Establishing Customers' Beer Tastes Analysis Model on the Basis of Fuzzy Neural Network Simulation

Internalization of environment cost assessment measures the level of an enterprise’s environmental cost internalization. It’s also the basis of carrying out recycling economic in an enterprise. First of all, we established an environmental cost analysis model, in line with which we build the internalization of environment cost index system. Then adopting comprehensive evaluation method basing on fuzzy neural network can help us assess the effect brought by the internalization of environment cost. Finally, we conducted an experiment which comparing fuzzy neural network with the fuzzy evaluation of environment cost objectively. So we can think it’s an effective method.

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New Media Advertising Communication Analysis Model Based on Extension Neural Network

Scientific Programming ◽

10.1155/2021/5969446 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Zhe Zhang

Keyword(s):

Neural Network ◽

New Media ◽

Fuzzy Neural Network ◽

Intelligent System ◽

Structure Design ◽

Analysis Model ◽

Communication Analysis ◽

Model Based ◽

Fuzzy Neural ◽

Advertising Communication

In order to improve the effect of new media advertising communication analysis, this paper combines the scalable neural network to construct the new media advertising communication analysis model. Moreover, this paper analyzes in detail the basic theories of fuzzy neural network and extension evaluation, the structure design and learning algorithm, and classification of fuzzy neural network. In particular, this paper summarizes the optimization algorithms and methods of neural network structure. In addition, this paper improves the algorithm to meet the needs of new media advertising data analysis and builds an intelligent system framework. The experimental verification shows that the new media advertising communication analysis model based on the extension neural network proposed in this paper meets the new media advertising communication analysis effect.

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Computer Simulation Technology of Electric Power Safety Based on Fuzzy Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.539.679 ◽

2014 ◽

Vol 539 ◽

pp. 679-683

Author(s):

Yu Ying Zheng

Keyword(s):

Neural Network ◽

Computer Simulation ◽

Electric Power ◽

Fuzzy Neural Network ◽

Financial Risks ◽

Analysis Model ◽

Power Enterprise ◽

Fuzzy Neural ◽

Hidden Layer ◽

Electric Power Enterprise

As the basis industry of the national economy electric power enterprise is shouldering significant social responsibility in the process of operation, also needs to face the operational risk generated by enterprise competition under the conditions of market economy. How to scientifically and efficiently manage the financial risks of power enterprises is one of the hot issues that are urgent needed to resolve in the current field. In this paper, on the basis of previous studies, firstly has combined with the structure characteristics of the fuzzy neural network model. Then it builds prediction analysis model of financial risks according to the fuzzy neural network structure. And it sets the selection of the number of neuron for the hidden layer based on the financial risks' characteristics of electric power enterprise. At last, it combines with 12 financial indicators data of electric power enterprise finance to make further computer simulation, so as to verify the scientificity of the model. And the results show that the model has strong reliability and a strong practical value.

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A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

Matériaux & Techniques ◽

10.1051/mattech/2019002 ◽

2018 ◽

Vol 106 (6) ◽

pp. 603 ◽

Cited By ~ 2

Author(s):

Bendaoud Mebarek ◽

Mourad Keddam

Keyword(s):

Neural Network ◽

Experimental Data ◽

Fuzzy Neural Network ◽

Treatment Time ◽

Calculation Model ◽

Average Error ◽

Network Approach ◽

Neural Network Approach ◽

Fuzzy Neural ◽

Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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