The combination of artificial neural networks and traditional industries is an effective way to solve traditional agricultural problems. The results show that risk analysis is effective in any case.Īt present, artificial intelligence is developing rapidly, and artificial neural network algorithm is the core of research. From the test results, the improved credit scoring system is the result of facing speculative and circular credit fraud and implies that the traders of risk commentators are in a leading position in each electronic device. This method is based on site conditions and can evaluate credit risk. Based on the improved credit calculation model, we developed an online clue risk calculation.
The logistic model shows that the profitability factor of the financing company, the debt repayment factor of the financing company, and the profitability of the core company are three factors that have a significant impact on the credit risk of online supply chain finance. Factors, supply chain online degree factors, financing enterprise quality, and cooperation factors, can well measure the credit risk of online supply chains. Using the principal component factor analysis method, seven representative common factors are selected to replace the original variables, which include the profitability factor of the financing enterprise, the solvency factor of the financing enterprise, the profitability factor of the core enterprise, the operation guarantee factor, and the growth ability of the financing enterprise. Two parameter optimization algorithms optimize nonlinear and linear parameters, reduce the computational complexity of SC-IR2FNN, and improve the learning rate. The nonlinear parameters are optimized by an advanced two-level algorithm, and the linear parameters are updated with the minimum biological multiplication. The parameter optimization mechanism based on a joint strategy, namely multilayer optimization engine, can split SC-IR2FNN parameters into nonlinear and linear parameters for joint optimization. In order to optimize the parameters of SC-IR2FNN, we developed a parameter optimization mechanism based on an interaction strategy. Moreover, this mechanism should not set the threshold in advance in practical application. By calculating the similarity and independent contribution of normative neurons, the effectiveness of fuzzy rules can be jointly evaluated, and effective structural changes can be realized.
The self-organization mechanism can be carried out simultaneously with the parameter optimization process. The mechanism includes a comprehensive evaluation algorithm and structure adjustment mechanism. In this paper, we propose a cooperative strategy-based self-organization mechanism to reconstruct the network.
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