Erkaymaz, OkanOzer, MahmutYumusak, Nejat2024-09-292024-09-2920141300-06321303-6203https://doi.org/10.3906/elk-1202-89https://hdl.handle.net/20.500.14619/7480Since feed-forward artificial neural networks (FFANNs) are the most widely used models to solve real-life problems, many studies have focused on improving their learning performances by changing the network architecture and learning algorithms. On the other hand, recently, small-world network topology has been shown to meet the characteristics of real-life problems. Therefore, in this study, instead of focusing on the performance of the conventional FFANNs, we investigated how real-life problems can be solved by a FFANN with small-world topology. Therefore, we considered 2 real-life problems: estimating the thermal performance of solar air collectors and predicting the modulus of rupture values of oriented strand boards. We used the FFANN with small-world topology to solve both problems and compared the results with those of a conventional FFANN with zero rewiring. In addition, we investigated whether there was statistically significant difference between the regular FFANN and small-world FFANN model. Our results show that there exists an optimal rewiring number within the small-world topology that warrants the best performance for both problems.eninfo:eu-repo/semantics/openAccessSmall-world networkfeed-forward artificial neural networkrewiringnetwork topologyArticle10.3906/elk-1202-892-s2.0-848978582277183Q370822WOS:000332942900015Q4