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This paper combines associative memory neural networks with enterprise financial management risks, studies the synchronization control and stability analysis problems of unidirectional associative memory\u2010like human brain amnestic neural networks with perturbation and mixed time\u2010varying time lags, proposes a bidirectional associative memory\u2010like brain stochastic amnestic neural network model with mixed time\u2010varying time lags, designs a discrete\u2010time sampling control strategy based on the model, and studies various types of recent financial risks. Based on the early warning research, based on the associative memory neural network method, we propose to reconstruct the risk categories, including improving the enterprise risk management system, enhancing the awareness of financial risk management from top to bottom, and strengthening the core competitiveness of the enterprise itself and control measures for financing, investment, operation, and cash flow risks.<\/jats:p>","DOI":"10.1155\/2021\/4383742","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T14:35:13Z","timestamp":1638455713000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Empirical Analysis of Corporate Financial Management Risk Prediction Based on Associative Memory Neural 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