舰船红外图象结构特征提取算法的研究

提出两种特征提取算法：ＦＦＴ和样条函数法来提取舰船红外图象结构的特征信息，采用ＢＰ神经网络算法进行识别。实验结果表明，对于高分辨率的舰船上层结构的红外象而言，局部化特征优于全局化特征，即采用样条函数法比ＦＦＴ法有较高识别率。     

Control of a Robotic Manipulator Using Artificial Neural Networks with On-line Adaptation

An adaptive neural system for positioning control of a PUMA 560 manipulator is presented". The computed torque method was implemented with a Multi-Layer Perceptron with on-line learning. The control scheme is implemented into two phases. The first one is the off-line phase in which the neural network is trained with previously known control actions. The second one is the on-line phase in which the neural network parameters are adapted while controlling the manipulator. The control system is able to respond to changes in the manipulator model and to load disturbances. As will be shown, control system performance is improved with the on-line learning strategy presented in this paper.     

人工神经网络在生物过程中的应用

人工神经网络(ANN)由于其自身具有的自适应非线性信息处理能力,在对动力学具有较强的非线性、非稳态及时变特征的生物反应过程的描述中具有其他方法所无法比拟的优势.分析了人工神经网络在生物反应过程中建模的优势,对其近年来在生物反应过程中建模与仿真、过程优化控制、故障诊断、软测量及培养基优化等方面的应用进行了介绍,并提出了其未来发展的趋势.     

基于ANN方法的煤巷掘进前方小构造预报技术

利用煤巷掘进过程中所暴露出的大量的煤层信息，确定了与煤巷掘进前方小构造存在密切关系的影响因素，分析了煤层倾角、煤层厚度、涌水量、瓦斯等各个影响因子，选出主控因子，建立主控因子与煤巷掘进前方小构造预测危险性指数间的人工神经网络（ANN）分析模型，求取了各影响因素对前方小构造预测的权重系数，并建立了煤巷掘进前方小构造预测预报的ANN模型及其判据．对鹤壁十矿煤巷掘进前方小构造进行了预测，结果表明：ANN技术可用于煤矿巷道掘进前方小构造预测．     

Using Boolean factors for the construction of an artificial neural networks

ABSTRACT

We propose a novel approach to define Artificial Neural Network(ANN) architecture from Boolean factors. ANNs are a subfield of machine learning applicable to several areas of life. However, defining its architecture for solving a given problem is not formalized and remains an open research problem. Since it is difficult to look into the network and figure out exactly what it has learnt, the complexity of such a technique makes its interpretation more tedious. We propose in this paper to build feedforward ANNs using the optimal factors obtained from the Boolean context representing a data. Since optimal factors completely cover the data and therefore give an explanation to these data, We could give an interpretation to the neurons activation and justify the presence of a neuron in our proposed neural network. We show through experiments and comparisons on the use data sets that this approach provides relatively better results for some key performance measures.     

基于ANN 及CBR 的产品需求映射实现方法

针对基于实例推理的产品需求映射实现过程中,指标权重设定的随机性及主观性导致无关的噪音特征影响结论的准确性等状况,提出了基于神经网络的CBR权值学习方法（即通过特征灵敏度、活跃度、凸度、相关度进行权值定义）,以及输入特征的转换机制,在此基础上给出了基于CBR的产品需求映射过程模型,并将上述模型在移动工作台需求映射实现中进行了应用,系统实现表明了提出方法的有效性,为产品需求映射提供了一种新的有效实现途径。     

The efficacy of back propagation neural network with delta bar delta learning in predicting the wear of carbide inserts in face milling

Face milling is a process predominantly affected by dynamic variation of cutting forces, thermo-mechanical shocks and vibration leading to catastrophic tool failure along with gradual wear of the inserts. Keeping in view the industrial importance of this process, it is necessary to devise suitable methods to predict in advance the onset of tool failure without grossly impairing the machining set-up and the job. Hence, the applicability of back propagation neural network with delta bar delta learning rule for faster convergence has been studied with the above objective. The multi sensor based tool condition monitoring strategy shows that the learning rate adaptation scheme combined with the selection of suitable process parameters drastically reduces the training time of the artificial neural network without dispensing with the prediction accuracy.     

蚁群神经网络在旅行商问题中的应用

在求解旅行商问题(TSP)时,首先引入交叉策略进行预处理,将具体的地图抽象为常见的无向完全图,即把TSP抽象为求无向完全图的一条Hamilton回路;然后用蚁群算法与人工神经网络相结合的方法来求解.实验结果表明了该方法的可行性和高效性.     

Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach

This paper presents a performance assessment of 88 Association of Southeast Asian Nations banks from 2010 to 2013, using an integrated three‐stage approach on financial criteria that emulates the CAMELS rating system. More precisely, fuzzy analytic hierarchy process is used first to assess the relative weights of a number of criteria related to capital adequacy (C), asset quality (A), management quality (M), earnings (E), liquidity (L), and sensitivity to market risk (S) based on the opinion of 88 Association of Southeast Asian Nations experts. Then, these weights are used as technique for order of preference by similarity to ideal solution inputs to assess their relative efficiency. Lastly, neural networks are combined with technique for order of preference by similarity to ideal solution results to produce a model for banking performance with effective predictive ability. The results reveal that contextual variables have a prominent impact on efficiency. Specifically, parsimony in equity leveraging derived from Islamic finance principles may be the underlying cause in explaining higher efficiency levels.     

均匀沙床面动床阻力计算的人工神经网络模型

针对泥沙运动数据含有噪音且样本数据较多的特点,提出采用人工神经网络(ANN)BP模型批学习的训练方法可有效地缩短计算时间、提高训练精度。建立了由能坡、无因次单宽流量和无因次泥沙粒径等3个参数预测水深的结构为3-33-1的冲积河槽动床阻力BP模型。预测结果与实测资料比较表明这个人工神经网络模型的预测精度较高,同时这个模型与21个动床阻力公式的比较表明人工神经网络模型要比传统的回归模型精度高。