GENERALIZED INVERSE GROUP OF SIGNAL AND ITS IMPLEMENTATION WITH NEURAL NETWORKS

A new concept, the generalized inverse group (GIG) of signal, is firstly proposed and its properties, leaking coefficients and implementation with neural networks are presented. Theoretical analysis and computational simulation have shown that (1) there is a group of finite length of generalized inverse signals for any given finite signal, which forms the GIG; (2) each inverse group has different leaking coefficients, thus different abnormal states; (3) each GIG can be implemented by a grouped and improved single-layer perceptron which appears with fast convergence. When used in deconvolution, the proposed GIG can form a new parallel finite length of filtering deconvolution method. On off-line processing, the computational time is reduced to O(N) from O(N2). And the less the leaking coefficient is, the more reliable the deconvolution will be.     

光学全交叉网络与SW榕树（F＝S＝2）网络拓扑等价的多样性

本文对有关互连网络拓扑等价的分析方法作了简单评述。并采用互连网络拓扑等价的图分析法分析了自由空间光学互连全交叉网络与ＳＷ榕树网络的拓扑等价及其多样性，拓展了全交叉网络在数字光计算、光子交换及多处理机系统的潜在应用。     

Energy minimization and design for testability

The problem of fault detection in general combinational circuits is NP-complete. The only previous result on identifying easily testable circuits is due to Fujiwara who gave a polynomial time algorithm for detecting any single stuck fault inK-bounded circuits. Such circuits may only contain logic blocks with no more thanK input lines and the blocks are so connected that there is no reconvergent fanout among them. We introduce a new class of combinational circuits called the (k, K)-circuits and present a polynomial time algorithm to detect any single or multiple stuck fault in such circuits. We represent the circuit as an undirected graphG with a vertex for each gate and an edge between a pair of vertices whenever the corresponding gates have a connection. For a (k, K)-circuit,G is a subgraph of ak-tree, which, by definition, cannot have a clique of size greater thank+1. Basically, this is a restriction on gate interconnections rather than on the function of gates comprising the circuit. The (k, K)-circuits are a generalization of Fujiwara'sK-bounded circuits. Using the bidirectional neural network model of the circuit and the energy function minimization formulation of the fault detection problem, we present a test generation algorithm for single and multiple faults in (k, K)-circuits. This polynomial time aggorithm minimizes the energy function by recursively eliminating the variables.     

基于神经网络的河道浅滩演变预测模型

河道浅滩演变是一个复杂的非线性动力学过程, 作者借助神经网络处理非线性问题的优势, 在分析影响河道浅滩演变因素的基础上, 建立了预测河道浅淮演变的BP网络模型, 并对模型中的输入因子和样本的提取进行了探讨. 以闽江竹岐至侯官河段为实例,用“试控法”给出了BP网络模型的建模方案, 用正交设计原理选取相应的训练样本集, 利用该样本集对网络进行学习和训练, 并用训练好的BP网络模型预测浅滩上年内最小水深和年平均淤积厚度. 计算结果表明: 模型预测结果与实际值吻合良好. 这为河道浅滩演变预测研究提供了新方法.     

人工神经网络及其应用

本文首首先介绍了人工神经网络的源泉一体神经网络的主要基本内容，然后简要论述了人工神经网络的主要内容，进程和问题，人工神经网的应用。     

A Method of Combined SHPB Technique and BP Neural Network to Study Impact Response of Materials

Abstract:  A new method combining the split Hopkinson pressure bar (SHPB) technique with the back-propagation (BP) neural network program is proposed. By this method, the treated strain wave signals become smooth with less noises induced by the transverse inertia. Moreover, the dynamic rate-dependent constitutive behaviour of materials can be identified, without any pre-assumption of a constitutive model. It is found that by taking the experimentally measured data of strain, strain rate and time as 'input' and the corresponding data of stress as 'output' of the BP neural network, the dynamic constitutive behaviour with internal damage or phase transformation evolution is easy to be identified, where the time could represent either the internal damage evolution or phase transformation process accompanied with the deformation process. It is emphasised that the data learnt by the BP neural network must include both loading and unloading processes, if the whole loading and unloading response is to be correctly predicted. The comparisons between the predictions and experimental results are in good agreement for both polyamide (PA) polymer (as an example of nonlinear viscoelastic materials) and Ti–Ni alloy (as an example of superelastic materials with stress-induced phase transformation).     

BP网络过拟合问题研究及应用

通过类比信息传递过程中的一般测不准关系式,研究BP网络出现过拟合现象时,代表网络学习能力的权值改变量与反映网络推广能力的未参与训练的检验样本的辨识误差之间的关系.在该关系式中引入一个过拟合参数P,通过对岷江流域紫坪埔站的流量预报,说明在判断网络是否出现过拟合现象时,计算过拟合参数P是一种有研究意义的参考.     

Analytical general results for handoff dwell time distribution in wireless networks

1 Introduction The calling dwell time characteristic is critical for the user network planning and deployment, e.g., global system for mobile communications (GSM), as well as the next generation wireless multimedia networks, such as, the currently standar…     

基于神经网络的雷达辐射源体制和用途智能识别

本文描述一种采用BP神经网络对雷达辐射源体制和用途进行智能识别的方法 ,包括理论分析、神经网络结构设计、推理和学习算法设计、编程算法设计、实验结果比较与分析等。实验表明 ,该方法有着良好的识别效果     

Bioprocess applications of model-based estimation techniques

The last decade has seen the development of a number of approaches for estimating those variables which are difficult to measure on-line in industrial process situations. Whilst a range of techniques is available, a common element is the use of process knowledge in the form of a system model. In the case of bioprocess systems, although a large range of models has been presented in the literature, their use in estimation schemes on an industrial scale has been limited. A number of reasons can be identified for their low level of utilisation. Of particular significance is the uncertainty which exists in quantifying system performance and the process-model mismatch which inevitably results. The level of ‘pre-defined model’ uncertainty, together with the knowledge gained during the course of the fermentation, serves to dictate estimator structure. The paper considers a range of estimation strategies and contrasts, through industrial applications, their performance characteristics and utility.