The performance of venture capital not only reflects the fiduciary responsibility, but also provides useful information for investment decisions, is the management guide for investment managers, is the investment guide for investors, it is not only related to the healthy development of venture capital industry, but also has important significance for the prosperity of the entire financial market. As an investment tool with co-existence of risk and income, the risk of fund is mainly manifested in the volatility of fund price. The prediction of fund price volatility can reflect the overall situation of fund companies, and the accurate prediction of fund price has important guiding significance for investment decisions. Based on animal disease prevention and control management system, the fund net value prediction model was established, first with nonlinear damping least square method to optimize Elman neural network learning algorithm, at the same time to carry on the dynamic adjustment, the artificial fish algorithm parameters make the early stage of the optimization can quickly obtain the global optimal solution domain, the late local search to improve the accuracy of the optimal solution. Combined with the advantages of the artificial fish algorithm and Elman neural network, the improved artificial fish algorithm for finding a set of optimal initial weights and threshold, the neural network to establish artificial fish neural network forecasting model, based on an empirical analysis of the representative of the investment fund, and comparing and grey model to predict the results of the analysis, show that the model has good nonlinear reflection ability and learning capability, prediction precision and can accurately predict the change trend of fund net value and the rise and decline of the turning point, to predict the tendency of fund net value provides a effective method.

Heng Zhang. Livestock Disease Prevention and Control Management System in Financial Venture Capital. Zoology and Animal Physiology (2022), Vol. 3, Issue 2: 1-18. https://doi.org/10.38007/ZAP.2022.030201.

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