基于补偿模糊神经网络的集群创新能力评价

目的为了提高产业集群创新能力及企业的整体创新水平，增强产业国际竞争力．方法将补偿模糊神经网络算法应用到产业集群创新能力评价当中，构建了基于网络结构的集群创新能力评价指标体系，通过因子分析将指标体系精简为6个因子，以6个因子为输入，建立补偿神经网络模型，实现对集群创新能力的评价研究．结果应用Matlab进行编程运算，从实验结果来看，误差非常小，训练结果令人满意，取得了很好的效果．结论该方法适用于产业集群创新网络能力的绩效评价，从而为研究产业集群的评价提供了一个新的思路．

Research on Innovation Ability of Industrial Clusters Based on Compensative Fuzzy Neural Network

In recent years, the research about industrial cluster has aroused widespread concern in academic field. A batch of big and small industrial clusters emerged all over the country, which have driven the economic development of our country, however, their innovation ability is not strong. So analyzing and appraising the innovation level of enterprises and strengthening the international competitiveness of Chinese industries are very necessary. The innovative ability and success of enterprises are indispensable with the enterprises＂ network structure, which benefits the enterprises in the following aspects to enhance the innovative ability, to deduce the cost of innovation, to increase the innovative efficiency, to speed up the new product innovation, and to motivate the enterprises in innovation. Based on the construction of the network perspective, we construct the industrial cluster innovation capability evaluation index system and apprais the innovation ability of industrial clusters in our country applying compensative fuzzy neural network theory.