An Evaluation Of The Enterprise’s Business Status Based On Intelligent Algorithm

The business status of enterprises is the primary attention problem of the enterprise, which could help investors understand the advantages and disadvantages of the enterprise objectively, grasp information accurately and assess the enterprise entirely. So it is very important to evaluate the operating status of the enterprise.After the summary of enterprise’s business status research background, significance and method, neural network, bat algorithm, firefly algorithm, the problem of the neural network and its improved algorithm for evaluation of the enterprise business are focused on.Firstly, an evaluation model of enterprise’s business status based on BA-BP algorithm is set up. Since the traditional BP neural network’s initial connection weights and thresholds are selected at random, and it is easy to make the network into a local optimum. Aimed at the shortcoming, we put forward an improved algorithm--BA-BP algorithm which uses the bat algorithm to optimize BP network’s parameters, and apply the algorithm to the evaluation of the business situation. The simulation results show that the prediction accuracy of BA-BP is higher than the traditional BP neural network.Secondly, an evaluation model of the enterprise’s business status based on the optimized BP of Niche Firefly Algorithm(NFA-BP) is set up. Since the firefly algorithm has the disadvantage of appearing premature convergence and trapping in local optima, we introduce niche technology and put forward an improved niche firefly algorithm(NFA) to optimize BP neural network’s parameters. The simulation results show that the correct recognition rate of the enterprise’s business status based on NFA-BP algorithm is higher than the traditional BP model.Thirdly, an evaluation model of the enterprise’s business status based on gaussian white noise disturbance of bat-radial basis function(GWBA-RBF) algorithm is set up. Since the basic bat algorithm has the disadvantage of trapping in local optima, an improved algorithm is brought forward, which is added in gaussian white noise disturbance near the location of the optimum bat to enhance its vitality, and the optimal initial parameters of RBF neural network is searched by it. The simulation results show that the correct recognition rate of the enterprise’s business status based on GWBA-RBF algorithm is higher than the traditional RBF model, so it is an effective evaluation model.Finally, the content of this article is summarized, and a further prospect in the future is made.