scholarly journals Improving a fuzzy neural network for predicting storage usage and calculating customer value

2020 ◽  
Vol 19 (5) ◽  
pp. 292-301
Author(s):  
Reza Rabieyan ◽  
Philipp Pohl
Keyword(s):  
Neural Network ◽  
Resource Management ◽  
Neural Network Model ◽  
Crucial Role ◽  
Customer Value ◽  
Fuzzy Neural Network ◽  
Prediction Methods ◽  
Fuzzy Neural ◽  
Customer Services

Abstract Predicting the behavior of customers plays a crucial role in the quality of resource management and customer services. In this article, a fuzzy neural network model for predicting the customer storage usage is identified. The identified fuzzy neural network is improved and finally the result of the improved fuzzy neural network is compared with some other fuzzy neural network and other prediction methods.

Application of T-S Fuzzy Neural Network in the Assessment of River Ecosystem Health

2013 ◽  
Vol 726-731 ◽  
pp. 958-962 ◽  
Author(s):  
Zhen Chun Hao ◽  
Xiao Li Liu ◽  
Qin Ju
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy System ◽  
Ecosystem Health ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Fuzzy Model ◽  
River Ecosystem ◽  
Fuzzy Neural

Healthy river ecosystem has been acknowledged as the object of river management, which is crucial for the sustainable development of cities. Simple and practical evaluation methods with great precision are necessary for the evaluation of river ecosystem health. Fuzzy system has been widely used in evaluation and decision making for its simple reasoning and the adoption of experts knowledge. However, much artificial intervention decreases the precision. Neural network has a strong ability of self-leaning while it is not good at expressing rule-based knowledge. The T-S fuzzy neural network model combines the advantages of fuzzy system and neural network. In this paper, the T-S fuzzy neural network model was used to establish a river ecosystem health evaluation model. Results show that the combination of T-S fuzzy model and neural network eliminates the influences of subjective factors and improve the final precisions efficiently.

Intelligent Prediction on the Quality of Cementing

2011 ◽  
Vol 84-85 ◽  
pp. 373-377
Author(s):  
Wei Zhang Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Influential Factor ◽  
Software Systems ◽  
Neural Network Modeling ◽  
Quality Prediction ◽  
Modeling Tools ◽  
Fuzzy Neural ◽  
Neural Network System

The present solutions of well cementing are mostly designed by designers’ experience and calculation which can not predict the engineering quality after application of the designs. Meanwhile some questions in the designs can not be solved before construction. On the basis of detailed evaluation of every influential factor according to construction and environmental conditions, this article provides cementing fuzzy neural network model by means of 2nsoftEditor neural network modeling tools, and the stable software systems with the combination of artificial neural network and fuzzy logic rules are expected to improve the credibility of cementing quality prediction. Construction practice shows that cementing quality prediction with application of fuzzy neural network system before cementing can greatly reduce the cementing costs and improve the cementing success ratio.

scholarly journals Modelling of the Slope Solute Loss Based on Fuzzy Neural Network Model

2022 ◽  
Vol 42 (2) ◽  
pp. 677-688
Author(s):  
Xiaona Zhang ◽  
Jie Feng ◽  
Zhen Hong ◽  
Xiaona Rui
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural
Sign in / Sign up

Export Citation Format

Share Document