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Multi-input bio-inspired weights and structure determination neuronet with applications in European Central Bank publications

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  • Simos, Theodore E.
  • Katsikis, Vasilios N.
  • Mourtas, Spyridon D.

Abstract

This paper introduces a 3-layer feed-forward neuronet model, trained by novel beetle antennae search weights-and-structure-determination (BASWASD) algorithm. On the one hand, the beetle antennae search (BAS) algorithm is a memetic meta-heuristic optimization algorithm capable of solving combinatorial optimization problems. On the other hand, neuronets trained by a weights-and-structure-determination (WASD) algorithm are known to resolve the shortcomings of traditional back-propagation neuronets, including slow speed of training and local minimum. Combining the BAS and WASD algorithms, a novel BASWASD algorithm is created for training neuronets, and a multi-input BASWASD neuronet (MI-BASWASDN) model is introduced. Using a power sigmoid activation function and while managing the model fitting and validation, the BASWASD algorithm finds the optimal weights and structure of the MI-BASWASDN. Four financial datasets, taken from the European Central Bank publications, validate and demonstrate the MI-BASWASDN model’s outstanding learning and predicting performance. Also included is a comparison of the MI-BASWASDN model to three other well-performing neural network models, as well as a MATLAB kit that is publicly available on GitHub to promote and support this research.

Suggested Citation

  • Simos, Theodore E. & Katsikis, Vasilios N. & Mourtas, Spyridon D., 2022. "Multi-input bio-inspired weights and structure determination neuronet with applications in European Central Bank publications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 451-465.
    • Handle: RePEc:eee:matcom:v:193:y:2022:i:c:p:451-465
    DOI: 10.1016/j.matcom.2021.11.007
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    References listed on IDEAS

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    1. Katsikis, Vasilios N. & Mourtas, Spyridon D. & Stanimirović, Predrag S. & Li, Shuai & Cao, Xinwei, 2020. "Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS)," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    2. Di Piazza, A. & Di Piazza, M.C. & La Tona, G. & Luna, M., 2021. "An artificial neural network-based forecasting model of energy-related time series for electrical grid management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 294-305.
    3. Valueva, M.V. & Nagornov, N.N. & Lyakhov, P.A. & Valuev, G.V. & Chervyakov, N.I., 2020. "Application of the residue number system to reduce hardware costs of the convolutional neural network implementation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 177(C), pages 232-243.
    4. Vasilios N. Katsikis & Spyridon D. Mourtas & Predrag S. Stanimirović & Shuai Li & Xinwei Cao, 2021. "Time-Varying Mean-Variance Portfolio Selection under Transaction Costs and Cardinality Constraint Problem via Beetle Antennae Search Algorithm (BAS)," SN Operations Research Forum, Springer, vol. 2(2), pages 1-26, June.
    5. Mansoor, Muhammad & Grimaccia, Francesco & Leva, Sonia & Mussetta, Marco, 2021. "Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 282-293.
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    Cited by:

    1. Hadeel Alharbi & Obaid Alshammari & Houssem Jerbi & Theodore E. Simos & Vasilios N. Katsikis & Spyridon D. Mourtas & Romanos D. Sahas, 2023. "A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition," Mathematics, MDPI, vol. 11(6), pages 1-17, March.
    2. Dimitris Lagios & Spyridon D. Mourtas & Panagiotis Zervas & Giannis Tzimas, 2023. "A Weights Direct Determination Neural Network for International Standard Classification of Occupations," Mathematics, MDPI, vol. 11(3), pages 1-14, January.
    3. Predrag S. Stanimirović & Spyridon D. Mourtas & Vasilios N. Katsikis & Lev A. Kazakovtsev & Vladimir N. Krutikov, 2022. "Recurrent Neural Network Models Based on Optimization Methods," Mathematics, MDPI, vol. 10(22), pages 1-26, November.

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