration steps by a single negative noise event acting in (Formula presented) The probability for (Formula presented) negative perturbation is (Formula presented) since it is caused by a noise sequence (Formula presented) Thus, the total gain of iterations by negative noise events is given by (Formula presented) e.g., it is just the gain of iterations for one single negative perturbation
    • Let (Formula presented) be the average gain of iteration steps by a single negative noise event acting in (Formula presented) The probability for (Formula presented) negative perturbation is (Formula presented) since it is caused by a noise sequence (Formula presented) Thus, the total gain of iterations by negative noise events is given by (Formula presented) e.g., it is just the gain of iterations for one single negative perturbation.
  • 20
    • 85037230131 scopus 로고    scopus 로고
    • To be exact, for maximizing the mean residence time (Formula presented) the product of (Formula presented) and (Formula presented) should be optimized with respect to (Formula presented) However, a usually strong decrease of (Formula presented) near (Formula presented) dominates the optimization, which is why (Formula presented) is still a good approximation
    • To be exact, for maximizing the mean residence time (Formula presented) the product of (Formula presented) and (Formula presented) should be optimized with respect to (Formula presented) However, a usually strong decrease of (Formula presented) near (Formula presented) dominates the optimization, which is why (Formula presented) is still a good approximation.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.