Research on Modeling for Safety Evaluation System of Amusement Ride Based on Fuzzy Neural Network

2009 ◽  
Author(s):  
Yong Shen ◽  
Yuan Xiao ◽  
Jianping Ye ◽  
Jianjun Qian
Keyword(s):  
Neural Network ◽  
Evaluation System ◽  
Fuzzy Neural Network ◽  
Safety Evaluation ◽  
Fuzzy Neural

Research on Performance Evaluation of Finance Transportation Projects Based on Fuzzy Neural Network

2014 ◽  
Vol 667 ◽  
pp. 60-63
Author(s):  
Wei Guo ◽  
Zhen Ji Zhang
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Set Theory ◽  
Evaluation System ◽  
Fuzzy Neural Network ◽  
Expert Knowledge ◽  
Highway Projects ◽  
Performance Evaluation System ◽  
Transportation Projects ◽  
Fuzzy Neural

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.

Design of Credit Risk Evaluation System Based on the Fuzzy Neural Network and Expert System

2014 ◽  
Vol 543-547 ◽  
pp. 4523-4527
Author(s):  
Hong Min Zhang
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Expert System ◽  
Credit Risk ◽  
Index System ◽  
Evaluation System ◽  
Fuzzy Neural Network ◽  
Enterprise Risk ◽  
Fuzzy Neural ◽  
Set Up

Credit risk is the main risk that Chinese commercial banks are facing. Taking into account three categories of risk factors, namely risk factors of enterprise, risk factors of commercial bank and risk factors of macroscopic economy, an index system was set up. Then, according to the index system and the characteristics of fuzzy neural network and expert system, a credit risk rating system based on fuzzy neural network and expert system was proposed.

The Assemblability Evaluation of Automotive Glass Windshield Mold Based on Fuzzy Neural Network

2010 ◽  
Vol 139-141 ◽  
pp. 1753-1756
Author(s):  
Lai Teng ◽  
Li Zhong Wang ◽  
De Hong Yu ◽  
Shun Lai Zang ◽  
Yu Jiao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation System ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Urgent Problem ◽  
Mapping Model ◽  
Fuzzy Neural

Nowadays the production of mold seriously restricts the manufacture of products as well as the development of new products, it has become an urgent problem to be solved. The paper mainly discussed the fuzzy neural network model and learning algorithm, and utilized expert evaluating system to acquire the training and test samples. Moreover, it established the related mapping model for fuzzy neural network to evaluate the assemblability of mold, so as to improve the productivity of mold. By adopting two different fuzzy neural networks to contrast and evaluate the assemblability evaluation system of the parts of windshield mold, it was concluded that the improved fuzzy neural network model had advantage over the conventional one. Finally, the satisfactory results of assemblability evaluation system of windshield mold had been achieved by coming with examples to carry out error analysis of the assemblability evaluation system.

Virtual Reality Simulation of 5dof Upper-Limb Rehabilitation Robot Based on Fuzzy Neural Network

2011 ◽  
Vol 48-49 ◽  
pp. 1345-1350
Author(s):  
Xing Li ◽  
Jian Hui Wang ◽  
Xiao Ke Fang
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Upper Limb ◽  
Evaluation System ◽  
Fuzzy Neural Network ◽  
Rehabilitation Robot ◽  
Virtual Reality Simulation ◽  
Upper Limb Rehabilitation ◽  
Fuzzy Neural ◽  
Limb Rehabilitation

In this paper, aiming at the structure of upper-limb rehabilitation robot, establish the model of algorithmic control based on fuzzy neural network and virtual reality simulation model for 5dof upper-limb rehabilitant robot, and take the elbow joint for example to do simulation analysis. The result of simulation shows the fuzzy neural network control is practicable and its control accuracy takes the precedence over the traditional methods. The virtual-reality simulation of 5dof upper-limb rehabilitation robot, which is benefit to understand the complex relationships among the objects, can emulate the features of real rehabilitation robot, laying a solid foundation for rehabilitation evaluation system and telemedicine.

Evaluation of the effect of music education on improving students’ mental health based on intelligent fuzzy system

2021 ◽  
pp. 1-11
Author(s):  
Tingting Zhang ◽  
Shengnan Liu
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Control System ◽  
Music Education ◽  
Fuzzy System ◽  
Evaluation System ◽  
Fuzzy Neural Network ◽  
Control Methods ◽  
Model Control ◽  
Fuzzy Neural

Traditional control methods and modern accurate mathematical model control methods do not perform well in the evaluation of students’ mental health. In order to improve the evaluation effect of students’ mental health, this paper takes the intelligent fuzzy system as the control center and proposes an evaluation system to evaluate the effect of music education in promoting students’ mental health based on fuzzy neural network. Moreover, according to the working characteristics of the music education system, this paper interprets the design requirements of its control system in detail, and has an in-depth understanding of the fuzzy principle, neural network principle and fuzzy god network principle. Secondly, this paper completes the design of the actual orthosis control algorithm applied to the fuzzy neural network control system and the optimization of the fuzzy neural network algorithm. Finally, this paper realizes the intelligent processing of the non-linear pressure signal output by the corresponding strain, and uses music education to evaluate the students’ mental health and manage the rehabilitation effect. From the experimental research results, it can be seen that the system constructed in this paper has a certain effect.

An intelligent method for the evaluation and prediction of fabric formability for men’s suits

2016 ◽  
Vol 88 (4) ◽  
pp. 438-452 ◽  
Author(s):  
Z Xue ◽  
X Zeng ◽  
L Koehl
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Principal Component Analysis ◽  
Data Analysis ◽  
Predictive Model ◽  
Sensory Evaluation ◽  
Evaluation System ◽  
Fuzzy Neural Network ◽  
Principal Component ◽  
Fuzzy Neural

Sixty-six commonly used suitings were selected as the experimental samples of the current study. The Kawabata Evaluation System was used to measure the mechanical properties of the samples. Each sample fabric was made into a shoulder-back as a part of a men’s suit. In order to study the appropriateness of the samples for making good shaped men’s suits, which is known as fabric formability, sensory evaluation methods have been applied to obtain panelists’ assessments on the shape of the shoulder-backs. During data analysis, principal component analysis was initially adopted to reduce the complexity of the system by extracting a small number of important mechanical properties. Then, a fuzzy neural network was developed to model the underlying relations between the samples’ formability and their mechanical properties. Finally, a number of testing samples were used to verify the effectiveness of the proposed predictive model.

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

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
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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