A Comparison of Selected Artificial Neural Networks that Help Auditors Evaluate Client Financial Viability
比较反向传播、分类学习和概率神经网络三种人工神经网络在帮助审计师判断客户持续经营能力时的分类准确性和误分类成本，发现概率神经网络整体错误率最低，而分类学习网络误分类成本最小。
ABSTRACT This study compares the performance of three artificial neural network (ANN) approaches—backpropagalion, categorical learning, and probabilistic neural network—as classification tools to assist and support auditor's judgment about a client's continued financial viability into the future (going concern status). ANN performance is compared on the basis of overall error rates and estimated relative costs of misclassificaticn (incorrectly classifying an insolvent firm as solvent versus classifying a solvent firm as insolvent). When only the overall error rate is considered, the probabilistic neural network is the most reliable in classification, followed by backpropagation and categorical learning network. When the estimated relative costs of misclassification are considered, the categorical learning network is the least costly, followed by backpropagation and probabilistic neural network.