• Computers and Concrete
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• v.27 no.1
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• pp.63-71
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• 2021

In this study, a new stopping criterion, called "backward controlled stopping criterion" (BCSC), was proposed to be used in Genetic Algorithms. In the study, the available stopping citeria; adaptive stopping citerion, evolution time, fitness threshold, fitness convergence, population convergence, gene convergence, and developed stopping criterion were applied to the following four comparison problems; high strength concrete mix design, pre-stressed precast concrete beam, travelling salesman and reinforced concrete deep beam problems. When completed the analysis, the developed stopping criterion was found to be more accomplished than available criteria, and was able to research a much larger area in the space design supplying higher fitness values.

• Journal of the Korean Institute of Gas
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• v.3 no.1
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• pp.64-74
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• 1999

Using the steady state gains, an appropriate criterion used for the interaction analysis among variables and the loop pairing is suggested. Based upon the suggested criterion derived from the derivative relation of implicit function, the SISO pairing which has minimum interaction among control system variables and good control performance can be determined. The relative effect among diagonal gains and off-diagona gains, which was not considered in other criteria, can be explained deterministically Also, the criterion can be easily applied to partial MIMO pairing. This criterion was applied to several examples to illustrate its usefulness in finding the feasible SISO pairing and MIMO pairing

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.867-875
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• 2003

This paper concerns continuous density HMM topology optimization. There have been several researches for HMM topology optimization. BIC (Bayesian Information Criterion) is one of the well known optimization criteria, which assumes statistically well behaved homogeneous model parameters. HMMs, however, are composed of several different kind of parameters to accommodate complex topology, thus BIC's assumption does not hold true for HMMs. Even though BIC reduced the total number of parameters of HMMs, it could not improve the recognition rates. In this paper, we proposed two new model selection criteria, HBIC (HMM-oriented BIC) and BIC_Anti. The former is proposed to improve BIC by estimating model priors separately. The latter is to combine BIC and anti-likelihood to accelerate discrimination power of HMMs. We performed some comparative research on couple of model selection criteria for online handwriting data recognition. We got better recognition results with fewer number of parameters.