The Evaluation Model of Knowledge Management Based on Information Entropy and RBF Neural Network (IE-RBF)

2009 ◽  
Author(s):  
Yu Chen
Keyword(s):  
Neural Network ◽  
Knowledge Management ◽  
Information Entropy ◽  
Rbf Neural Network ◽  
Evaluation Model

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.

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.

PPPs Performance Evaluation Based on RBF Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 1256-1261
Author(s):  
Hui Sun ◽  
Min Zhou ◽  
Zhi Qing Fan
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Statistical Data ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Project Performance ◽  
Quantitative Index ◽  
Theoretical Methods ◽  
Evaluation Problem ◽  
Viable Method

Based on RBF neural network and combining with the study of PPP performance evaluation, quantitative index system was built from economic, society and environment three aspects. Focusing on the project performance evaluation, which is a nonlinear evaluation problem, the evaluation model based on RBF was established. Empirical analysis was carried out with quantitative projects statistical data, the results show that the program can effectively and accurately evaluate the PPP projects performance, successfully proposed and verified a viable method, and set a basis of theoretical methods for further study.

Damage Condition Assessment of Expressway Asphalt Pavement Based on RBF Neural Network

2012 ◽  
Vol 446-449 ◽  
pp. 2548-2553
Author(s):  
Ya Xin Huang ◽  
Fei Shao ◽  
Ya Wen Liu
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Asphalt Pavement ◽  
Evaluation Model ◽  
Affecting Factors ◽  
Pavement Maintenance ◽  
Condition Evaluation ◽  
Practical Engineering ◽  
Damage Condition ◽  
Crack Condition

In order to make the performance evaluation of highway asphalt pavement more scientific and reasonable, carrying out pavement maintenance management is more necessary. Taking advantage of excellent adaptability of neural network technology to deal with nonlinear mapping problem, a breakage condition evaluation model based on radial basis function (RBF) neural network is presented. This model considers four main affecting factors including pavement rut condition, crack condition, pit slot condition and repair condition. Certain number of sample data is chosen to train and simulate the RBF neural network model. The tests results, accordant with expectation, indicate that the model is qualified for practical engineering applications.

Application of Radial Basis Function Neural Network to Support Concept Evaluation

2012 ◽  
Vol 472-475 ◽  
pp. 1926-1931
Author(s):  
Qing Wei Yang ◽  
Nai Chao Wang ◽  
Ma Lin
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Concept Evaluation ◽  
Radial Basis ◽  
Design Characteristics

In order to solve the problem that how to evaluate the complex system support concept, an evaluation method based on Radial Basis Function (RBF) neural network model was presented. Through researching the support system overall design characteristics and elements of support, on this basis, evaluation parameters of support concept were abstracted. Support concept evaluation model based on RBF was established and a mature and stable RBF neural network was trained to calculate the comprehensive evaluation value for support concept. Finally, the further demonstration and verification of the method are given through specific case application and compared with the result for evaluation results of data envelopment analysis (DEA) model.

Evaluation of English interpretation teaching quality based on GA optimized RBF neural network

2020 ◽  
pp. 1-8
Author(s):  
Lu Chen ◽  
He Being
Keyword(s):  
Neural Network ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Principal Component ◽  
Teaching Quality ◽  
Network Teaching ◽  
Time Performance

Aiming at the problem of low accuracy of the current English interpretation teaching quality evaluation, a teaching quality evaluation method based on a genetic algorithm (GA) optimized RBF neural network is proposed. First, the principal component analysis is used to select the teaching quality evaluation index, and then design The RBF neural network teaching evaluation model is used, and GA is used to optimize the initial weights of the RBF neural network. Experimental results show that this method can effectively evaluate the quality of English interpretation teaching, and has high accuracy and real-time performance.

scholarly journals Establishment of Thermal Comfort Evaluation Model Based on Individual Difference

2019 ◽  
Vol 136 ◽  
pp. 04101
Author(s):  
Yalong Yang ◽  
Yufu Liu ◽  
Rui Zhang ◽  
Xulai Zhu ◽  
Mingyue Wang
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Energy Saving ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Building Energy ◽  
Physiological Signals ◽  
Model Based ◽  
Comfort Evaluation ◽  
Building Energy Saving

“People-oriented” and “Energy saving” are the two major themes of current social development. In the field of thermal comfort, establishment of thermal comfort model based on physiological parameters plays an important role in meeting the needs of human health and comfort, optimizing the design of building environment and building energy saving. In this paper, three types of physiological signals (skin temperature, skin conductance and heart rate) were collected through comfort physiological experiments. The changes of the three types of physiological signals under environmental temperature were analyzed. Furthermore, subjective questionnaire survey of human thermal comfort under five experimental conditions was performed. In addition, the thermal comfort evaluation model based on individual differences was established by partial least squares regression and ELM-RBF neural network. The established models were compared with the classical PMV model to analyze the superiority of the model. The results show that the thermal comfort evaluation model based on individual differences established by ELM-RBF neural network can better predict the trend of people's thermal comfort and satisfy the individual's demand for thermal comfort. Meanwhile, it can achieve the goal of building energy saving. Therefore, it has high practical and social significance.

scholarly journals Prediction of Flight Status of Logistics UAVs Based on an Information Entropy Radial Basis Function Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3651
Author(s):  
Qin Yang ◽  
Zhaofa Ye ◽  
Xuzheng Li ◽  
Daozhu Wei ◽  
Shunhua Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Network Structure ◽  
Information Entropy ◽  
Basis Function ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Output Layer ◽  
Radial Basis ◽  
Hidden Layer

Aiming at addressing the problems of short battery life, low payload and unmeasured load ratio of logistics Unmanned Aerial Vehicles (UAVs), the Radial Basis Function (RBF) neural network was trained with the flight data of logistics UAV from the Internet of Things to predict the flight status of logistics UAVs. Under the condition that there are few available input samples and the convergence of RBF neural network is not accurate, a dynamic adjustment method of RBF neural network structure based on information entropy is proposed. This method calculates the information entropy of hidden layer neurons and output layer neurons, and quantifies the output information of hidden layer neurons and the interaction information between hidden layer neurons and output layer neurons. The structural design and optimization of RBF neural network were solved by increasing the hidden layer neurons or disconnecting unnecessary connections, according to the connection strength between neurons. The steepest descent learning algorithm was used to correct the parameters of the network structure to ensure the convergence accuracy of the RBF neural network. By predicting the regression values of the flight status of logistics UAVs, it is demonstrated that the information entropy-based RBF neural network proposed in this paper has good approximation ability for the prediction of nonlinear systems.

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.

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