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data-driven randomized learning of Feedforward Neural Networks

Data-Driven Randomized Learning of Feedforward Neural Networ...

International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Dudek, Grzegorz Czestochowa Tech Univ Dept Elect Engn Czestochowa Poland

ISBN: (纸本)9781728169262

randomized methods of neural network learning suffer from a problem with the generation of random parameters as they are difficult to set optimally to obtain a good projection space. The standard method draws the parameters from a fixed interval which is independent of the data scope and activation function type. This does not lead to good results in the approximation of the strongly nonlinear functions. In this work, a method which adjusts the random parameters, representing the slopes and positions of the sigmoids, to the target function features is proposed. The method randomly selects the input space regions, places the sigmoids in these regions and then adjusts the sigmoid slopes to the local fluctuations of the target function. A bias-variance tradeoff is controlled by the region size. This brings very good results in the approximation of the complex target functions when compared to the standard fixed interval method and other methods recently proposed in the literature.

关键词： data-driven randomized learning feedforward neural networks neural networks with random hidden nodes randomized learning algorithms

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A constructive approach to data-driven randomized learning for feedforward neural networks

引用

APPLIED SOFT COMPUTING 2021年 112卷 107797-107797页

作者： Dudek, Grzegorz Czestochowa Tech Univ Fac Elect Engn 17 Armii Krajowej Ave PL-42200 Czestochowa Poland

There is an issue with the way in which feedforward neural networks with random hidden nodes generate random parameters in order to obtain a good projection space. Typically, random weights and biases are both drawn from the same interval, which is misguided as they have different functions. Recently, more sophisticated methods of random parameters generation have been developed, such as the data-driven approach, where the sigmoids are placed in randomly selected regions of the input space and then their slopes are adjusted to the local fluctuations of the target function. In this work, we propose a new constructive data-driven method that builds iteratively the network architecture. This method successively generates new candidate hidden nodes and accepts them when the training error falls significantly. The threshold of acceptance is adapted throughout training, accepting at the beginning of the training process only those nodes which lead to the largest reductions in error. In the next stages, the threshold is successively reduced to accept only those nodes which model the target function details more accurately. This leads to a more compact network architecture, as it includes only "significant" nodes. It is worth noting that redundant, random nodes, which are usually generated by existing randomized learning methods, are not accepted by the proposed method. We empirically compared our approach with several alternative methods, including its predecessor, competitive randomized learning solutions, a gradient-based network and a generalized additive model. We found that our proposed approach outperformed its competitors in terms of fitting accuracy. (C) 2021 Elsevier B.V. All rights reserved.

关键词： data-driven randomized learning Feedforward neural networks Neural networks with random hidden nodes randomized learning algorithms

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