非线性分布参数系统的块脉冲函数辨识法及截断项数的最佳选取

本文介绍了利用二维块脉冲函数辨识一类非线性分布参数系统的方法，强调并进行了这种近似处理下的误差分析，通过构建一非线性数规划模型讨论了截断项数的最佳选取问题，给出了两个实例以分别说明有关的概念和方法。     

Hybrid learning concepts based on self-organizing neural networks for adaptive control of walking machines

Online learning of complex control behaviour of autonomous mobile robots like walking machines is one of the current research topics. In this article, a hybrid learning architecture based on reinforcement learning (RL) and self-organizing neural networks for online adaptivity is presented. The hybrid concept integrates different learning methods and task-oriented representations as well as available domain knowledge. The proposed concept is used for RL of control strategies on different control levels on a walking machine.     

核废物处置库厂址的地震地质评价

讨论了核废物处置库地震地质评价的基本目的和任务，提出了与能动构造和设计基准地震动相关的５项评价内容，给出了它们之间的逻辑关系和评价程序．     

边际函数的绝对连续性

本文给出边际函数的绝对连续性性质．     

有教师的线性基本函数前向三层神经网络结构研究

优化选择隐节点数是人们在应用基于误差反传算法的有教师的线性基本函数前向三层神经网络过程中首先遇到的一个十分重要而又困难的问题。本文从国内外大量应用裕列中陪结归纳出了一个初定这种网络隐节点数的经验公式，提出了一种判断所选隐节点数是否多余的具体方法，并从理论上做了详细的推导。     

Philippe Flajolet's Research in Analysis of Algorithms and Combinatorics

Philippe Flajolet's research in theoretical computer science spans over more than 20 years. He made lasting contributions to the analysis of algorithms and analytic combinatorics. Among many of his results we mention here some in such diversified topics as enumeration, number theory, formal languages, continued fractions, automatic analysis of algorithms, Mellin transform, digital sums, recurrences, trees, random generation of combinatorial objects, random graphs and mappings, polynomial factorization, communications, codes, graphics, etc. This gives only a small snapshot of his work, and we encourage the reader to visit Flajolet's homepage http://www-rocq.inria.fr/algo/flajolet/index.html for a fuller account. The bibliography was taken from his homepage and may not be totally complete, although we added a few items. Our paper was written without giving any prior notice to Philippe Flajolet. It reflects the view of the authors and any misunderstandings and shortcomings should be put on their account.     

Sensor Fusion System Using Recurrent Fuzzy Inference

In robotic and manufacturing systems, it is difficult to measure the state of systems accurately because of many uncertain factors and noise, and it is very important to estimate the state of systems. We must measure the phenomena of systems by multiple sensors and estimate the state of systems by acquiring information of sensors. However, we can not acquire all of sensor information synchronically, because each sensor has particular sensor information and measuring time. For estimating the state of systems by multiple sensors, a multi-sensor fusion system fusing various sensory information is needed. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with recurrent inputs and apply it to a multi-sensor fusion system for estimating the state of systems. The membership functions of RFI are expressed by Radial Basis Function (RBF) with insensitive ranges. The shape of the membership functions can be adjusted by a learning algorithm. The learning algorithm is based on the steepest descent method and incremental learning which can add new fuzzy rules. The effectiveness of the multi-sensor fusion system using RFI will be shown through a numerical experiment of moving robot and estimation of surface roughness in grinding process.     

On Pattern Frequency Occurrences in a Markovian Sequence

Consider a given pattern H and a random text T generated by a Markovian source. We study the frequency of pattern occurrences in a random text when overlapping copies of the pattern are counted separately. We present exact and asymptotic formulae for moments (including the variance), and probability of r pattern occurrences for three different regions of r , namely: (i) r=O(1) , (ii) central limit regime, and (iii) large deviations regime. In order to derive these results, we first construct certain language expressions that characterize pattern occurrences which are later translated into generating functions. We then use analytical methods to extract asymptotic behaviors of the pattern frequency from the generating functions. These findings are of particular interest to molecular biology problems (e.g., finding patterns with unexpectedly high or low frequencies, and gene recognition), information theory (e.g., second-order properties of the relative frequency), and pattern matching algorithms (e.g., q -gram algorithms).     

造纸过程中定量检测系统的研究

介绍了以MCS－51系列单片机为核心的定量检测系统的基本原理。并对其硬件结构和软件设计进行了详细地介绍。     

基于神经网络的软测量技术及应用

软测量是一门新兴的工业技术，它借助现代估计理论构造模型推断出工程上难以检测的变量。本文提出了基于径向基函数神经网络（RBFNN）的软测量技术，并且结合工艺机理分析和过程数据关联，对其在轻柴油凝固点软测量的应用进行了研究。结果表明，RBFNN的良好的非线性动态建模能力使其在软测量中具有很大的应用潜力。