基于免疫算法的Hopfield神经网络多用户检测器

提出的基于免疫算法的Hopfield神经网络多用户检测器，将扰乱的Hopfield神经网络多用户检测器的输出作为免疫算法的初始种群，利用了免疫算法的全局收敛的特点，从而克服了Hopfield易收敛到局部能量最小点的缺点。理论分析和仿真结果表明：该检测器具有良好的抗多址干扰和抗远近效应的能力。     

Technical Note: Selecting a Classification Method by Cross-Validation

If we lack relevant problem-specific knowledge, cross-validation methods may be used to select a classification method empirically. We examine this idea here to show in what senses cross-validation does and does not solve the selection problem. As illustrated empirically, cross-validation may lead to higher average performance than application of any single classification strategy, and it also cuts the risk of poor performance. On the other hand, cross-validation is no more or less a form of bias than simpler strategies, and applying it appropriately ultimately depends in the same way on prior knowledge. In fact, cross-validation may be seen as a way of applying partial information about the applicability of alternative classification strategies.     

Perceptually Grounded Language Learning: Part 2—DETE: A Neural/Procedural Model

In Part 1 of this two-part series, we introduced Katamic memory—a neural network architecture capable of robust sequence learning and recognition. In Part 2, we introduce the Blobs World taskjdomain for language learning and describe the DETE language learning system, which is composed of over 50 Katamic memory modules. DETE currently learns small subsets of English and Spanish via association with perceptual! motor inputs. In addition to Kaiamic memory, DETE employs several other novel features: (1) use of feature planes, to encode visual shapes, spatial relationships and the motions of objects, (2) phase-locking of neural firing, in order to represent focus of atention and to bind objects across multiple feature planes, and (3) a method for encoding temporal relationships, so that DETE can learn utterances involving the immediate past and future. We compare DETE to related models and discuss the implications of this approach for language-learning research.     

基于神经网络的智能检索系统

文章首先概述了当前大多数的搜索引擎的主要的弊端；然后着重介绍了智能检索系统的运行基本思路和售用算法，以及该算法在检索领域中运用的优点；最后指出，神经网络运用在检索领域，可解决网络管理中的诸多难题。     

Artificial Neural Network Modeling of Osmotic Dehydration Mass Transfer Kinetics of Fruits

Artificial neural network (ANN) models were developed for the prediction of transient moisture loss (ML) and solid gain (SG) in osmotic dehydration of fruits using process kinetics data from the literature. ANN models for ML and SG were developed based on data over a broad range of operating conditions and ten common processing variables: temperature and concentration of osmotic solution, immersion time, initial water and solid content of the fruit, porosity, surface area, characteristic length, solution-to-fruit mass ratio, and agitation level. The trained models were able to accurately predict the outputs with associated regression coefficients (r) of 0.96 and 0.93, respectively, for ML and SG. These ANN models performed much better than those obtained from linear multivariate regression analysis. The large number of process variables and their wide ranges considered along with their easy implementation in a spreadsheet make them very useful and practical for process design and control.     

基于GRNN网络模型的土壤重金属空间分布的研究

以江苏省南通市为研究区,利用采样点实测数据,借助GRNN神经网络模型并结合3S技术对农田土壤重金属的空间动态分布进行了深入研究。结果表明,GRNN神经网络模型能够智能地学习各个采样点的空间位置与该点各重金属含量之间的映射关系,并能够稳健地对各个空间插值点处的土壤重金属含量进行预测;结果显示南通市农田土壤重金属污染总体较轻,但也存在局部地区的严重污染。在运用GRNN神经网络模型进行空间插值了解重金属空间动态分布的基础上,可以根据污染的状况确定农产品的生产布局和规划。     

Marcia K. Johnson: 2006 Award for Distinguished Scientific Contributions.

Presents the citation to Marcia K. Johnson, who received the Award for Distinguished Scientific Contributions "for raising and illuminating fundamental questions about the cognitive and neural processes that constitute the subjective experience of mental life." A brief profile and a selected bibliography, as well as Johnson's award address, entitled Memory and Reality (see record 2006-21079-028), accompany the citation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

正则模糊神经网络对一类连续模糊值函数的普遍近似性

本文讨论了正则模糊神经网络于对定义于区间［０，Ｔ０］上连续可减模糊值函数的普遍近似性，在此基础上，证明了［０，Ｔ０］上取值为三角形模糊连续递增函数可用正则模糊神经网络逼近到任意精度。     

人工神经网络A／D变换器

本文对［５］的４位人工神经网络Ａ／Ｄ变换器进行了研究．指出按其方法推广实现多位Ａ／Ｄ时存在突触电阻大小以至无法实现且抗干扰能力也不好的问题并给出了解决办法．同时对如何改善神经元转移特性的硬限幅曲线作了简单讨论．     

Nonlinear black-box modeling in system identification: a unified overview

A nonlinear black-box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area, with structures based on neural networks, radial basis networks, wavelet networks and hinging hyperplanes, as well as wavelet-transform-based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques. It is pointed out that the nonlinear structures can be seen as a concatenation of a mapping form observed data to a regression vector and a nonlinear mapping from the regressor space to the output space. These mappings are discussed separately. The latter mapping is usually formed as a basis function expansion. The basis functions are typically formed from one simple scalar function, which is modified in terms of scale and location. The expansion from the scalar argument to the regressor space is achieved by a radial- or a ridge-type approach. Basic techniques for estimating the parameters in the structures are criterion minimization, as well as two-step procedures, where first the relevant basis functions are determined, using data, and then a linear least-squares step to determine the coordinates of the function approximation. A particular problem is to deal with the large number of potentially necessary parameters. This is handled by making the number of ‘used’ parameters considerably less than the number of ‘offered’ parameters, by regularization, shrinking, pruning or regressor selection.