Abstract
Abstract In order to explore the application of machine learning algorithm to intelligent analysis of big data in an artificial intelligence (AI) environment, make cognitive computing meet the requirements of AI and better assist humans to carry out data analysis, first, the theoretical basis of machine learning algorithm is elaborated. Then, a cognitive computational model based on the machine learning algorithm is proposed, including the essence, principle, function, training method of deep belief network (DBN) algorithm, as well as the joint use of DBN algorithm and multilayer perceptron. Finally, the proposed algorithm is simulated. The results show that under the same parameter conditions, the accuracy rate of the DBN algorithm combined with multilayer perceptron is higher than that of the DBN algorithm; when the number of units is >40, the accuracy rate of the DBN algorithm combined with multilayer perceptron is significantly higher than that of the DBN algorithm; when the number of units is 30, the best effect can be obtained, and the error rate is <0.05, but the DBN algorithm cannot achieve this effect alone; when the number of network layers is specified as four, the error rate of the DBN algorithm combined with multilayer perceptron is <0.05, forming the optimal level. In the AI environment, the performance of the cognitive computational model based on the DBN algorithm and multilayer perceptron can reach the highest level, which makes the computer become a handy intelligent auxiliary tool for human beings.
Highlights
With the deepening of global economic integration, the financial markets of various countries and regions have become more closely related, and their correlation has become more obvious [1]
Understanding and seizing the correlation between financial markets is of great significance for effectively avoiding the spread of financial crisis
In traditional correlation studies of financial variables, methods such as Pearson correlation coefficient, Spearman correlation and Granger causality test are often used, which are with great limitations [4,5,6]
Summary
With the deepening of global economic integration, the financial markets of various countries and regions have become more closely related, and their correlation has become more obvious [1]. The Copula function has gradually become one of the main methods to explore the correlation between financial variables. The single Copula function adopted to explore correlations in financial markets can lead to large errors [7, 8]. There are few studies on how to measure the portfolio risk by combining the Copula function with HMM. The results of this study aim to provide a theoretical basis for exploring the risk-dependent structure among financial variables in China so as to conduct accurate risk assessment
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