scholarly journals Forecasting the Acquisition of University Spin-Outs: An RBF Neural Network Approach

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Weiwei Liu ◽  
Zhile Yang ◽  
Kexin Bi
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Empirical Studies ◽  
The Novel ◽  
Neural Network Approach ◽  
Positive Effects ◽  
Sample Data ◽  
Transfer Channel ◽  
Negative Effect ◽  
Average Distribution

University spin-outs (USOs), creating businesses from university intellectual property, are a relatively common phenomena. As a knowledge transfer channel, the spin-out business model is attracting extensive attention. In this paper, the impacts of six equities on the acquisition of USOs, including founders, university, banks, business angels, venture capitals, and other equity, are comprehensively analyzed based on theoretical and empirical studies. Firstly, the average distribution of spin-out equity at formation is calculated based on the sample data of 350 UK USOs. According to this distribution, a radial basis function (RBF) neural network (NN) model is employed to forecast the effects of each equity on the acquisition. To improve the classification accuracy, the novel set-membership method is adopted in the training process of the RBF NN. Furthermore, a simulation test is carried out to measure the effects of six equities on the acquisition of USOs. The simulation results show that the increase of university’s equity has a negative effect on the acquisition of USOs, whereas the increase of remaining five equities has positive effects. Finally, three suggestions are provided to promote the development and growth of USOs.

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

2010 ◽  
Vol 171-172 ◽  
pp. 274-277
Author(s):  
Yun Liang Tan ◽  
Ze Zhang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Creep Curve ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Reference Value ◽  
Network Approach ◽  
Neural Network Approach ◽  
Training Time ◽  
Set Up

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.

scholarly journals A Novel Method for Intrusion Detection Based on SARSA and Radial Bias Feed Forward Network (RBFFN)

2013 ◽  
Vol 7 (3) ◽  
pp. 646-653
Author(s):  
Anshul Chaturvedi ◽  
Prof. Vineet Richharia
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Rbf Neural Network ◽  
Neural Network Approach ◽  
Feed Forward Network ◽  
Machine Learning Approach ◽  
Network Process ◽  
Reinforced Learning ◽  
Attack Data

The Internet, computer networks and information are vital resources of current information trend and their protection has increased importance in current existence. Any attempt, successful or unsuccessful to finding the middle ground the discretion, truthfulness and accessibility of any information resource or the information itself is measured a security attack or an intrusion. Intrusion compromised a loose of information credential and trust of security concern. The mechanism of intrusion detection faced a problem of new generated schema and pattern of attack data. Various authors and researchers proposed a method for intrusion detection based on machine learning approach and neural network approach all these compromised with new pattern and schema. Now in this paper a new model of intrusion detection based on SARAS reinforced learning scheme and RBF neural network has proposed. SARAS method imposed a state of attack behaviour and RBF neural network process for training pattern for new schema. Our empirical result shows that the proposed model is better in compression of SARSA and other machine learning technique.

The Application of RBF Neural Networks in Curve Fitting

2012 ◽  
Vol 490-495 ◽  
pp. 688-692
Author(s):  
Zhong Biao Sheng ◽  
Xiao Rong Tong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Rbf Neural Network ◽  
Engineering Application ◽  
Nonlinear Function ◽  
Rbf Neural Networks ◽  
Neural Network Approach ◽  
Rbf Network ◽  
Fast Learning ◽  
Nonlinear Processing

Three means to realize function approach such as the interpolation approach, fitting approach as well as the neural network approach are discussed based on Matlab to meet the demand of data processing in engineering application. Based on basic principle of introduction, realization methods to non-linear are researched using interpolation function and fitting function in Matlab with example. It mainly studies the RBF neural networks and the training method. RBF neural network to proximate nonlinear function is designed and the desired effect is achieved through the training and simulation of network. As is shown from the simulation results, RBF network has strong nonlinear processing and approximating features, and RBF network model has the characteristics of high precision, fast learning speed for the prediction.

An RBF neural network approach to geometric error compensation with displacement measurements only

2016 ◽  
Vol 28 (6) ◽  
pp. 1235-1248 ◽  
Author(s):  
Rui Yang ◽  
Kok Kiong Tan ◽  
Arthur Tay ◽  
Sunan Huang ◽  
Jie Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Error Compensation ◽  
Rbf Neural Network ◽  
Geometric Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Displacement Measurements

scholarly journals Pesquisa sobre empresas multinacionais brasileiras: análises descritivas e preditivas

Internext ◽  
2015 ◽  
Vol 10 (2) ◽  
pp. 64
Author(s):  
Mario Henrique Ogasavara ◽  
Gilmar Masiero ◽  
Marcio De Oliveira Mota ◽  
Lucas Souza
Keyword(s):  
Neural Network ◽  
Citation Analysis ◽  
Multinational Enterprises ◽  
Descriptive Analysis ◽  
Predictive Analysis ◽  
The Novel ◽  
Neural Network Approach ◽  
Novel Approach ◽  
The Neural Network ◽  
Eclectic Paradigm

<p><em>T</em>his study attempts to review recent research on the internationalization of Brazilian multinational enterprises (I-BMNEs) based on an analysis of the 174 published articles that have appeared in international and Brazilian academic journals, books, and conference proceedings. The descriptive analysis seeks to undertake a citation analysis as well as to provide a typology of the leading researchers and school affiliations, the predominating theoretical and methodological approaches. This paper also proposes a predictive analysis based on a novel approach of neural network in order to classify features of a manuscript and predict the fit of its publication. We find that the research on I-BMNEs is driven by a small number of leading institutions and researchers which utilize case studies as their research method and have the Uppsala and Eclectic Paradigm models as theoretical framework. The citation analysis shows that authors of foreigner origin are cited from journal publications or translated books. The novel technique and design of the neural network approach was modeled to fit for bibliometric studies and the outcomes of the predictive analysis were able to classify correctly 56.25% of the manuscripts. We conclude by providing a set of recommendations to advance the research on I-BMNEs.</p>

The Analysis of Combined Prediction Model of Building Energy Consumption with Grey Theory and RBF Neural Network

2011 ◽  
Vol 374-377 ◽  
pp. 90-93
Author(s):  
Yan Bai ◽  
Qing Chang Ren ◽  
Hong Mei Jiang
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Rbf Neural Network ◽  
Grey Theory ◽  
The Novel ◽  
Grey Prediction ◽  
Consumption Data ◽  
Building Area ◽  
Simulation And Experiment

A kind of new combined modeling method with GM(1,1) and RBNN (Radial Basis Neural Network) is brought forward, according to the idea that the method of neural network can bring grey prediction model a good modified effect. Based on the analysis of the energy consumption data of the existing and the annually-increased building area, the GM(1,1) model was then constructed. And the RBF neural network was used for the model residual error revising. The simulation and experiment results show that the novel model is more effective than the common grey model.

The Method Research of Grey Neural Network Control Based on Data

2014 ◽  
Vol 602-605 ◽  
pp. 1131-1134
Author(s):  
Yong Wei Li ◽  
Zhi Gang Ye ◽  
Chao Chao Huo
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Rbf Neural Network ◽  
Control Process ◽  
Network Control ◽  
Neural Network Control ◽  
Rbf Network ◽  
Sample Data ◽  
Speed Up ◽  
Predict Model

A control process based on data often contains large amounts of sample data. The accuracy of system is always influenced by the randomness of the initial data when the RBF neural network is used to predict model. However, the grey accumulated generating operation (AGO) can reduce the effect of randomness of the initial data, which is able to make data more regular. Based on the two points above, a new kind of method is proposed, which is called grey RBF neural network. This method not only can reduce the randomness, speed up the network convergence, but also improve the modeling accuracy. The grey RBF neural network can be proved to be feasible and effective by applying the grey RBF neural network to the synthetic ammonia decarburization process, and comparing the simulation results with the results which was only using RBF network.

The Forecasting of Short-Term Wind Speed on Wind Farm Based on Phase Space Reconstruction and Neural Network

2012 ◽  
Vol 246-247 ◽  
pp. 496-500
Author(s):  
Ying Ying Su ◽  
Fei Ma ◽  
Hai Yan Zhang ◽  
Zhi Qiang Liao ◽  
Peng Jun
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Wind Speed ◽  
Wind Farm ◽  
Absolute Error ◽  
Phase Space Reconstruction ◽  
The Novel ◽  
Short Term ◽  
Sample Data ◽  
The Mean

The forecasting precision of short-term wind speed is not high for its chaos and time-varying. Aimed at the problem, the novel data space is reconstructed with the best embedding dimension and time delay according to the phase space reconstruction. On the basis, neural network (NN) is used as the modeling tool with the novel sample data. Meanwhile, the structure of NN is confirmed compared with the others on the precision. In the end, the model of short-term wind speed is able to be obtained. The results show that the method is available and the Mean absolute error (MAE) is decreased to 16.2% for 2 hours.

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