@article{3023966,
    title = "Multi-input bio-inspired weights and structure determination neuronet with applications in European Central Bank publications",
    author = "Simos, T.E. and Katsikis, V.N. and Mourtas, S.D.",
    journal = "Mathematics and Computers in Simulation",
    year = "2022",
    volume = "193",
    pages = "451-465",
    publisher = "Elsevier B.V.",
    issn = "0378-4754",
    doi = "10.1016/j.matcom.2021.11.007",
    keywords = "Biomimetics;  Combinatorial optimization;  MATLAB;  Neurons;  Nonlinear programming;  Structural optimization, Beetle antenna search;  Determination algorithm;  European Central Bank;  Feed-forward neuronet;  Multiinput;  Neural-networks;  Neuronet model;  Structure determination;  Weight determination;  Weight-and-structure-determination neuronet, Neural networks",
    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. © 2021 International Association for Mathematics and Computers in Simulation (IMACS)"
}