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This work aims to explore and improve the problems of the general optimized algorithm. General optimized algorithm is the mainstream method these days because it is suitable for a wide array of problems. Among all the options, genetic algorithm, particle swarm algorithm, and simulated annealing methods are the ones most commonly used to deal with difficult but regular fitness functions. Yet each method...
Focusing on the deficiencies of the existing IRT parameter estimation algorithm, the Resilient Back propagation algorithm and variable learning rate learning algorithm are used in the basis of artificial neural network algorithm to improve the network convergence speed, and the genetic algorithm is used to solve the local minima problem, then the improved BP algorithm is generated. Finally, the standard...
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