Hongwei Wu
Shijiazhuang Information Engineering Vocational College, Shijiazhuang,050000,China

DOI:https://doi.org/10.5912/jcb1080


Abstract:

Aiming at the problem of low recognition accuracy caused by the high probability of training model falling into local extreme value in audit credit guarantee risk identification, an audit credit guarantee risk identification method based on LM algorithm is proposed. The risk identification index system is established and its weight is assigned to provide the index basis of the input layer for the identification model. The BP neural network is improved by LM algorithm to reduce the probability of falling into local extreme value, and the audit credit guarantee risk identification model is established by using the improved network. Using the same audit credit guarantee risk sample to test the identification method, the identification accuracy of this method is 95.15%, which is 4.86% and 4.61% higher than that based on BP neural network and LVQ network respectively. Therefore, the identification results of the design method are more feasible. As a result of the Bio Revolution, businesses will have access to a whole new set of capabilities. New specialized skills in domains like genomics, molecular biology, biochemistry, and neurology will be in great demand, similar to the recent surge in data-science and software-engineering abilities. In fact, the combination of digital and biological expertise is a formidable one. These new capabilities are enabling more efficient and less expensive manufacturing processes, as well as better-performing inputs with superior properties, which in turn enable the delivery of goods and services with higher added value to end users. It's understandable that so many players are vying for the benefit of being the first to shift the needle.