{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T07:01:10Z","timestamp":1760598070954,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T00:00:00Z","timestamp":1629417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The use of gradient descent training to optimize the performance of a rule-fact network expert system via updating the network\u2019s rule weightings was previously demonstrated. Along with this, four training techniques were proposed: two used a single path for optimization and two use multiple paths. The performance of the single path techniques was previously evaluated under a variety of experimental conditions. The multiple path techniques, when compared, outperformed the single path ones; however, these techniques were not evaluated with different network types, training velocities or training levels. This paper considers the multi-path techniques under a similar variety of experimental conditions to the prior assessment of the single-path techniques and demonstrates their effectiveness under multiple operating conditions.<\/jats:p>","DOI":"10.3390\/computers10080103","type":"journal-article","created":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T08:44:45Z","timestamp":1629449085000},"page":"103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Assessment of Gradient Descent Trained Rule-Fact Network Expert System Multi-Path Training Technique Performance"],"prefix":"10.3390","volume":"10","author":[{"given":"Jeremy","family":"Straub","sequence":"first","affiliation":[{"name":"Department of Computer Science, North Dakota State University, Fargo, ND 58108, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107275","DOI":"10.1016\/j.knosys.2021.107275","article-title":"Expert system gradient descent style training: Development of a defensible artificial intelligence technique","volume":"228","author":"Straub","year":"2021","journal-title":"Knowl. 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