RUI: Multilayer Neural Network With Multi-Valued Neurons, its Application to Image Recognition and Processing and Incorporation of the Research Results into the Educational Process

RUI：具有多值神经元的多层神经网络，其在图像识别和处理中的应用以及将研究成果纳入教育过程

• 批准号: 0925080
• 负责人: Igor Aizenberg
• 金额: $ 29.93万
• 依托单位: Texas A&M University-Texarkana
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0925080&HistoricalAwards=false
• 关键词: RUI; Multilayer; Neural; Network; Multi

Abstract"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)"The project focuses on the development of a new tool for solving pattern recognition problems. This tool is based on the application of an original artificial neural network ? multilayer neural network with multi-valued neurons. This network has a simple and productive learning algorithm, which allows to solve recognition and classification problems that are difficult to approach using other techniques. An example is multiple-class classification problems. The project involves solving multiple-class image recognition problems, such as texture classification, textural segmentation, blurred images recognition, and intelligent edge detection.The research involves study of nonlinear phenomena of a multilayer neural network based on multi-valued neurons (MLMVN). A multi-valued neuron (MVN) is a complex-valued neuron whose inputs and output are located on the unit circle. A multilayer neural network based on this neuron has a derivative-free self-adaptive learning algorithm. It outperforms other techniques in terms of training speed and classification/prediction rates. The following problems are considered in this project. The relationship between the topology of MLMVN and the quality of multiple-class classification is investigated. MLMVN is used for texture classification, textural segmentation, and as an edge detector for noisy images. The Fourier phase spectrum is used as a feature space for blurred images recognition using MLMVN. A hardware implementation of MVN and MLMVN is also considered. These studies will involve undergraduate students, who participate in scientific research as part of their education at Texas A&M-Texarkana. Hence, in addition to obtaining answers to fundamental questions related to artificial neural networks and pattern recognition, this project also is closely tied to educating students who will become scientists and engineers.

摘要“该奖项由2009年美国复苏和再投资法案（公法111-5）资助”该项目的重点是开发解决模式识别问题的新工具。该工具是基于原始人工神经网络的应用？多值神经元多层神经网络该网络具有简单而高效的学习算法，可以解决使用其他技术难以解决的识别和分类问题。一个例子是多类分类问题。该项目涉及解决多类图像识别问题，如纹理分类、纹理分割、模糊图像识别和智能边缘检测，研究基于多值神经元的多层神经网络（MLMVN）的非线性现象。多值神经元（MVN）是一种复值神经元，其输入和输出位于单位圆上。基于该神经元的多层神经网络具有无导数的自适应学习算法。它在训练速度和分类/预测率方面优于其他技术。本项目考虑了以下问题。研究了MLMVN的拓扑结构与多类分类质量之间的关系。MLMVN用于纹理分类，纹理分割，并作为噪声图像的边缘检测器。傅立叶相位谱作为特征空间，用于模糊图像的MLMVN识别。MVN和MLMVN的硬件实现也被认为是。这些研究将涉及本科生，他们参加科学研究作为他们在德克萨斯州A M-Texarkana教育的一部分。因此，除了获得与人工神经网络和模式识别相关的基本问题的答案外，该项目还与教育将成为科学家和工程师的学生密切相关。