Faculty Early Career Development: Resampling Approaches to Neural Model Validation

9502134本研究的重点是通过开发用于模型验证的神经适应和混合统计重采样方法来改进神经网络模型的验证。统计领域已经有几种成熟的统计模型验证方法，这些方法使用可用的数据进行模型构建和验证。重采样方法的缺点是需要构建多个模型。几个模型的构建是计算密集型的，不像它们的确定性统计对应物。此外，在统计建模中不存在神经网络模型构建的随机方面。本研究旨在通过研究验证过程的精度、偏差和方差之间的权衡，以及三种主要的重新采样技术（交叉验证、jackknife和bootstrap）以及两种最常用的神经网络验证技术（重新替换和训练-测试）的计算需求，开发神经适应性的重新采样方法。其他需要调查和解决的研究问题是替代误差度量，神经网络权重的初始化，混合重采样方法，神经网络训练终止标准，以及通过委员会网络方法重用多个神经网络模型。开发的方法将在典型的制造问题上进行演示，例如电路板的波峰焊，作业车间金属成形的工艺规划以及数据稀疏的其他应用。神经网络验证技术的改进对于复杂系统建模和优化的进一步研究和实现至关重要。该研究有可能影响目前涉及神经网络建模的几个学科，其中数据稀疏，模型验证至关重要。这些领域包括精密制造、机器人控制、医疗诊断以及防御模式分类和控制应用。待开发的方法可以推广到其他经验建模技术，其中模型的构建是计算密集型的。

9502134 The focus of this research is the improvement of the validation of neural network models by developing neuro-adaptations and hybrids of statistical resampling methodologies for model validation. The field of statistics has matured several approaches to statistical model validation which use the available data for both model construction and validation. Resampling approaches have the drawback that they require the construction of multiple models. The construction of several models is computationally intensive, unlike their deterministic statistical counterparts. Furthermore, there are stochastic aspects to neural network model construction that are not present in statistical modeling. This research is aimed at developing neuro-adaptations of resampling approaches by investigating the trade-offs between precision, bias, and variance of the validation procedure with the computational requirements for each of the three major resampling techniques - cross validation, jackknife, and bootstrap - along with the two most commonly used neural network validation techniques - resubstitution and train-and-test. Other research issues to be investigated and resolved are alternative error metrics, initialization of neural network weights, hybrid resampling methodologies, neural network training termination criteria, and reuse of multiple neural network models through the committee network approach. The methodologies developed will be demonstrated on typical manufacturing problems such as wave soldering of circuit boards, process planning for job shop metal forming, and other applications where data is sparse. The improvement of validation techniques for neural networks is essential for furthering research and implementation of complex systems modeling and optimization. The research has the potential to impact several disciplines currently involved in neural network modeling where data is sparse and model validation is crucial. These areas include precision manufacturing, robotic control, me dical diagnosis, and defense pattern classification and control applications. The methods to be developed can be generalized to other empirical modeling techniques where the construction of the model is computational intensive.