AGCS Technique to Improve the Performance of Neural Networks

Abstract

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

Improving Learning Performance in Neural Networks

“…Guided policy search for learning purposes and trained autonomous optimizers for different classes of convex and non-convex objective functions was proposed to demonstrate that the autonomous optimizers converged faster and better than handengineered optimizers [6]. A combination of the adaptive genetic and cuckoo search algorithms was proposed by [22]. Algorithm intended to train a particular Artificial Neural Network (ANN) to obtain the finest weight load by using a hybrid ANN protocol.…”