Abstract
In this paper, to determine the optimal parameters of the product acceptance process under parametric uncertainty of underlying models, a new intelligent technique for optimization of product acceptance process on the basis of misclassification probability is proposed. It allows one to take into account all possible situations that may occur when it is necessary to optimize the product acceptance process. The technique is based on the pivotal quantity averaging approach (PQAA) which allows one to eliminate the unknown parameters from the problem and to use available statistical information as completely as possible. It is conceptually simple and easy to use. One of the most important features of the proposed new intelligent technique for optimization of product acceptance process on the basis of misclassification probability is its great generality, enabling one to optimize diverse problems within one unified framework. To illustrate the proposed technique, the case of log-location-scale distributions is considered under parametric uncertainty.
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Nechval, N., Berzins, G., Nechval, K. (2019). A Novel Intelligent Technique for Product Acceptance Process Optimization on the Basis of Misclassification Probability in the Case of Log-Location-Scale Distributions. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_68
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