Identification of Urban Industry Life Cycle based on Rough Set Neural Network

Abstract

Taking urban economy as the background, a method of urban industry life cycle identification based on rough set neural network is proposed. First, the fuzzy clustering algorithm based on MDV function and information entropy is used to discretize the continuous attributes, and then the rough set theory is used to reduce the important index system. Finally, the training samples are input into the RBF neural network for learning and training, and the industrial life cycle stage of the test samples is judged. Urban industry life cycle recognition based on rough set neural network is used to identify the stages or stages of specific industries. This technology can be used in different industries, such as manufacturing, construction, etc. It is designed to help companies understand their business cycle and how to drive their business. The main idea behind this technology development is that there are many parameters that will affect the performance and success rate of any company. These parameters include: R&D investment; The main purpose of this technology is to clearly understand the current situation, future potential and growth prospects of any particular industry. How does urban industry life cycle recognition base on rough set neural network work? Rough set neural network has been used as a classification tool to analyze data from various sources, such as statistical data and market research reports. The fuzzy clustering algorithm based on MDV function and information entropy can effectively improve the discretization effect. Compared with the commonly used fuzzy evaluation method, this method has higher prediction accuracy for test samples, and is an effective and practical tool for urban industry life cycle identification.