Transmutation and Reconstruction of Enterprise Management Model Framework Based on Improved Markov Model

Abstract

Abstract With the deepening of socialization, the requirements for enterprises are getting higher and higher, which also prompts enterprises to innovate their management models. To enable enterprises to find the correct way to reconfigure their corporate management models, a prediction method based on an improved Markov model is used to forecast the direction of corporate management model reconfiguration. In this paper, the residuals generated by fitting GM (1, 1) at each time node are considered as the expected output values of positive and negative two random states under the state probability distribution, and the squares loss function of the residuals is established as the objective function. To obtain the minimum value of the squares loss function, the gradient descent algorithm is applied to approximate the optimal values of the probability intensity and the pending coefficients of the Kolmogorov equation under the condition of the available small amount of information, and then the Markov model correction value is determined to correct the prediction results of GM (1, 1), which effectively improves the reasonableness and feasibility of the Markov model to predict the results of the enterprise management model architecture reconstruction. The results of the calculation of the data show that According to the calculation results of the data, it can be seen that the gradient descent Markov model prediction results reduce the average relative errors of the three sets of data to 0.214%, 1.582%, and 4.134%, respectively, which is better than various other prediction models. It shows that the gradient descent algorithm based on the squares error loss function under the Markov model can effectively improve the reasonableness of the transfer probability intensity, enhance the reasonableness and reference value of the prediction results, and provide a practical direction for enterprises to reconstruct and transmute their management models under the condition of small sample data.