ПРОГРАММНЫЕ И АППАРАТНЫЕ РЕАЛИЗАЦИИ ОСНОВНЫХ КОМПОНЕНТ СПАЙКОВЫХ И ФОРМАЛЬНЫХ НЕЙРОННЫХ СЕТЕЙ

2020 ◽  
Vol 96 (3s) ◽  
pp. 570-579
Author(s):  
И.А. Суражевский ◽  
К.Э. Никируй ◽  
А.В. Емельянов ◽  
В.В. Рыльков ◽  
В.А. Демин
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Hardware Implementation ◽  
Hebbian Learning ◽  
Inhibitory Neurons ◽  
Local Weight ◽  
Network Algorithms ◽  
Spike Neural Network

Разработаны Verilog-A-модели тормозного и возбуждающего нейронов с бипрямоугольной и битреугольной формами импульсов (спайков) и мемристивными синаптическими весами. Показана возможность сходимости весов в численном моделировании обучения по Хеббу нейрона на основе локальных правил модификации весов. Предложена схема нейросинаптического ядра для аппаратной реализации формальных и спайковых нейронных сетей на их основе. The paper presents Verilog-A models of excitatory and inhibitory neurons with birectangular and bitriangular shapes of spikes. Besides, it highlights the possibility of convergence of weights in the numerical simulation of a neuron Hebbian learning based on local weight updates rules, as well as offers schemes for the neurosynaptic core for the hardware implementation of formal and spike neural network algorithms.

scholarly journals Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update

2021 ◽  
Vol 15 ◽  
Author(s):  
Taeyoon Kim ◽  
Suman Hu ◽  
Jaewook Kim ◽  
Joon Young Kwak ◽  
Jongkil Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Hardware Implementation ◽  
Promising Candidate ◽  
Spike Neural Network ◽  
Neuromorphic Hardware ◽  
Non Linear ◽  
Device Properties ◽  
The Impact ◽  
The Brain

Among many artificial neural networks, the research on Spike Neural Network (SNN), which mimics the energy-efficient signal system in the brain, is drawing much attention. Memristor is a promising candidate as a synaptic component for hardware implementation of SNN, but several non-ideal device properties are making it challengeable. In this work, we conducted an SNN simulation by adding a device model with a non-linear weight update to test the impact on SNN performance. We found that SNN has a strong tolerance for the device non-linearity and the network can keep the accuracy high if a device meets one of the two conditions: 1. symmetric LTP and LTD curves and 2. positive non-linearity factors for both LTP and LTD. The reason was analyzed in terms of the balance between network parameters as well as the variability of weight. The results are considered to be a piece of useful prior information for the future implementation of emerging device-based neuromorphic hardware.

scholarly journals Management of the Torch Structure with the New Methodological Approaches to Regulation Based on Neural Network Algorithms

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1909
Author(s):  
Konstantin Osintsev ◽  
Sergei Aliukov ◽  
Yuri Prikhodko
Keyword(s):  
Neural Network ◽  
Coal Dust ◽  
Combustion Process ◽  
Analytical Calculation ◽  
Continuous Distribution ◽  
Initial Section ◽  
Distribution Functions ◽  
Thermophysical Characteristics ◽  
Network Algorithms ◽  
Furnace Volume

A method for evaluating the thermophysical characteristics of the torch is developed. Mathematically the temperature at the end of the zone of active combustion based on continuous distribution functions of particles of solid fuels, in particular coal dust. The particles have different average sizes, which are usually grouped and expressed as a fraction of the total mass of the fuel. The authors suggest taking into account the sequential nature of the entry into the chemical reactions of combustion of particles of different masses. In addition, for the application of the developed methodology, it is necessary to divide the furnace volume into zones and sections. In particular, the initial section of the torch, the zone of intense burning and the zone of afterburning. In this case, taking into account all the thermophysical characteristics of the torch, it is possible to make a thermal balance of the zone of intense burning. Then determines the rate of expiration of the fuel-air mixture, the time of combustion of particles of different masses and the temperature at the end of the zone of intensive combustion. The temperature of the torch, the speed of flame propagation, and the degree of particle burnout must be controlled. The authors propose an algorithm for controlling the thermophysical properties of the torch based on neural network algorithms. The system collects data for a certain time, transmits the information to the server. The data is processed and a forecast is made using neural network algorithms regarding the combustion modes. This allows to increase the reliability and efficiency of the combustion process. The authors present experimental data and compare them with the data of the analytical calculation. In addition, data for certain modes are given, taking into account the system’s operation based on neural network algorithms.

Dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms

2020 ◽  
Vol 39 (4) ◽  
pp. 5521-5534
Author(s):  
Ying Liu ◽  
Zhongqi Fan ◽  
Hongliang Qi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Management ◽  
Coal Mine ◽  
Evaluation System ◽  
Statistical Evaluation ◽  
Specific Situation ◽  
Network Algorithms ◽  
Management Capability ◽  
Coal Enterprises

By establishing the evaluation system of emergency management capability for coal mine enterprises, we can identify the problems and shortcomings in coal mine emergency management, improve and improve its emergency management capability for coal mine emergencies. In this paper, the authors analyze the dynamic statistical evaluation of safety emergency management in coal enterprises based on neural network algorithms. Neural networks can form any form of topological structure through neurons, so they can directly simulate fuzzy reasoning in structure, that is to say, the equivalent structure of neural networks and fuzzy systems can be formed. This paper constructs the index system based on accident causes, and verifies the scientific rationality of the system. On this basis, according to the specific situation of coal mine emergency management, we design the evaluation criteria of coal mine emergency management capability evaluation index. Because coal mine accidents have the characteristics of complexity, variability and sudden dynamic, it is necessary to adjust and improve the accidents dynamically at any time. The model combines qualitative and quantitative indicators, and can make an overall evaluation of coal mine emergency management capability. It has the characteristics of clear results and strong fitting of simulation results.

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