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

Abstract

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.

Public Budget Revenue Model Based on SVR and Neural Network-A Case Study of Shanxi Province

“…The choice of influencing factors has a significant impact on the prediction accuracy of the model. Most researchers in past studies [1][2][3][4][5][6][7] selected a number of factors as influencing factors of public budget revenue, including the number of employees in employment, total wages, total retail sales of consumer goods, fixed asset investment, resident consumption index, gross regional product, primary industry, secondary industry, and tertiary industry. In Li Min's study on the impact of fiscal revenue in Gansu Province [1], the largest number of factors was as many as 39 and the smallest number of factors was 5.…”

“…Peng Qin [3] used minimum angle regression to solve the adaptive Lasso estimation, which eliminates some covariates and variables with small effects. Peiyu Liu [4] used lasso and random forest for variable screening, respectively. Yang He [5] demonstrated that lasso regression has great advantages in the variable selection process.…”

“…On the construction of the research forecasting model, Peng Qin [3] used a BP neural network model to forecast fiscal revenue in Anhui province. Peiyu Liu [4] used a combined BP and RBF neural network model to predict the fiscal revenue of Hunan province. Jingjing Ren and Shangbin Gao [6] used GM(1,1) to predict each influencing factor and built a fiscal revenue forecasting model using SVR.…”

State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks

“…To overcome some shortcomings in the aforementioned methods for the battery pack SOC estimation, this paper presents an improved RBF method using a fast recursive algorithm (FRA) to estimate the SOC of a battery pack. e FRA method [27] can be used for both neural inputs selection [28] and hidden layer node selection [29][30][31] in the configuration of RBF networks. Comparing to [32], the average cell temperature, the time mean pack voltage, the time mean pack temperature, and the time mean loop current all over 10 seconds intervals can be also added to the initial candidate pool of input variables, other input candidates can also be included such as the maximum cell voltage, the minimum cell voltage, the average cell voltage, and loop current.…”