Performance evaluation of fuzzy neural network with various aggregation operators

Author(s):  
P.M. Patil ◽  
U.V. Kulkarni ◽  
T.R. Sontakke
2014 ◽  
Vol 667 ◽  
pp. 60-63
Author(s):  
Wei Guo ◽  
Zhen Ji Zhang

A performance evaluation system of finance transportation projects is mainly researched, in which the sub-module of the highway projects evaluation, waterway projects evaluation, Passenger stations projects evaluation, Energy saving projects evaluation are incorporated. In addition, the expert knowledge are inserted in the system, the multi-layer neural network and fuzzy-set theory are used to implement Performance Evaluation system of Finance invest Transportation Projects, and the feasibility and effectiveness of the evaluation system are finally verified by practice.


2021 ◽  
Vol 251 ◽  
pp. 02097
Author(s):  
Mingle Zhou ◽  
Shengli Cao ◽  
Ran Wang ◽  
Yu Wang

Open government affairs (OGA) play an important role in promoting national governance system and capacity. In order to realize an open and efficient government, it is necessary to scientifically evaluate the performance of government. The effect of OGA can be improved continuously through the feedback from evaluation, which is beneficial for the sustainable development of OGA. However, with the continuous development of OGA, the existing methods of evaluation are faced with such problems as poor-timeliness, high-cost and subjective uncertainty, which are difficult to satisfy the demands of performance evaluation of OGA. Therefore, this paper puts forward a performance evaluation method based on T-S fuzzy neural network. Our method has a strong ability of data processing, which can simplify the work flow. The T-S fuzzy neural network was trained and tested through using the performance evaluation data of all districts and counties in Shandong Province, China. Finally, the evaluation results offered by our method are highly accurate. Hence, our method is suitable for the performance evaluation of OGA, it can continuously enhance the improvement of government’s performance management and capacity building so as to promote the sustainable development of OGA.


2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam

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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