模型 Resources

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Application of genetic algorithms for power grid planning, specifically in transmission network optimization. This research proposes a genetic algorithm-based transmission network planning model that minimizes the combined cost of new line investments and annual system operational expenses. The mathematical model incorporates N-1 contingency analysis to ensure planning scheme rationality, demonstrated through optimization of the Garver-6 bus system with implementation of chromosome encoding and fitness evaluation methods.

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Decomposing multi-class problems into multiple binary-class subproblems is a common approach for multi-class classification. Traditional One-Against-All (OAA) decomposition relies more on individual classifier accuracy than diversity. This paper introduces a neural network ensemble model suitable for multi-class problems, implemented with ensemble learning techniques. The core architecture consists of a binary classifier using OAA decomposition and a complementary multi-class classifier. Validation shows this model achieves higher accuracy than classical ensemble algorithms on multi-class datasets, with advantages in reduced storage requirements and computational time. The implementation involves parallel training of base classifiers and intelligent voting mechanisms for final decision fusion.

MATLAB 209 views Tagged
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