PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance

We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the generation of strings of symbols, that have been used in the context of speech and handwriting recognition, and bioinformatics. Recent work on learning PDFAs from random examples has used the KL-divergence as the error measure; here we use the variation distance. We build on recent work by Clark and Thollard, and show that the use of the variation distance allows simplifications to be made to the algorithms, and also a strengthening of the results; in particular that using the variation distance, we obtain polynomial sample size bounds that are independent of the expected length of strings.     

Multiagent learning using a variable learning rate

Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents. This creates a situation of learning a moving target. Previous learning algorithms have one of two shortcomings depending on their approach. They either converge to a policy that may not be optimal against the specific opponents' policies, or they may not converge at all. In this article we examine this learning problem in the framework of stochastic games. We look at a number of previous learning algorithms showing how they fail at one of the above criteria. We then contribute a new reinforcement learning technique using a variable learning rate to overcome these shortcomings. Specifically, we introduce the WoLF principle, “Win or Learn Fast”, for varying the learning rate. We examine this technique theoretically, proving convergence in self-play on a restricted class of iterated matrix games. We also present empirical results on a variety of more general stochastic games, in situations of self-play and otherwise, demonstrating the wide applicability of this method.     

基于约束优化的联想记忆模型学习算法

本文提出了一种对称互连神经元网络的学习策略,利用全局约束优化方法确定连接权。优化过程采用了梯度下降技术。这种学习算法可以保证训练样本成为系统的稳定吸引子,并且具有优化意义上的最大吸引域。本文讨论了网络的存储容量,训练样本的渐近稳定性和吸引域大小。计算机实验结果说明了学习算法的优越性。     

Hierarchical intelligent control with flexible AC transmission systems application

A novel hierarchical intelligent controller configuration is proposed using an artificial neural network as a control-mode classifier in the supervisory level and a set of pre-designed controllers in the lower level. Controller outputs are modified nonlinearly by the classifying signals in a structure resembling one artificial neuron with adaptively changed weights. The lower-level local controllers are implemented using neural networks. An illustrative example of this approach is based on the transient stabilization of a single-machine infinite-bus system studied in Flexible AC Transmission Systems (FACTS) research.     

Knowledge extraction for production management

A procedure and underlying algorithm for extracting knowledge from production and inventory databases to support engineering management activities is described. The process searches for, detects and isolates behaviour patterns inherent in the data. It relates these patterns to production irregularities, suggests connections with specific causes and helps propose possible corrective or preventive actions. The approach is based on a four-phase procedure: (1) the decision-maker focuses on the subject or difficulty at issue, represented by a target concept; (2) the KEDB algorithm, based on a machine learning approach, processes the relevant database and provides knowledge characterizing and classifying the target concept; (3) the output is interpreted in Pareto fashion as a series of possible circumstances explaining the target concept behaviour; and (4) based on these causes, the decision-maker decides on possible corrective actions to improve the situation, or preventive actions to forestall unfavourable conditions. A case study based on an actual quality control database is detailed.     

关于深冲薄钢板中的夹杂和成形性的商榷

对深冲薄钢板大生产的全工艺过程进行了跟踪，研究了ＷＹ０８ＡｌＡ薄钢板中Ａｌ＿２Ｏ＿３的形成，形态以及它们对冷轧成品的深冲性能和冲压小件的冲压成形废品率的影响。     

高强IF钢析出物的析出特征

高强ＩＦ钢是汽车用深冲钢板中深冲性能最好的高强度钢板。目前，国际上对该钢种的研究十分活跃。本文在实验条件下，通过对高强ＩＦ钢析出物的观察研究了高强ＩＦ钢析出物的析出特征，发现了新的析出相ＴｉＦｅＰ，并对磷对深冲性能影响的作用机制，进行了探讨。     

Synthesis and characterization of novel copolymers with the trimethylsilyl group for deep‐UV photoresists

N‐(4‐Acetoxyphenyl) maleimide (APMI) and three kinds of comonomers bearing a trimethylsilyl group were copolymerized at 60°C in the presence of azobisisobutyronitrile (AIBN) as an initiator in 1,4‐dioxane to obtain the three IP, IIP, and IIIP copolymers. These copolymers were removed from the acetoxy group in a transesterification process into new IVP, VP, and VIP copolymers with a pendant hydroxyl group. Two modified processes were adopted to prepare photoresists using these copolymers. The first process involved mixing the dissolution inhibitor, o‐nitrobenzyl cholate, with the new copolymers. Second, o‐nitrobenzyl cholate was introduced into the copolymers using 1,8‐diazabicyclo[5.4.0]undec‐7‐ene (DBU) in dimethylformamide (DMF). The cyclic maleimide structure is responsible for the high thermal stability of these copolymers. After irradiation using deep–UV light and development with aqueous Na₂CO₃ (0.01 wt %), the developed patterns showed positive images and exhibited good adhesion to the silicon wafer without using any adhesion promoter. The resolution of these resists was at least 0.8 μm and an oxygen‐plasma etching rate was 1/5.3 to that of hard‐baked HPR‐204. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 83: 2791–2798, 2002; DOI 10.1002/app.10255     

Distinguished Scientific Awards for an Early Career Contribution to Psychology.

The Early Career Awards recognize the large number of excellent young psychologists. Recipients of this award may not have held a PhD for more than nine years. The areas considered in 1991 were animal learning and behavior, developmental, and personality. The winners for 1991 were Kent C. Berridge, Susan A. Gelman, and Randy J. Larsen. For each winner a citation, biography, and selected bibliography of their works are presented. (PsycINFO Database Record (c) 2010 APA, all rights reserved)