基于多元相关分析和三值逻辑神经元模型的银行风险评估

Peng Dong, Feng Dai, Yingjiang Li
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引用次数: 0

摘要

大多数问题都具有不确定性和模糊性。针对二值逻辑神经元的不足,提出了基于多重相关分析的三值逻辑神经网络,并成功应用于银行风险评估。本文首先构建了多重相关模型。其次,提出了三值逻辑神经网络,并解释了其推理规则等基本操作。最后,结合银行风险评估并运用多重相关分析,确定了评价指标,构建了评价神经网络模型。结果表明,该模型可用于三值连接规则,实现三值逻辑推理。这为不确定性知识的表达和推理提供了一种有力的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Risk Assessment of Bank Based on Multiple Correlation Analysis and Three-Valued Logic Neuron Model
Most of questions have the uncertainty and the illegibility. Taking account of the insufficiency of two value logic neuron, the paper puts forward three-valued logic neural network, based on the multiple correlation analysis, which have been successfully used in the bank risk assessment. The paper, firstly has constructed the multiple correlation model. Secondly, puts forward the three-valued logic neural network and explains its basic operations such as inference rules. Finally, combining of the bank risk assessment and using the multiple correlation analysis and has determined the evaluation index and constructed an assessment of the neural network model. The result indicates that this model can be used in three-valued connection rules to realize three-valued logic reasoning. This has provided one powerful method for the uncertainty knowledge's expression and the inference.
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