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

摘要：“该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的”，该项目侧重于开发解决模式识别问题的新工具。这个工具是基于原始人工神经网络的应用？具有多值神经元的多层神经网络。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。该项目涉及解决纹理分类、纹理分割、模糊图像识别、智能边缘检测等多类图像识别问题。研究了基于多值神经元（MLMVN）的多层神经网络的非线性现象。多值神经元（MVN）是一种输入和输出位于单位圆上的复值神经元。基于该神经元的多层神经网络具有无导数自适应学习算法。它在训练速度和分类/预测率方面优于其他技术。本项目主要考虑以下问题：研究了MLMVN拓扑结构与多类分类质量之间的关系。MLMVN用于纹理分类、纹理分割以及作为噪声图像的边缘检测器。将傅里叶相位谱作为特征空间，利用MLMVN对模糊图像进行识别。本文还考虑了MVN和MLMVN的硬件实现。这些研究将涉及本科生，他们参与科学研究，作为他们在德克萨斯A&amp - texarkana大学教育的一部分。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，该项目还与培养将成为科学家和工程师的学生密切相关。

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.