Interior-Point Methods in Artificial Neural Networks

该项目将调查 应用内点的好处 学习算法的技术 在神经网络中。 私家侦探将 试图通过使用 内点方法的工具， 神经网络算法，如 可以改进反向传播 在学习时间和质量上 通过优化给定的 学习方法。 作为 介绍性研究，这项建议 将调查 学习BP算法 分析中心（Huard 1967， Sonnevend 1985，Renegar 1989）。一 开发了类似的方法， Trafalis（1989）研究， Abhyankar，Morin和Trafalis 1990年：多目标 优化问题 拟议的研究将是 根据三部分进行 计划：（1）分段考虑 线性凸激活函数 研究结果将 然后将其推广到 一般激活功能（例如， sigmoid函数）;（2）设计， 实现和计算测试 学习算法， 在第一阶段发展的 （3）测试开发的 视觉问题中的学习规律 与医疗应用有关， 癌症诊断

This project will investigate the benefits of applying interior point techniques to learning algorithms in neural networks. The P.I. will attempt to show that by using the tools of interior point methods, a neural network algorithm such as back propagation can be improved both in learning time and quality of solution by optimizing the given methods of learning. As an introductory study, this proposal will investigate the effects on learning in BP by studying a method of analytical centers (Huard 1967, Sonnevend 1985, Renegar 1989). A similar approach was developed and investigated by Trafalis (1989), Abhyankar, Morin and Trafalis (1990) for multiobjective optimization problems. The proposed research would be conducted according to a three-part plan: (1) To consider piecewise linear convex activation functions. The findings of the research will then be generalized to cover more general activation functions (e.g. sigmoid functions); (2) To design, implement and computationally test the learning algorithms which were developed in phase one of the research; (3) To test the developed learning laws in vision problems related to medical applications in cancer diagnosis.