Risk assessment of knowledge fusion in an innovation ecosystem based on a GA-BP neural network |
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Affiliation: | 1. School of Management, Jilin University, Changchun, China;2. Graduate School, Jilin Jianzhu University, Changchun, China;1. Key Laboratory for Bionics Engineering of Education Ministry, Jilin University, Changchun 130022, China;2. School of Engineering, Jiangxi Agricultural University, Nanchang 330045, China;3. Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100083, China;4. China North Vehicle Research Institute, Beijing 100071, China;1. Key Laboratory of Smart Grid of Ministry of Education (Tianjin University), Tianjin 300072, China;2. Hulunbuir University, Hailaer 021008, China;3. Electrical and Computer Engineering Department, Clarkson University, Potsdam, NY 13699, USA |
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Abstract: | The risk assessment of knowledge fusion in innovation ecosystems is directly related to these ecosystems’ success or failure. A back-propagation (BP) neural network optimized by a genetic algorithm (GA) is thus proposed to evaluate the risk of knowledge fusion in innovation ecosystems. First, an index system is constructed for evaluating the risk of knowledge fusion in innovation ecosystems, and data are collected by questionnaire for use as training data for the neural networks. To realize machine learning, 84 datasets were generated, of which 60 were used to train the network, and 24 were used to test the network in MATLAB (R2014b). Evaluation models were then constructed by the BP neural network and GA-BP neural network, and their accuracy was judged by comparing the evaluation value with the target value. The comparison shows that the GA-BP neural network has faster convergence speed and higher stability, can achieve the goal more often, and reduces the possibility of the BP neural network falling into a local optimum instead of reaching global optimization. The GA-BP neural network model for the knowledge fusion risk assessment of innovation ecosystems provides a new method for practice. |
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Keywords: | GA-BP neural network Risk assessment Knowledge fusion Innovation ecosystem |
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