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- W113647686 abstract "We use particle swarm optimization (PSO) to train the functional link artificial neural network (FLANN) for software effort prediction. The combined framework is known as PSO-FLANN. This framework exploits the global classification capability of PSO and FLANN’s complex nonlinear mapping between its input and output pattern space by using functional expansion. The Chebyshev polynomial has been used as choice of expansion in FLANN to exhaustively study the performance in three real time datasets. The simulation results show that it not only deals efficiently with noisy data but achieves improved accuracy in prediction.KeywordsSoftware cost estimationParticle Swarm optimizationFunctional Link Artificial Neural Networks" @default.
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- W113647686 date "2013-01-01" @default.
- W113647686 modified "2023-10-16" @default.
- W113647686 title "A Particle Swarm Optimized Functional Link Artificial Neural Network (PSO-FLANN) in Software Cost Estimation" @default.
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- W113647686 doi "https://doi.org/10.1007/978-3-642-35314-7_8" @default.
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