A New Evaluation Approach to Mechanical Kinematic Concept Using Fuzzy Neural Network

2011 ◽  
Vol 421 ◽  
pp. 666-669 ◽  
Author(s):  
Jian De Wu
Keyword(s):  
Neural Network ◽  
Evaluation System ◽  
Function Analysis ◽  
Evaluation Model ◽  
Indicator System ◽  
Bp Network ◽  
Evaluation Indicator ◽  
Evaluation Approach ◽  
Fuzzy Neural ◽  
Attribute Value

In this paper, BP network is applied to structure multi-level evaluation model to implement evaluation for the kinematic concepts acquired by function analysis. Under this approach, the best concept can be selected once evaluation indicators of each candidate are fuzzily quantified, converted into evaluation attribute value, and fed into the trained network model. During the process, neural network is used to solve the bottle-neck problem of knowledge acquiring and expression, which can be viewed as knowledge base and reasoning engine for the evaluation. At the same time, it is effective in solving the problem of weight distribution in evaluation indicator system. Fuzzy logic is used to achieve the fuzzy quantization for the attribute value of evaluation indicator in evaluation system, which can be used as the I/O value for neural network.

An Improved BP Neural Network Algorithm and its Application in Competitive Advantage Evaluation of Logistics Enterprises

2013 ◽  
Vol 850-851 ◽  
pp. 788-791
Author(s):  
Feng Lan Luo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Competitive Advantage ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Indicator System ◽  
Research Field ◽  
Competitive Advantages ◽  
Evaluation Indicator ◽  
Logistics Enterprises

BP neural network is a hot research field for its powerful simulation calculation ability in various disciplines in recent years, but the algorithm has some shortages such as low convergence which limit the usage of the algorithm. The paper improves BP model with genetic algorithm and applies it to evaluate competitive advantages of logistics enterprises. First the paper designs an evaluation indicator system of competitive advantage of logistics enterprises through analyzing the characteristics of the evaluation indicator; Second, genetic algorithm is used to speed up the convergence of BP algorithm and based on this the paper advances a new competitive advantage evaluation model for logistics enterprises. Finally, the improved model is realized with the data from four Chinese logistics enterprises and the realization of the experimental results show that the model can improve algorithm efficiency and evaluation accuracy and can be used for evaluating the competitive advantages of logistics enterprises practically.

Research on a Fuzzy Neural Network Based Medical Institutions Selection in Subway Station Emergency

2011 ◽  
Vol 361-363 ◽  
pp. 1204-1210 ◽  
Author(s):  
Zong Feng Zou ◽  
Hong Xia Guo
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Subway Station ◽  
Affecting Factors ◽  
Evaluation Approach ◽  
Medical Institutions ◽  
Fuzzy Neural ◽  
Selection Of

The paper introduces fuzzy neural network into the selection of medical institutions in subway station emergency in view of some negatives of previous evaluation approaches. It also makes comprehensive analysis about affecting factors of subway station incident, and constructs the evaluation index system, and then proposes a new evaluation approach. This approach is a fuzzy neural network. The parameters accident factors, medical institutions' conditions and external environment are trained firstly.Then to construct a fuzzy neural network evaluation model and thereby derive the evaluating value of the selection of medical institutions in subway station emergency. Experimental results show that this method is effective, feasible and highly accurate.

Research on Application of Neural Network in Computer Network Security Evaluation

2014 ◽  
Vol 8 (1) ◽  
pp. 766-771
Author(s):  
Shujuan Jin
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Evaluation System ◽  
Evaluation Method ◽  
Computer Network ◽  
Evaluation Model ◽  
Indicator System ◽  
Security Evaluation ◽  
Model Based ◽  
Computer Network Security

Purpose: discuss the role of the neural network (NN) theory in the computer network security evaluation. Method: propose three-level and four-class indicator system suitable for network security evaluation, establish the network security evaluation system model based on NN, optimize the NN model by using the particle swarm, collect 100- group data on the computer network security evaluation of different scales via expert scoring, and normalize them; Result: the evaluation model based on NN is simple and practicable to network security evaluation and can eliminate disturbance of the subjective factors of the human being. The simulation results indicate that the system can reduce relative output error and improve correctness rate of evaluation. Conclusion: The NN model is very valuable in research on the computer network security evaluation system, which can offset weaknesses of the past evaluation methods to some extent, improve precision of the evaluation results, and provide reference to prediction and control of the network security problems in future.

scholarly journals INVESTIGATION INTO EVALUATION OF AGRICULTURE INFORMATIZATION LEVEL BASED ON TWO-TUPLE

2011 ◽  
Vol 17 (1) ◽  
pp. 74-86 ◽  
Author(s):  
Peide Liu ◽  
Xin Zhang
Keyword(s):  
Evaluation System ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Qualitative Evaluation ◽  
Indicator System ◽  
Research Status ◽  
Evaluation Indicator ◽  
Evaluation Indicator System ◽  
At Home ◽  
The Evaluation System

Agriculture informatization level is an important part of one country's modernization. It is important to construct reasonable agriculture informatization evaluation indicator system and propose the evaluation method for promoting the agriculture informatization. This paper firstly analyzes the research status of the indicator system and evaluation method of informatization at home and abroad. On the basis of the relative literatures, the evaluation indicator system of the agriculture informatization in China is constructed, and the evaluation model of the agriculture informatization based on two-tuple and the relative operators are also constructed. This model not only can be used to rank the orders of the different areas according to the informatization level, but also can realize the qualitative evaluation of the agriculture informatization of the different areas according to the evaluation system. Finally, the application example shows that the evaluation system of the agriculture informatization constructed in this paper is effective, and the evaluation method proposed in this paper is simple and easy to use.

Concept Optimization Model with Multilevel Hierarchy Based on Fuzzy Neural Network

2011 ◽  
Vol 338 ◽  
pp. 30-33
Author(s):  
Rui Feng Bo
Keyword(s):  
Neural Network ◽  
Optimization Problem ◽  
Function Analysis ◽  
Evaluation Model ◽  
Data Sets ◽  
The Neural Network ◽  
Concept Evaluation ◽  
Fuzzy Neural ◽  
Bottle Neck ◽  
Objective System

To implement optimization for mechanical concepts acquired by function analysis more effectively, BP neural network is adopted to structure multilevel evaluation model, which capitalizes on the features of nonlinearity, self-organization, and fault tolerance of neural network. By using appropriate data sets to train the neural network, expertise is acquired and expressed using a trained weight and threshold matrix. Once evaluation objectives of each candidate are fuzzily quantified, converted into evaluation attribute value, and fed into the trained network model, the optimal concept can be obtained. During the process, neural network is used to solve the bottle-neck problem of knowledge acquisition and expression and can be viewed as knowledge base and reasoning engine for the optimization. Hence the proposed evaluation model can effectively deal with concept evaluation and optimization problem with multilevel objective system.

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.

Study on Intelligent Analysis of the Causes of SMT Solder Joint Defects Based on Fuzzy Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 3257-3261
Author(s):  
Chun Yue Huang ◽  
He Geng Wei ◽  
Tian Ming Li ◽  
De Jin Yan
Keyword(s):  
Neural Network ◽  
Solder Joint ◽  
Membership Function ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Training Samples ◽  
Intelligent Analysis ◽  
Fuzzy Neural ◽  
Input Variables ◽  
Set Up

By determining membership function of the input parameters and selecting defuzzification method, the evaluation model which can be used to intelligent analyzing the causes of SMT solder joint defects was set up. The fuzzy neural network was trained by using the output variables of the training samples from intelligent discrimination as the input variables of training samples of fuzzy neural network. The fuzzy neural network was tested by using the output variables of the testing samples from intelligent discrimination as the input variables of testing samples of fuzzy neural network. The results show that by using the evaluation model the cause of SMT solder joint defects can be analyzed intelligently and the results of intelligently analysis are reasonable, the evaluation model can be used practically.

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.

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.

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