神经网络分类器的快速构造算法

使用ＢＰ算法训练多层网络的速度很慢而且事先难于确定隐节点和隐层的适当数目。本文提出一个有效的算法，先构造决策树，然后将构造的决策树转换为神经网。文中使用一个全局准则函数控制决策树的增长，它较好地匹配了树的复杂性和训练样本量及错分率界。实验结果，本文的算法比用ＢＰ算法训练多层网络要快，而其分类精度不低于用ＢＰ算法训练的多层神经网。     

模糊环境的表示及机器人轨迹规划

本文利用模糊集理论，提出一种非确定环境的描述方法，并且针对机器人在该类环境中的避障轨迹规划，建立了相应的决策函数和规划策略，最后，给出在ＰＣ－３８６计算机上的仿真试验结果。     

控制决策的信息传输

本文讨论了在一条通讯通道上执行基本控制决策的信息传输问题。结果表明当且仅当第一条通道可模拟另一条通道的操作时，则第一条通道在任何情况下比另一条通道都有优势。     

钢铁企业价格预测决策模型体系研究

通过对钢铁企业现行价格管理体系的分析 ,建立了钢铁企业价格预测决策模型体系。充分利用大量的定性、定量信息 ,并把趋势预测和因果预测技术相结合 ,采用组合预测模型对价格进行预测 ,最后对预测出来的多方案进行模糊综合评价 ,给出推荐方案顺序。实际应用表明 ,模型体系是有效的。     

A bidding decision index for construction contractors

Results from applying the model on a sample of contractors, the majority of whom were international and operating in Egypt, reinforces the credibility of the developed methodology, claim the authors.     

强化技改决策促进技术进步和企业发展

以有效的技改投资决策理念指导企业技改投资，加快了企业技术进步，促进了企业跨越式发展，使三钢的产能从1996年年产钢50万t钢规模发展到目前的200万t钢水平，且优化了产业结构，技术经济指标从全国同行业的中下游上升到领先水平，增强了企业实力。但是，在取得显著成绩的同时，周边民营钢铁企业异军突起，面对严峻的挑战，三钢力争两年内依靠技改使产能达到300万t钢规模，并筹谋更远大的发展。     

What's the use of being happy? Mood states, useful objects, and repetition priming effects.

Two experiments involving 99 undergraduate participants sought to examine the influence of mood states on encoding speed within lexical decision and pronunciation tasks. Mood states were measured naturalistically in Experiment 1 and manipulated in Experiment 2. Stimuli consisted of nouns representing useful (e.g., food) and nonuseful (e.g., lint) objects. Mood states had no implications for initial encoding speed. However, when the same words were presented a 2nd time (i.e., repeated), happy individuals displayed a tendency to encode useful words faster than nonuseful ones. Thus, mood states influenced repetition priming on the basis of stimulus valence. The authors propose that happiness sensitizes individuals to useful or rewarding objects, which in turn creates a stronger memory trace for such stimuli in the future. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Safe and effective navigation of autonomous robots in hazardous environments

The development of autonomous mobile machines to perform useful tasks in real work environments is currently being impeded by concerns over effectiveness, commercial viability and, above all, safety. This paper introduces a case study of a robotic excavator to explore a series of issues around system development, navigation in unstructured environments, autonomous decision making and changing the behaviour of autonomous machines to suit the prevailing demands of users. The adoption of the Real-Time Control Systems (RCS) architecture (Albus, 1991) is proposed as a universal framework for the development of intelligent systems. In addition it is explained how the use of Partially Observable Markov Decision Processes (POMDP) (Kaelbling et al., 1998) can form the basis of decision making in the face of uncertainty and how the technique can be effectively incorporated into the RCS architecture. Particular emphasis is placed on ensuring that the resulting behaviour is both task effective and adequately safe, and it is recognised that these two objectives may be in opposition and that the desired relative balance between them may change. The concept of an autonomous system having “values” is introduced through the use of utility theory. Limited simulation results of experiments are reported which demonstrate that these techniques can create intelligent systems capable of modifying their behaviour to exhibit either ‘safety conscious’ or ‘task achieving’ personalities.     

线性分组码的神经网络译码

介绍了神经网络与线性分组码之间的关系，并在文献[1]的基础上证明了软判决译码与求解能量函数最大值之间的等价性，然后以(7，4，3)汉明码为例介绍了神经网络在循环码硬判决、软判决译码中的应用。     

An intelligent decision theoretic approach to producibility optimization in conceptual design

Product optimization involves selecting design, manufacturing, and support attributes that can produce the best system. Producibility or manufacturability is the term often used to describe the relative ease of manufacturing a product. In complex systems, productibility optimization is a very difficult process, particularly when the values of many attributes are restricted by constraints. One challenge is to develop more universal producibility metrics for the conceptual design phase when design information is limited and drawings are nondimensional. This paper develops a new method for producibility optimization in conceptual design based on a combination of both decision theoretic and expert system techniques. Decision theoretic techniques provide the means to model the design for producibility problem in a manner that can deal with risk, uncertainty, and user (or corporate) preferences, and can effectively integrate diverse factors to provide a measure of the overall worth of a design. The particular decision theoretic approach employed is based on multi-attribute utility theory. An illustrative example of the methodology is applied to the conceptual design of a structural composite part.