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.

中文翻译：

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基于GA-BP神经网络的创新生态系统知识融合风险评估

摘要 创新生态系统中知识融合的风险评估直接关系到这些生态系统的成败。因此，提出了一种通过遗传算法 (GA) 优化的反向传播 (BP) 神经网络来评估创新生态系统中知识融合的风险。首先，构建创新生态系统知识融合风险评价指标体系，通过问卷收集数据作为神经网络的训练数据。为了实现机器学习，生成了 84 个数据集，其中 60 个用于训练网络，24 个用于在 MATLAB（R2014b）中测试网络。然后通过BP神经网络和GA-BP神经网络构建评价模型，通过比较评价值与目标值来判断其准确性。对比表明，GA-BP神经网络收敛速度更快，稳定性更高，可以更频繁地达到目标，降低了BP神经网络陷入局部最优而不是达到全局最优的可能性。GA-BP神经网络模型为创新生态系统知识融合风险评估提供了一种新的实践方法。