By Zuoling Nie, Yuhai Su, Chong-jin Wang (auth.), Zhenyu Du (eds.)
2012 foreign convention of Intelligence Computation and Evolutionary Computation (ICEC 2012) is hung on July 7, 2012 in Wuhan, China. This convention is backed by way of info expertise & business Engineering learn middle.
ICEC 2012 is a discussion board for presentation of recent examine result of clever computation and evolutionary computation. Cross-fertilization of clever computation, evolutionary computation, evolvable and newly rising applied sciences is strongly inspired. The discussion board goals to compile researchers, builders, and clients from worldwide in either and academia for sharing state-of-art effects, for exploring new parts of study and improvement, and to debate rising concerns dealing with clever computation and evolutionary computation.
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Additional resources for Intelligence Computation and Evolutionary Computation: Results of 2012 International Conference of Intelligence Computation and Evolutionary Computation ICEC 2012 Held July 7, 2012 in Wuhan, China
Usually, the zero-bias can be denoted as B0 = f (T , t ) by taking temperature and time into account synthetically, where T denotes temperature factor and t denotes time factor. But through long-time tests, we can find that the influence of time factor to zero-bias is little. So the zero-bias can be denoted as B0 = f (T ) for simplify. The purpose of this paper is to learn the mapping relationship f (⋅) using GPR. The process is shown in the following: (1) Build the training set D = (T , B0 ) according to the experiment data (Ti , B0i ) , where i = 1,2,, n .
It is completely specified by its mean function and covariance function as m(x) = E[ f (x)] k (x, x′) = Ε[( f (x) − m(x))( f (x′) − m(x′))] (1) where x, x′ ∈ R d denotes any random variable. So we can define the Gaussian process as f (x) ~ GP( m( x), k ( x, x′)) . For notational simplicity, we usually pretreat the data to make the mean function to be zero. For linear regression problems, we can define the model as f (x) = xT w , y = f (x) + ε (2) where x denotes an input vector, w denotes a weights vector of the linear model, f denotes the function value and y denotes the observed value which is polluted by additivity noise.
For reducing the data redundancy, this paper makes full use of the mature SQL query optimization technique of relationship database and connects these to generate a fault data sheet, which contains the failure properties of all levels. 5 The Analysis of Obtaining the Failure Knowledge It can get the following three kinds of knowledge through the mining of the above fault database, which based on the association rule, such as the knowledge about the fault reason and phenomenon, the fault knowledge of fault equipment and the knowledge about the choice of Spare Parts.
Intelligence Computation and Evolutionary Computation: Results of 2012 International Conference of Intelligence Computation and Evolutionary Computation ICEC 2012 Held July 7, 2012 in Wuhan, China by Zuoling Nie, Yuhai Su, Chong-jin Wang (auth.), Zhenyu Du (eds.)