Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network

The convolutional neural network (CNN) is a technique that often used in deep learning. Various models have been proposed and improved for learning on CNN. When with CNN, it important to determine the optimal parameters. This paper proposes an optimization of CNN parameters using logarithm decreasing inertia weight (LogDIW). two datasets, i.e., MNIST CIFAR-10 dataset. experiment, dataset, compared its accuracy standard based LeNet-5 architectural model. CNN's baseline was 94.02% at 5th epoch, LogDIWPSO, which improves accuracy. 28.07% 10th LogDIWPSO 69.3%, increased