AGCS Technique to Improve the Performance of Neural Networks

• Main Authors: Katha Kishor Kumar, Pabboju Suresh
• Format: Article
• Language: English
• Published: De Gruyter 2019-03-01
• Series: Journal of Intelligent Systems
• Subjects: adaptive genetic (ag) algorithm; cuckoo search (cs) algorithm; genetic algorithm (ga); back propagation algorithm (bpa); artificial neural network (ann); levy flight
• Online Access: https://doi.org/10.1515/jisys-2017-0423

In this paper, a fresh method is offered regarding training of particular neural networks. This technique is a combination of the adaptive genetic (AG) and cuckoo search (CS) algorithms, called the AGCS method. 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.