  In the age of neural networks and Internet of Things (IoT), the search for
new neural network architectures capable of operating on devices with limited
computing power and small memory size is becoming an urgent agenda. Designing
suitable algorithms for IoT applications is an important task. The paper
proposes a feed forward LogNNet neural network, which uses a semi-linear Henon
type discrete chaotic map to classify MNIST-10 dataset. The model is composed
of reservoir part and trainable classifier. The aim of the reservoir part is
transforming the inputs to maximize the classification accuracy using a special
matrix filing method and a time series generated by the chaotic map. The
parameters of the chaotic map are optimized using particle swarm optimization
with random immigrants. As a result, the proposed LogNNet/Henon classifier has
higher accuracy and the same RAM usage, compared to the original version of
LogNNet, and offers promising opportunities for implementation in IoT devices.
In addition, a direct relation between the value of entropy and accuracy of the
classification is demonstrated.

Machine Learning;Computer Vision and Pattern Recognition;Neural and Evolutionary Computing;Image and Video Processing;Chaotic Dynamics