Modified multi-layered perceptron applied to packing and covering problems

Prior knowledge of the input–output problems often leads to supervised learning restrictions that can hamper the multi-layered perceptron’s (MLP) capacity to find an optimal solution. Restrictions such as fixing weights and modifying input variables may influence the potential convergence of the back-propagation algorithm. This paper will show mathematically how to handle such constraints in order to obtain a modified version of the traditional MLP capable of solving targeted problems. More specifically, it will be shown that fixing particular weights according to prior information as well as transforming incoming inputs can enable the user to limit the MLP search to a desired type of solution. The ensuing modifications pertaining to the learning algorithm will be established. Moreover, four supervised improvements will offer insight on how to control the convergence of the weights towards an optimal solution. Finally, applications involving packing and covering problems will be used to illustrate the potential and performance of this modified MLP.     

Treatment of skewed multi-dimensional training data to facilitate the task of engineering neural models

Successful application of neural network models relies heavily on problem-dependent internal parameters. As the theory does not facilitate the choice of the optimal parameters of neural models, these can solely be obtained through a tedious trial-and-error process. The process requires performing multiple training simulations with various network parameters, until satisfactory performance criteria of a neural model are met. In literature, it has been shown that neural models are not consistently good in prediction under highly skewed data. Consequently, the cost of engineering neural models rises in such circumstance to seek for appropriate internal parameters. In this paper the aim is to show that a recently proposed treatment of highly skewed data eases the task of practitioners in engineering neural network models to meet satisfactory performance criteria. As the applications of neural models grows dramatically in diverse engineering domains, the understanding of the treatment show indispensable practical values.     

连续字符识别的级联HMM训练算法

用较简洁的模型表达词典中成千上万的词条以及由这些词组成的类别无以计数的句子,是连续手写识别技术要解决的关键问题之一.利用HMM对时间序列的较强的建模能力这一特点,提出了可用于连续字符识别的HMM级联模型;给出了字符HMM模型连接概率和模型间状态转移概率的定义,并通过修正训练算法的重估公式,重估字符模型的连接参数,描述了用于手写体识别的字符HMM的设计方案;给出了级联训练算法重估公式和级联Baum-Welch训练算法描述.将级联方法应用于手写体英文单词的识别,结果表明,级联模型的描述能力与比较系统相比有明显的优势,获得了良好的识别性能.     

Assessment of the performances of multilayer perceptron neural networks in comparison with recurrent neural networks and two statistical methods for diagnosing coronary artery disease

Abstract: We aimed to examine the diagnostic performances of multilayer perceptron neural networks (MLPNNs) for predicting coronary artery disease and to compare them with different types of artificial neural network methods, namely recurrent neural networks (RNNs) and two statistical methods (quadratic discriminant analysis (QDA) and logistic regression (LR)). MLPNNs were trained with backpropagation, quick propagation, delta-bar-delta and extended delta-bar-delta algorithms as classifiers; the RNN was trained with the Levenberg–Marquardt algorithm; LR and QDA were used for predicting coronary artery disease. Coronary artery disease was classified with accuracy rates varying from 79.9% to 83.9% by MLPNNs. Even though MLPNNs achieved higher accuracy rates than the statistical methods, LR (73.2%) and QDA (58.4%), their performances were lower compared to the RNN (84.7%). Among the four different types of training algorithms that trained MLPNNs, quick propagation achieved the highest accuracy rate; however, it was lower than the RNN trained with the Levenberg–Marquardt algorithm. RNNs, which demonstrated 84.7% accuracy and 86.5% positive predictive rates, may be a helpful tool in medical decision making for diagnosis of coronary artery disease.     

Algorithm for construction of parametric vectors for solution of classification problems by a feed-forward neural network

A universal heuristic algorithm is considered that constructs the vector of properties of an object that are distributed among heterogeneous groups. This algorithm is based on a feed-forward neural network. An automatic system for objects classification is described. The use of the automatic classification system for a literary authorship attribution problem is considered. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 3–14, March–April 2007.     

求解约束优化问题的文化算法研究

文化算法的主要思想是明确地从进化种群中获得求解问题的知识 (即信念) 并用于指导搜索过程. 本文提出了一种基于多层信念空间的文化算法, 该算法通过对多层信念空间的择优选用将提取的知识用于提高进化计算性能来解决约束优化问题. 应用实例表明该算法具有较好的结果和较少的计算量.     

基于小波描述子和神经网络的形状识别

提出一种把小波描述子和神经网络相结合的形状识别方法。通过小波描述子提取待识别物体的形状特征,所提取的特征值不受物体位移、缩放和旋转的影响,接着用多层分类器网络对物体的形状进行识别分类,并采用BP算法对神经网络进行学习和训练。最后得出令人满意的实验结果。     

面向方面编程在B/S多层架构中业务层的应用

介绍了基于面向方面编程(AOP)基本思想,分析了B/S多层架构中面向对象编程所面临的困境,介绍如何将AOP思想应用于B/S多层架构中的业务层,相比纯面向对象的实现突出显示了AOP在业务层事务处理上优越性。它主要体现在两个方面:系统架构清晰,可读性、复用性、扩展性增强;降低系统的耦合性,有利于团队成员分工合作,减少主业务开发人员负担。     

基于C/S的CAD培训信息管理系统的设计与实现

在认真分析CAD培训信息管理需求的基础上,从系统的业务流程、整体结构、数据库3个主要方面阐述了系统开发的设计方案,选用Windows XP、SQL Server 2000、Power Builder 9.0作为系统开发的平台和工具,实现了一套基于C/S的CAD培训信息管理系统.该系统具有基本信息管理等7大功能模块,而且具有良好的可靠性、可移植性、可维护性和安全性,容易与Internet/Intranet集成起来,也可以扩展到IT培训信息的管理,使繁琐的培训信息管理工作变得轻松愉快.     

基于单片机的冲击波探测处理系统设计

本文提出了基于单片机的水下爆炸冲击波超压信号探测处理系统设计,详细介绍了系统硬件组成和软件的主程序流程.该单片机系统能够通过探测水下爆炸冲击波峰值超压,来判断对潜模拟攻击的有效性,并用有色烟雾来指示.该系统可以作为反潜训练器材使用.