Recognition of handwritten MNIST digits on low-memory 2 Kb RAM Arduino board using LogNNet reservoir neural network

Izotov, Y A; Velichko, Andrei; Ivshin, A. A.; Novitskiy, R

doi:10.1088/1757-899x/1155/1/012056

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…In this paper, the layer structure 784:25:10 is the simplest structure which needs small memory and easy to implicate in various IoT devices. The simple structure and low memory usage approve that the model of 784:25:10 is suitable to apply in edge computing IoT devices which will be a hot topic in near future [22]. The relation between the approximate entropy and the accuracy of the classification is investigated.…”

Section: Discussionmentioning

confidence: 99%

An improved LogNNet classifier for IoT applications

Heidari

Velichko

2021

J. Phys.: Conf. Ser.

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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.

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Section: Discussionmentioning

confidence: 99%

An improved LogNNet classifier for IoT applications

Heidari

Velichko

2021

J. Phys.: Conf. Ser.

Self Cite

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“…Therefore, the fact of stimulation can be detected by a perceptron-based biosimilar chaos sensor. Such sensor does not require much computing power, as the perceptron model is simple, takes up little RAM, and can be implemented on devices such as the Arduino Uno [ 42 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science

Velichko

Борисков

Belyaev

et al. 2023

Sensors

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The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of a short time series with high accuracy, with a determination coefficient of R2~0.9. The Hindmarsh–Rose spike model was used to generate time series of spike intervals, and datasets for training and testing the perceptron. The selection of the hyperparameters of the perceptron model and the estimation of the sensor accuracy were performed using the K-block cross-validation method. Even for a hidden layer with one neuron, the model approximates the fuzzy entropy with good results and the metric R2~0.5 ÷ 0.8. In a simplified model with one neuron and equal weights in the first layer, the principle of approximation is based on the linear transformation of the average value of the time series into the entropy value. An example of using the chaos sensor on spike train of action potential recordings from the L5 dorsal rootlet of rat is provided. The bio-inspired chaos sensor model based on an ensemble of neurons is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing. The study will be useful for specialists in the field of computational neuroscience, and also to create humanoid and animal robots, and bio-robots with limited resources.

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“…In this context, it is a problem of paramount importance to assess the effectiveness of the different entropies when used as features in ML classification. Recently, Velichko et al proposed the use of a LogNNet neural network [12,13] for neural network entropy (NNetEn) calculation [1]. LogNNet neural network is a feedforward neural network that uses filters based on the logistic function and a reservoir inspired by recurrent neural networks, thus enabling the transformation of a signal into a high-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

Exploring Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps

Conejero,

Velichko,

Garibo-i-Orts

et al. 2023

Preprint

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In this paper, we propose a method for assessing the effectiveness of entropy features using several chaotic mappings. We anlyze fuzzy entropy (FuzzyEn) and neural network entropy (NNetEn) on four discrete mappings: the logistic map, the sine map, the Planck map, and the two-memristor-based map, with a base length time series of 300 elements. FuzzyEn is shown to have improved global efficiency (GEFMCC) in the classification task compared to NNetEn. At the same time, there are local areas of the time series dynamics in which the classification efficiency NNetEn is higher than FuzzyEn. The results of using horizontal visibility graphs (HVG) instead of the raw time series are also shown. GEFMCC decreases after HVG time series transformation, but there are local areas of the time series dynamics in which the classification efficiency increases after including the HVG. The scientific community can use the results to explore the efficiency of entropy-based classification of time series.

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Recognition of handwritten MNIST digits on low-memory 2 Kb RAM Arduino board using LogNNet reservoir neural network

Cited by 6 publications

References 24 publications

An improved LogNNet classifier for IoT applications

An improved LogNNet classifier for IoT applications

A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science

Exploring Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps

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