@article {27086,
	title = {Multi-input bio-inspired weights and structure determination neuronet with applications in European Central Bank publications},
	journal = {Mathematics and Computers in Simulation},
	year = {2021},
	month = {2021},
	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{\textquoteright}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.},
	keywords = {Beetle antennae search, Finance, Neural networks, Nonlinear programming, WASD neuronet},
	isbn = {0378-4754},
	url = {https://www.sciencedirect.com/science/article/pii/S0378475421004031},
	author = {Theodore E. Simos and Vasilios N. Katsikis and Spyridon D. Mourtas}
}
