Early Warning Against Insolvency of Enterprises Based on a Self-learning Artificial Neural Network of the SOM Type

Migdał-Najman, Kamila; Najman, Krzysztof; Antonowicz, Paweł

doi:10.1007/978-3-030-21274-2_12

Cited by 1 publication

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“…? @ = ' A * (7) where ' A shows the generalized Moore-Penrose inverse of the outputs of the neurons in the hidden layer.…”

Section: Algebraic Learning Machine (Alm)mentioning

confidence: 99%

“…The adaptation of ANN means determining the optimal values of its free parameters and this determining stage is called training. In order to increase the adapting capability of ANN, various ANN structures (e.g., feed-forward, recurrent, single hidden layer feed-forward) and ANN training methods (gradient-based methods, randomized methods) have been proposed [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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A Single Hidden Layer Artificial Neural Network Model that Employs Algebraic Neurons: Algebraic Learning Machine

Ertuğrul

2021

Preprint

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Artificial neural networks (ANN) have been employed successfully because of their high modeling capability. Many versions of the ANN have been proposed to increase the modeling ability. Since ANN based on the biological neural network system, the only mathematical operation is summation or subtraction (while the coefficients are negative). This research was done to investigate the application of other mathematical operations, which are multiplication, division, logarithm, and exponential, in nodes. Based on this fact, a novel a single hidden layer feed-forward artificial neural network (SLFN) model, which was called was algebraic learning machine (ALM), was proposed. The proposed ALM was evaluated and validated with 60 different benchmark datasets. Obtained results were compared with results obtained by each of the extreme learning machine (ELM), randomized artificial neural network, and random vector functional link, and back-propagation trained SLFN methods. Achieved results show that the proposed method is successful enough to be employed in classification and regression.

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“…? @ = ' A * (7) where ' A shows the generalized Moore-Penrose inverse of the outputs of the neurons in the hidden layer.…”

Section: Algebraic Learning Machine (Alm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Single Hidden Layer Artificial Neural Network Model that Employs Algebraic Neurons: Algebraic Learning Machine

Ertuğrul

2021

Preprint

View full text Add to dashboard Cite

show abstract

Early Warning Against Insolvency of Enterprises Based on a Self-learning Artificial Neural Network of the SOM Type

Cited by 1 publication

References 13 publications

A Single Hidden Layer Artificial Neural Network Model that Employs Algebraic Neurons: Algebraic Learning Machine

A Single Hidden Layer Artificial Neural Network Model that Employs Algebraic Neurons: Algebraic Learning Machine

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