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The intention of training a particular artificial neural network (ANN) is to obtain the finest weight load. With this protocol, a particular weight is taken into account as feedback, which is optimized by means of the hybrid AGCS protocol. In the beginning, a collection of weights is initialized and the similar miscalculation is discovered. Finally, during training of an ANN, we can easily overcome the training complications involving ANNs and also gain the finest overall performance with training of the ANN. We have implemented the proposed system in MATLAB, and the overall accuracy is about 93%, which is much better than that of the genetic algorithm (86%) and CS (88%) algorithm.<\/jats:p>","DOI":"10.1515\/jisys-2017-0423","type":"journal-article","created":{"date-parts":[[2019,3,3]],"date-time":"2019-03-03T11:18:16Z","timestamp":1551611896000},"page":"1235-1245","source":"Crossref","is-referenced-by-count":1,"title":["AGCS Technique to Improve the Performance of Neural Networks"],"prefix":"10.1515","volume":"29","author":[{"given":"Kishor Kumar","family":"Katha","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering , Osmania University , Hyderabad 500 007 , India"}]},{"given":"Suresh","family":"Pabboju","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Chaitanya Bharathi Institute of Technology , Hyderabad 500 075 , India"}]}],"member":"374","published-online":{"date-parts":[[2019,3,4]]},"reference":[{"key":"2025120523293234403_j_jisys-2017-0423_ref_001","doi-asserted-by":"crossref","unstructured":"D. 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