# An improved LogNNet classifier for IoT application

arXiv: https://arxiv.org/abs/2105.14412 · HTML: https://mlsanity.com/paper/2105.14412/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 30 May 2021.

## Builds on (most cited first, 6 of 6)

- [Low-Memory Neural Network Training: A Technical Report](https://mlsanity.com/paper/1904.10631/) (2019)
- [Memory- and Communication-Aware Model Compression for Distributed Deep Learning Inference on IoT](https://mlsanity.com/paper/1907.11804/) (2019)
- [Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map](https://mlsanity.com/paper/2006.02824/) (2020)
- [Recognition of handwritten MNIST digits on low-memory 2 Kb RAM Arduino board using LogNNet reservoir neural network](https://mlsanity.com/paper/2105.02953/) (2021)
- [MRAM Co-designed Processing-in-Memory CNN Accelerator for Mobile and IoT Applications](https://mlsanity.com/paper/1811.12179/) (2018)
- [In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications](https://mlsanity.com/paper/2006.11595/) (2020)

## Built on by (most cited first, 6 of 6)

- [A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks](https://mlsanity.com/paper/2107.08399/) (2021)
- [A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare](https://mlsanity.com/paper/2108.02428/) (2021)
- [Novel techniques for improving NNetEn entropy calculation for short and noisy time series](https://mlsanity.com/paper/2202.12703/) (2022)
- [Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network](https://mlsanity.com/paper/2205.09974/) (2022)
- [Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation](https://mlsanity.com/paper/2303.17995/) (2023)
- [Speech Command Recognition Using LogNNet Reservoir Computing for Embedded Systems](https://mlsanity.com/paper/2509.00862/) (2025)

## Abstract (arXiv)

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.