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Title: ELM_PSO-master Download
 Description: It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can p
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FilenameSizeDate
ELM_PSO-master 0  2018-11-27

ELM_PSO-master\CPSO 0  2018-11-27

ELM_PSO-master\CPSO\CPSO.py 6683  2018-11-27

ELM_PSO-master\CPSO\Particula.py 2040  2018-11-27

ELM_PSO-master\CPSO\SolveSystemFitness.py 3410  2018-11-27

ELM_PSO-master\CPSO\__pycache__ 0  2018-11-27

ELM_PSO-master\CPSO\__pycache__\CPSO.cpython-37.pyc 4444  2018-11-27

ELM_PSO-master\CPSO\__pycache__\Particula.cpython-37.pyc 2703  2018-11-27

ELM_PSO-master\CPSO\__pycache__\SolveSystemFitness.cpython-37.pyc 4637  2018-11-27

ELM_PSO-master\Candle.py 1073  2018-11-27

ELM_PSO-master\IO.py 12284  2018-11-27

ELM_PSO-master\experimento_sem_open.py 1653  2018-11-27

ELM_PSO-master\func_aux_pso.py 6621  2018-11-27

ELM_PSO-master\func_auxiliares.py 4184  2018-11-27

ELM_PSO-master\kelm.py 1522  2018-11-27

ELM_PSO-master\main_ind.py 2949  2018-11-27

ELM_PSO-master\main_semanal.py 3168  2018-11-27

ELM_PSO-master\nova_main.py 3422  2018-11-27

ELM_PSO-master\predicao_ind.py 3203  2018-11-27

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