scholarly journals Progressive optimization methods for applied in computer network

2009 ◽  
Vol 57 (6) ◽  
pp. 45-50
Author(s):  
Miroslav Cepl ◽  
Jiří Šťastný
Keyword(s):  
Neural Network ◽  
Computer Network ◽  
Hopfield Neural Network ◽  
Optimization Methods ◽  
Processing Unit ◽  
Communications Networks ◽  
Active Elements ◽  
Artificial Neural ◽  
And Function ◽  
Network Switch

Standard core of communications’ networks is represent by active elements, which carries out the processing of transmitted data units. Based on the results of the processing the data are transmitted from sender to recipient. The hardest challenge of the active elements present to determine what the data processing unit and what time of the system to match the processing priority assigned to individual data units. Based on the analysis of the architecture and function of active network components and algorithms, artificial neural networks can be assumed to be effectively useable to manage network elements. This article focuses on the design and use of the selected type of artificial neural network (Hopfield neural network) for the optimal management of network switch.

The Computer Network Optimization Model Based on Neural Network Algorithm Research

2013 ◽  
Vol 798-799 ◽  
pp. 545-548
Author(s):  
Xun Wang ◽  
Jie Rong
Keyword(s):  
Neural Network ◽  
Network Optimization ◽  
Optimization Model ◽  
Computer Network ◽  
Hopfield Neural Network ◽  
Optimization Methods ◽  
Information Storage ◽  
Distributed Information ◽  
Network Algorithm ◽  
Neural Network Algorithm

The speed of development of the computer network is an urgent need to comprehensively improve and optimize the overall performance of the network. Neural network algorithm has a massively parallel processing and distributed information storage, Hopfield neural network showed a unique advantage in the associative memory and optimization based on the neural network algorithm for computer network optimization model of Hopfield neural network theory and reality computer network, modern optimization methods, it is combined.

FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

2021 ◽  
Author(s):  
Fei Yu ◽  
Zinan Zhang ◽  
Hui Shen ◽  
Yuanyuan Huang ◽  
Shuo Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Encryption ◽  
High Performance ◽  
Random Sequence ◽  
Random Number Generator ◽  
Security Analysis ◽  
Hopfield Neural Network ◽  
Data Encryption ◽  
Design Tool ◽  
Processing Unit

Abstract In this paper, a memristive Hopfield neural network with a special activation gradient (MHNN) is proposed by adding a suitable memristor to the Hopfield neural network (HNN) with a special activation gradient. The MHNN is simulated and dynamic analyzed, and implemented on FPGA. Then, a new pseudo-random number generator (PRNG) based on MHNN is proposed. The post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator, which effectively ensures the randomness of PRNG. The experiments in this paper comply with the IEEE 754-1985 high precision 32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7Z020CLG400-2 FPGA chip and the Verilog-HDL hardware programming language. The random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis, proving its randomness and high performance. Finally, an image encryption system based on PRNG is proposed and implemented on FPGA, which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things (IoT).

scholarly journals Artificial neural network for solving multi-parameter optimization problems

2021 ◽  
Vol 2092 (1) ◽  
pp. 012013
Author(s):  
Krivorotko Olga ◽  
Liu Shuang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problems ◽  
Three Dimensional ◽  
Test Functions ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Quality Of Approximation ◽  
And Function

Abstract An artificial neural network (ANN) is a mathematical or computational model that simulates the structure and function of biological neural networks used to evaluate or approximate functions at given points. After developing the training algorithm, the resulting model will be used to solve image recognition problems, control problems, optimization, etc. In the process of ANN training, the algorithm of backpropagation is used in the case of convex optimization functions. The article is analyzed test functions for experiments and also study the effect of the number of ANN layers on the quality of approximation in cases one-, two- and three-dimensional. The backpropagation method is improved during the experiments with the help of adaptive gradient, as a result of which more accurate approximations of the functions are obtained. This article also presents the numerical results of test functions.

scholarly journals Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

2017 ◽  
Author(s):  
Atul Anand ◽  
L Suganthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tamil Nadu ◽  
Consumer Price Index ◽  
Optimization Methods ◽  
Electricity Demand ◽  
The State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Optimizing Algorithm

In  the present study, a hybrid  optimizing algorithm has been proposed using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of  electricity demand of  the state of Tamil Nadu in India. The GA-PSO model optimizes  the coefficients of factors of  gross state domestic product (GSDP), per capita demand, income and  consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models  are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as  ANN-BP, ANN-GA, ANN-PSO models. Further  the paper also forecasts the electricity demand of Tamil Nadu  based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario  is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that the direct causality exists between  GSDP and the electricity demand of the state.

scholarly journals Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

2018 ◽  
Author(s):  
Atul Anand ◽  
L Suganthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tamil Nadu ◽  
Consumer Price Index ◽  
Electricity Consumption ◽  
Optimization Methods ◽  
Electricity Demand ◽  
The State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

Numerical Simulation of Neural Network Components of Controlling and Measuring Systems

2015 ◽  
Vol 756 ◽  
pp. 507-512
Author(s):  
S.N. Danilin ◽  
M.V. Makarov ◽  
S.A. Shchanikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Tolerance ◽  
Feedforward Neural Networks ◽  
Measuring Systems ◽  
Tolerance Level ◽  
Artificial Neural ◽  
And Function

The article deals with the problem of calculating the fault tolerance of neural network components of industrial controlling and measuring systems used in mechanical engineering. We have formulated a general approach to developing methods for quantitative determination of the level of the fault tolerance of artificial neural networks with any structure and function. We have studied the fault tolerance of four artificial feedforward neural networks as well as the correlation between the result of determining the fault tolerance level and a selected performance parameter of artificial neural networks.

scholarly journals Artificial Neural Network Algorithms for 3D Printing

Materials ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 163
Author(s):  
Muhammad Arif Mahmood ◽  
Anita Ioana Visan ◽  
Carmen Ristoscu ◽  
Ion N. Mihailescu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
3D Printing ◽  
Process Parameters ◽  
Optimization Methods ◽  
Physical Models ◽  
Network Algorithms ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.

scholarly journals JARINGAN SYARAF TIRUAN MENGGUNAKAN ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI PRESTASI SISWA

2018 ◽  
Vol 1 (1) ◽  
pp. 58-72
Author(s):  
Fatmi Zola
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Student Achievement ◽  
Computer Network ◽  
Processing Technique ◽  
Training Data ◽  
Percentage Error ◽  
Neural Network System ◽  
Improve Student Achievement ◽  
Artificial Neural

Vocational High School Negeri 1 Bunga Raya (SMK N 1 Bunga Raya) is a vocational school in the field of Multi Competency. Currently there are 265 students with 5 majors (field) including Computer Network Engineering (TKJ), Motorcycle Engineering (TSM), Accounting, Agricultural Processing Technique (TPHP), Plantation Agribusiness (ATP) .To improve student achievement, then schools provide scholarships in the form of academic achievement scholarships. Artificial Neural Network System using Backpropagation method applied in this research to predict student achievement, and expected to help solve the problem of giving scholarship to student achievement. Implemented using Matlab as a supportive software. With the value of School examination as training data and testing. The result of testing with architectural pattern 4-2-1, the data into two parts that is 20 training data with percentage error 95,6%. And 20 test data with 100% error percentage. The smaller the level of accuracy error used will be the smaller deviation results Artificial Neural Network with the desired target.

Search for Optimum Operating Conditions for a Water Purification Process Integrated to a Heat Transformer with Energy Recycling using Artificial Neural Network Inverse Solved by Genetic and Particle Swarm Algorithms

2012 ◽  
Vol 7 (1) ◽  
Author(s):  
Youness El Hamzaoui ◽  
Bassam Ali ◽  
J. Alfredo Hernandez ◽  
Obed Cortez Aburto ◽  
Outmane Oubram
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Purification ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Purification Process ◽  
Artificial Neural ◽  
Heat Transformer

The coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling was optimized using the artificial intelligence. The objective of this paper is to develop an integrated approach using artificial neural network inverse (ANNi) coupling with optimization methods: genetic algorithms (GAs) and particle swarm algorithm (PSA). Therefore, ANNi was solved by these optimization methods to estimate the optimal input variables when a COP is required. The paper adopts two cases studies to accomplish the comparative study. The results illustrate that the GAs outperforms the PSA. Finally, the study shows that the GAs based on ANNi is a better optimization method for control on-line the performance of the system, and constitutes a very promising framework for finding a set of “good solutions”.

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