The Evaluation Model of the Hydropower Project Financing Risk Based on AHP-RS and RBF Neural Network

2011 ◽  
Vol 474-476 ◽  
pp. 2243-2246 ◽  
Author(s):  
Hui Zhao ◽  
Li Ming Chen
Keyword(s):  
Neural Network ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Indicator System ◽  
Basic Function ◽  
Hydropower Project ◽  
Project Financing ◽  
Analytic Hierarchy ◽  
Financing Risk

A evaluation model based on the integration of analytic hierarchy process (AHP)-rough set theory (RS) and radial basic function (RBF) neural network is put forward for grasping the hydropower project financing risk. Firstly, the evaluation indicator system is constructed by AHP, then the evaluation indicators are discretized by RS neural network. And then, RBF neural network is used to evaluate the hydropower project financing risk. In order to grasp this evaluation model better, finally, the paper provides an example to demonstrate the application of this evaluation model.

Improved PSO in Water Supply Systems Based on AHP-RS and RBF Neural Network

2011 ◽  
Vol 99-100 ◽  
pp. 199-202
Author(s):  
Ao Jie Wang ◽  
Chao Lue Liu
Keyword(s):  
Neural Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Basic Function ◽  
Attractive Alternative ◽  
Water Supply Systems ◽  
Improved Pso

A evaluation model based on the integration of analytic hierarchy process(AHP)-rough set theory (RS) and radial basic function (RBF) neural network is put forward for grasping the hydropower project financing risk.The Particle Swarm Optimization (PSO) algorithm is implemented to optimize the node numbers of the hidden layers in the model. The study indicates that the AHP-RS and RBF neural network connecting with improved PSO method is an attractive alternative to the conventional regression analysis method in modeling water distribution systems.

scholarly journals A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network

2019 ◽  
Vol 11 (21) ◽  
pp. 6125
Author(s):  
Lianyan Li ◽  
Xiaobin Ren
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Smart Growth ◽  
Principal Component Regression ◽  
Principal Component ◽  
Self Organizing Map ◽  
Radial Basis

Smart growth is widely adopted by urban planners as an innovative approach, which can guide a city to develop into an environmentally friendly modern city. Therefore, determining the degree of smart growth is quite significant. In this paper, sustainable degree (SD) is proposed to evaluate the level of urban smart growth, which is established by principal component regression (PCR) and the radial basis function (RBF) neural network. In the case study of Yumen and Otago, the SD values of Yumen and Otago are 0.04482 and 0.04591, respectively, and both plans are moderately successful. Yumen should give more attention to environmental development while Otago should concentrate on economic development. In order to make a reliable future plan, a self-organizing map (SOM) is conducted to classify all indicators and the RBF neural network-trained indicators are separate under different classifications to output new plans. Finally, the reliability of the plan is confirmed by cellular automata (CA). Through simulation of the trend of urban development, it is found that the development speed of Yumen and Otago would increase slowly in the long term. This paper provides a powerful reference for cities pursuing smart growth.

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.

A Rough-Fuzzy RBF Neural Network Based on PSO Algorithm

2013 ◽  
Vol 710 ◽  
pp. 617-622
Author(s):  
Jing Zhao
Keyword(s):  
Neural Network ◽  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Training Sample ◽  
Network Training ◽  
Acquisition Method

A Rough-Fuzzy RBF Neural Network was raised based on PSO Algorithm. In this model,gives a knowledge acquisition method that based on rough set theory,the Rough-Fuzzy RBF neural network are constructed according to the results of the knowledge acquisition,the PSO are used to optimize the network parameters.This paper take number plate for example to conduct a simulation experiment.The results shows that the model can simplify the network training sample,optimize the network structure and enhance the systems study efficiency and the precision.

scholarly journals An Evaluation Model for Tailings Storage Facilities Using Improved Neural Networks and Fuzzy Mathematics

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sen Tian ◽  
Jianhong Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Back Propagation ◽  
Safety Evaluation ◽  
Evaluation Index System ◽  
Analytic Hierarchy ◽  
Operation Rate ◽  
Network Methods ◽  
Neural Network Algorithm

With the development of mine industry, tailings storage facility (TSF), as the important facility of mining, has attracted increasing attention for its safety problems. However, the problems of low accuracy and slow operation rate often occur in current TSF safety evaluation models. This paper establishes a reasonable TSF safety evaluation index system and puts forward a new TSF safety evaluation model by combining the theories for the analytic hierarchy process (AHP) and improved back-propagation (BP) neural network algorithm. The varying proportions of cross validation were calculated, demonstrating that this method has better evaluation performance with higher learning efficiency and faster convergence speed and avoids the oscillation in the training process in traditional BP neural network method and other primary neural network methods. The entire analysis shows the combination of the two methods increases the accuracy and reliability of the safety evaluation, and it can be well applied in the TSF safety evaluation.

Study on Function Zoning and Evaluation of Shallow Groundwater in Jinjiang City

2014 ◽  
Vol 675-677 ◽  
pp. 830-841
Author(s):  
Mingyao Yu ◽  
Suo Zhong Chen ◽  
Chu Yu Chen
Keyword(s):  
Evaluation Model ◽  
Shallow Groundwater ◽  
Indicator System ◽  
Scientific Basis ◽  
Function Evaluation ◽  
Technical Requirement ◽  
Analytic Hierarchy ◽  
Assessment Index ◽  
Assessment Indicator ◽  
Secondary Function

Based on the comprehensive analysis about the hydrogeological condition, ecological environment, and the characteristics of geological environment, energy is devoted to constructing a function evaluation indicator system of shallow groundwater with Jinjiang regional characteristic in the light of the assessment indicator system proposed in technical requirement for function evaluation and zoning of shallow groundwater issued by ministry of Land and resources. By dint of expert’s knowledge and experience, efforts are spent in assigning different weight values to the indicators, classifying function evaluation grading standards, and putting forward a secondary function zoning rating system. Besides, focus is put on building a comprehensive function evaluation model of shallow groundwater by means of analytic hierarchy process based on the constructed assessment indicator system of shallow groundwater. In order to guarantee assessment accuracy, the studied region is split into regular discrete grid for the purpose of calculating the comprehensive assessment index of grid unit. Meanwhile, based on GIS’ spatial analysis function, a research is conducted on the overall function zoning map of the shallow groundwater in the studied region to accurately reveal its spatial distribution and exploitation and provide scientific basis for groundwater management.

scholarly journals An Evaluation Model for the Innovation and Entrepreneurship Thinking Ability of College Students Based on Neural Network

2021 ◽  
Vol 16 (02) ◽  
pp. 188
Author(s):  
Feng Zhang ◽  
Limin Xi
Keyword(s):  
Neural Network ◽  
College Students ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Analytic Hierarchy ◽  
Thinking Ability ◽  
Fuzzy Neural ◽  
Knowledge Rules ◽  
Innovation And Entrepreneurship

Mass innovation and entrepreneurship (I&E) is a national campaign in China. In this context, it is important to encourage college students to engage in I&E activities, and this calls for accurate and comprehensive evaluation of their I&E thinking ability. Therefore, this paper proposes an evaluation model for the I&E thinking ability of college students based on neural network (NN). Firstly, a reasonable evaluation index system was created for the I&E thinking ability of college students, and the evaluation indices were preprocessed through fuzzy analytic hierarchy process (AHP). Then, a fuzzy neural network (FNN) was constructed based on GA rule optimization and the specific steps of the algorithm were given. Moreover, a few representative rules were selected by GA based on uncertain fuzzy knowledge rules, a 4-layer NN model with fuzzy inputs and outputs was established, and the evaluation flow of the I&E thinking ability of college students was proposed. Finally, the effectiveness of the proposed model was verified through experiments. The research results of this paper provide a reference for the application of NN in the field of ability evaluation.

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