Τίτλος:
Multi-input bio-inspired weights and structure determination neuronet with applications in European Central Bank publications
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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)
Συγγραφείς:
Simos, T.E.
Katsikis, V.N.
Mourtas, S.D.
Περιοδικό:
Mathematics and Computers in Simulation
Λέξεις-κλειδιά:
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
DOI:
10.1016/j.matcom.2021.11.007
