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Multi-parallel Extreme Learning Machine with Excitatory and Inhibitory Neurons for Regression

机译：具有兴奋性和抑制性神经元的多平行极端学习机，用于回归

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摘要

Compared with traditional neural networks, extreme learning machine (ELM) shows outstanding performances on speed and computation. Aiming at the problems that ELM needs more hidden layer neurons and meaningful features of data sometimes are sacrificed in order to improve the training speed, a novelty network multi-parallel extreme learning machine with excitatory and inhibitory neurons (MEI-ELM) is proposed based on the idea of biological neurons. In MEI-ELM, (1) A parallel system is introduced to make it more compact and reduce the number of hidden layer neurons. (2) The property of excitatory and inhibitory of biological neuronal for data processing is introduced to improve its performance. Through applying MEI-ELM, ELM, Fast Learning Network (FLN) and Fast Learning Network with Parallel Layer Perceptrons (PLP-FLN) to 11 classical regression problems, it can be obtained that MEI-ELM performs much better than the other methods in generalization and stability.
机译：与传统的神经网络相比，极端学习机（ELM）显示出速度和计算的出色性能。针对榆树需要更多隐藏层神经元的问题，有时牺牲了数据的有意义的数据，以提高训练速度，提出了一种基于兴奋性和抑制性神经元（Mei-Elm）的新型网络多平行极端学习机（Mei-Elm）。生物神经元的想法。在Mei-Elm中，引入并行系统以使其更紧凑，减少隐藏层神经元的数量。 （2）引入了数据处理生物神经元的兴奋性和抑制性的性质，提高了其性能。通过应用Mei-Elm，ELM，快速学习网络（FLN）和快速学习网络，并行层Perceptrons（PLP-FLN）至11个经典回归问题，可以获得Mei-Elm比泛化中的其他方法更好地表现得多和稳定。

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