基于ANN实现的供应需求预测方法

基于人工神经网络技术，建立了后勤供应需求的网络预测方法，并以某区域高校后勤保障需求为例，给出了后勤供应需求预测的步骤与实现过程。     

人工神经网络在组织网络化发展评价中的应用*

通过对给定样本模式的学习,构建的三层神经网络模型能够获取评价专家的经验、知识、主观判断以及对目标重要性的权重协调能力,较好地保证评价结果的客观性.实例证明,人工神经网络方法应用于组织网络化发展评价是可行和有效的.     

基于生物启发计算的警用流动人口分析系统

目前对流动人口的管理仅停留在数据查询比对和简单统计上,缺少对数据的深层次分析,难以对决策指挥提供支持。针对流动人口的分析问题,提出了构建一个基于生物启发计算的智能分析系统,用于发现流动人口中各类人员的流动模式以及流动人口的趋势性问题,找出异常的流动信息和模式。该系统综合运用了前沿的生物启发计算技术——基于多层染色体基因表达式编程算法、重叠基因表达进化算法、基于概念相似度神经网络分类模型和层次距离计算的聚类算法搭建了一个警用流动人口的分析平台。同时根据实际需求,提出了一种新的基于智能分析结果的分级报警模型。实验表明系统具有较高的性能和实用性。     

基于人工神经网络的结构钢回火后力学性能预测

利用多层前向神经网络，使用B－P算法对结构钢回火性能预测进行了研究。通过利用99种钢450余组训练数据样本对神经网络进行训练，建立了结构钢回火后的力学性能与金属成分和回火温度之间的隐性函数。并针对训练用样本不足的问题，设计了为网络提供自学功能软件，在钢完全淬透的前提下，用此神经网络模型可在一定精度范围内预测结构钢的回火力学性能。     

Computer aided detection system for micro calcifications in digital mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications ‘patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.     

Measurement of soybean fatty acids by near-infrared spectroscopy: Linear and nonlinear calibration methods

A key element of successful development of new soybean cultivars is availability of inexpensive and rapid methods for measurement of FA in seeds. Published research demonstrated applicability of NIR spectroscopy for FA profiling in oilseeds. The objectives of this study were to investigate the applicability of NIR spectroscopy for measurement of FA in whole soybeans and compare performance of calibration methods. Equations were developed using partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM) regression methods. Validation results demonstrated that (i) equations for total saturates had the highest predictive ability (r ²=0.91–0.94) and were usable for quality assurance applications, (ii) palmitic acid models (r ²=0.80–0.84) were usable for certain research applications, and (iii) equations for stearic (r ²=0.49–0.68), oleic (r ²=0.76–0.81), linoleic (r ²=0.73–0.76), and linolenic (r ²=0.67–0.74) acids could be used for sample screening. The SVM models produced significantly more accurate predictions than those developed with PLS. ANN calibrations were not different from the other two methods. Reduction in the number of calibration samples reduced predictive ability of all equations. The rate of performance degradation of SVM models with sample reduction was the lowest.     

轮胎在侧向力、纵向力联合工况下的神经网络模型

建立了轮胎在侧向力、纵向力联合工况下的神经网络模型，分析了不同训练方法对模型精度的影响。通过对轮胎实测值与神经网络预测值的对比表明，轮胎神经网络模型的精度能满足工程要求，BP前馈神经网络可有效地应用于车辆轮胎特性的数学建模。     

Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank

The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six‐step procedure. The back‐propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.     

基于神经网络的数字识别

本文介绍了神经网络、数字识别的相关内容及其概念,在此基础上重点研究了基于神经网络的数字识别系统。本文对基于神经网络的数字识别系统包含的功能,以及每个功能模块所使用的技术进行了阐述。最后对基于神经网络的数字识别系统所具有的优点进行了分析和讨论。     

基于遗传算法的BP神经网络优化策略研究

人工神经网络（ANN）可用作机器人控制器,完成多机器人协作搬运作业。针对这种方法收敛速度较慢,误差较大的不足,本文提出基于遗传算法优化的方法。该方法利用遗传算法优化人工神经网络,通过改变ANN结构和遗传算法操作参数,找到最优网络,提高网络收敛速度。仿真结果证明,该方法的可行性与有效性。