An Enhanced Linear Programming Approach to the Design and Training of Neural Networks for Pattern Classification

WPCD 2 B V P Z快递10cpi？X，x 6 X@8；X@HP LaserJet IIIP HPLSIIIP.PRS x@，\，|X@2 2B#|x 9224810 Ignizio模式分类问题的例子(各种已知为：模式识别、判别分析和模式分组)似乎随处可见。通常，此类问题涉及将对象分配给各种组或类的需要。最近解决这个问题的尝试采用了线性规划方法，特别是神经网络。在这项研究中，将开发一种将线性规划(特别是传统的线性规划和/或线性目标规划)与神经网络相结合的方法，这种方法应该被证明是对开发这种组合的早期和正在进行的尝试的重大进步。通过线性规划进行神经网络设计/训练的持续努力既没有考虑到硬件复制的具体细节，也没有包括对硬件复制的细节进行足够严格的处理。研究人员将开发和评估所有相关网络处理元素和连接的详细表示，以便最终允许通过硬件进行直接复制。

WPCD 2 B V P Z Courier 10cpi ? x x x , x 6 X @ 8 ; X @ HP LaserJet IIIP HPLSIIIP.PRS x @ , \ , |X @ 2 2 B #| x 9224810 Ignizio Examples of the pattern classification problem (known variously as: pattern recognition, discriminant analysis, and pattern grouping) seem to be found virtually anywhere and everywhere. In general such problems involve the need to assign objects to various groups, or classes. Recent attempts to solve this problem have employed linear programming methods and, in particular, neural networks. In this research an approach will be developed that combines linear programming (specifically, traditional linear programming and/or linear goal programming) with neural networks an approach that should prove to be a significant advance over earlier and ongoing attempts toward the development of such combinations. Ongoing efforts toward neural network design/training via linear programming have failed to either consider or include an adequately rigorous treatment of the specifics of hardware replication. The researchers will develop and evaluate a detailed representation of all pertinent network processing elements and connections in order that direct replication via hardware ultimately be permitted.