By Xue-Quan Xu, Xian-Bin Wen, Yue-Qing Li, Jin-Juan Quan (auth.), De-Shuang Huang, Laurent Heutte, Marco Loog (eds.)
The overseas convention on clever Computing (ICIC) used to be shaped to supply an annual discussion board devoted to the rising and demanding themes in synthetic intelligence, computing device studying, bioinformatics, and computational biology, and so forth. It goals to convey - gether researchers and practitioners from either academia and to percentage rules, difficulties and suggestions concerning the multifaceted facets of clever computing. ICIC 2007, held in Qingdao, China, August 21-24, 2007, constituted the 3rd - ternational convention on clever Computing. It equipped upon the good fortune of ICIC 2006 and ICIC 2005 held in Kunming and Hefei, China, 2006 and 2005, respectively. This 12 months, the convention focused frequently at the theories and methodologies in addition to the rising functions of clever computing. Its goal was once to unify the image of up to date clever computing suggestions as an crucial idea that highlights the traits in complex computational intelligence and bridges theoretical study with purposes. as a result, the subject for this convention used to be “Advanced clever Computing know-how and Applications”. Papers concentrating on this subject matter have been solicited, addressing theories, methodologies, and functions in technology and technology.
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Additional resources for Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence: Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007. Proceedings
75 x)e − x2 2 (4) By substitution (3) to (4), the following formula is drawn. 75 ⋅ ⎜ ⎜ ⎝ ⎞⎞ ⎟⎟ ⎟ e ⎟ ⎠⎠ ⎛ x −t j ⎜ ⎜ sj −⎝ 2 ⎞ ⎟ ⎟ ⎠ 2 ( j = 1, 2," , l ) (5) Layer 3 is an output layer that sums the production of output value of the hidden nodes and the output connection weight w2 between the hidden nodes and the output nodes. The output formula of wavelet network is expressed as follows: l y = ∑ w2 j ϕ j (6) j =1 From the theory above, the wavelet network formula can be deduced as follows: ⎛ ∑n w1 x − t ij i j l ⎜ y( x) = ∑ w2 j s j ψ ⎜ i =1 j =1 sj ⎜ ⎝ 1 − 2 ⎞ ⎟ ⎟ ⎟ ⎠ (7) Two key problems in designing of WNN are how to determine WNN architecture, what learning algorithm can be effectively used for training the WNN, and how to find the proper orthogonal or nonorthogonal wavelet basis.
References 1. : A Methodology for Detection and Estimation of Software Aging. Int. Symp. On Software Reliability Engineering, ISSRE (1998) 2. , Fulton. N: Software Rejuvenation: Analysis, Module and Applications. IEEE Int. Symposium on Fault Tolerant Computing, FTCS 25 (1995). 3. : Measurement of failure rate in widely distributed software. In Proc. of 25th IEEE Intl. Symposium on Fault-Tolerant Computing, Pasadena, CA (1995) 424–433 4. , Iyer, R. : Dependability Measurement Modeling of a Multicomputer System.
Calculate the contribution rate of each component respectively. If the accumulation contribution rate of the first m components is more than 85 percent, the first m factors x1,x2,x3,…,xm are principal components. After dealing with principal component analysis, response time and throughput are selected as impact factors of software aging. Thus, formula (1) can be reduced to formula as follow: y = f ( x1 , x2 ) (2) where y denotes the amount of JVM heap memory usage, x1 is response time and x2 is throughput amount.