PID控制在火箭炮伺服系统中的仿真实现——改进的专家自适应PID控制

针对传统PID（Proportional-Integral-Derivative）控制无法兼顾部分系统的静态性能和动态性能,结合专家PID控制原理,提出了一种改进的专家自适应PID控制器的设计方案,对某火箭炮伺服系统进行仿真跟踪。给出了伺服系统的分析设计过程,利用MATLAB/Simulink完成了改进的专家自适应PID控制器在某伺服系统中的仿真应用,得到了良好的跟踪特性图,说明了该方法的有效性。     

Business performance forecasting of convenience store based on enhanced fuzzy neural network

Reliable business performance forecasting of convenience store (CVS) can not only help in making the correct local selection decision but also in decreasing the store cost and thereby enlarging the profit significantly. Therefore, the main aim of this paper is to design an enhanced fuzzy neural network (EFNN)-based predictor to forecast the business performance of CVS. Without considering relevant domain knowledge, traditional fuzzy neural networks suffer from the problem of low accuracy of forecasting unseen examples. Moreover, traditional fuzzy neural networks have to turn weights with a kind of time-consuming gradient steepest descent training algorithm. Considering the relationship between the evaluation factors globally, we devise the EFNN which assigns connection weights based on the expert domain knowledge without painstakingly and repeatedly turning them. Furthermore, by generating and refining the activation function based on genetic algorithm, our EFNN can provide comprehensive and accurate activation functions and fit a wider range of nonlinear models. By experimenting our methods with several benchmark methods, the proposed methods are found to have an optimal accuracy in forecasting the business performance of CVS with a permanent good performance.     

Persuasiveness of expert systems

Expert system advice is not always evaluated by examining its contents. Users can be persuaded by expert system advice because they have certain beliefs about advice given by a computer. The experiment in this paper shows that subjects (n = 84) thought that, given the same argumentation, expert systems are more objective and rational than human advisers. Furthermore, subjects thought a problem was easier when advice on it was said to be given by an expert system while the advice was shown in production rule style. Such beliefs can influence expert system use.     

流量工程中多维目标优化的路由算法综述

互联网中传统的最短路径路由算法本质上存在导致拥塞的可能。网络拥塞不仅会降低网络性能,而且会使得服务提供商难以完成对客户的服务质量保证。通过在路由选择阶段面向流量工程实施优化,能够避免拥塞,提高网络资源利用率。概述面向流量工程的约束路由算法的研究进展。首先阐述了三类主要的流量工程优化目标：最小化网络资源占用、负载平衡和最小干涉,然后分析了各类典型算法,最后对此方向的研究进行了总结和展望。     

Online Portfolio Selection Strategy Based on Combining Experts’ Advice

The weak aggregating algorithm (WAA) developed from learning and prediction with expert advice makes decisions by considering all the experts’ advice, and each expert’s weight is updated according to his performance in previous periods. In this paper, we apply the WAA to the online portfolio selection problem. We first consider a simple case in which the expert advice is the strategy for investing in one stock; for this case, we obtain a portfolio selection strategy WAAS and prove that the WAAS can identify the best stock. We also discuss a more complicated case in which constant rebalanced portfolios are considered as expert advice, and obtain a corresponding portfolio selection strategy WAAC. The theoretical result shows that the cumulative gain that WAAC achieves is as large as that of the best constant rebalanced portfolio. Numerical analysis shows that the cumulative gains of our proposed strategies are as large as those of the best expert advice.     

A fast projection algorithm for sequence data searching

In real life, data often appear in the form of sequences and this form of data is called sequence data. In this paper, a new definition on sequence similarity and a novel algorithm, Projection Algorithm, for sequence data searching are proposed. This algorithm is not required to access every datum in a sequence database. However, it guarantees that no qualified subsequence is falsely rejected. Moreover, the projection algorithm can be extended to match subsequences with different scales. With careful selection of parameters, most of the similar subsequences with different scales can be retrieved. We also show by experiments that the proposed algorithm can outperform the traditional sequential searching algorithm up to 96 times in terms of speed up.     

ITS中基于纠错诊断的知识表示模型

智能教学系统是以专家系统为核心,帮助学生解决问题的教学系统。传统的辅助教学系统在测试学生学习情况时,只是简单的给学生一个分数,而没有给他们提高学习表现的机会。如果对测试结果进行分析,并提出相应的建议,将更有利于学生对各知识点的学习。文章提出了一个概念图模型,并将它应用在一个基于测试与诊断的ITS系统中,通过分析学科知识和测试结果来向学生提供学习建议,从而进一步提高学生成绩。最后,我们对该模型的性能给出了一个初步的结论。     

Reliance,trust and heuristics in judgmental forecasting

Judgmental forecasting gives light to the use of computers in human decision making. This paper reviews studies in judgmental forecasting focusing on what has been learned from human judgment and human–computer interaction. Available information was analyzed in the framework of three dimensions: reliance and trust on computer suggestions and heuristics employed by forecasters to produce forecasts. Results show that computer’s advice disuse is pervasive in forecasting; and the disuse increases with higher task complexity and lower perceived system performance. Explanations and past performance are good candidates to increase trust in computer’s advice, but the appropriate format to deliver this information is still controversial. Forecasters usually overforecast but report to prefer underforecast, which can lead to a cognitive dissonance and in turn to conflicting goals in the task. Heuristics research in time series forecasting indicates that forecasters heavily assess their own judgment, which in turn tend to be grounded in last outcomes and an overall evaluation of features like mean, trend and autocorrelation. It appears that heuristics not always lead to harmful biases for the forecast.     

基于遗传算法优化和BP神经网络的短期天然气负荷预测

天然气负荷预测的研究,对于保证天然气管网用气量、优化管网的调度和设备维修具有极其重要的意义.短期天然气负荷预测问题具有周期性和随机性的变化规律.传统方法无法进行准确的预测,预测精度较低.为了提高天然气负荷的预测精度,提出一种基于遗传算法优化和BP神经网络的天然气负荷预测方法.采用遗传算法对BP神经网络连接权值和阈值等模型参数进行优化,从而建立最优的天然气负荷预测模型,并采用某企业的天然气负荷数据对所建立预测模型的可行性和有效性进行验证.仿真结果表明,相对于传统BP预测算法,基于遗传算法优化参数的BP神经网络提高了天然气负荷预测精度,具有一定的实际工程应用价值.     

一种基于分裂-合并方法的中医舌像区域分割算法及其实现

舌像的区域分割是实现计算机中医舌诊自动化系统的一项前期工作，只有实现了良好的区域分割，后续工作的开展才能得以保证。为此提出了一种改进的分裂一合并算法对舌像进行区域分割，和其他几种分割算法的处理效果进行了分析比较，其结果表明，该算法在均匀一致性的判别条件，算法速度和处理效果等方面都显示出了其优势，传统方法的时间复杂度为O(n(n 1)／2)，而该算法的时间复杂度为O(n)，该算法在舌像分割方面具有普遍的适应性和实用性，实验结果令人满意。     

改进鲸鱼算法在云计算资源负载预测中的应用

为了解决传统云计算资源负载预测方法对负载序列高频分量预测精度不高和泛化能力弱的缺点,提出一种混合小波包变换和正余混沌双弦鲸鱼优化（CSCWOA）算法优化多层感知器神经网络（MLP）的短期云计算资源负载预测方法。通过小波包变换对负载序列进行多频段预处理分解,然后采用CSCWOA算法优化的MLP神经网络,对单支重构所得的负载子序列进行预测;最后叠加各子序列的预测值来获取实际预测结果。实验结果表明,该方法能掌握负载序列各频段冲击毛刺的变化规律,具有较好的预测精度和泛化能力。     

基于遗传算法的模糊聚类研究及其应用

为了克服传统聚类算法对初始化敏感的缺点，提出了一种基于增强型遗传算法的模糊聚类方法。它把遗传结束的准则与传统算法的终止准则有机地结合起来，不仅提高了算法的聚类分析性能，也提高了算法的收敛速度。比盲目的搜索效率要高，也比专门的针对特定问题的算法通用性强。通过在国内一家大型乳业集团的HRM系统中的成功运用，说明了该算法的有效性和通用性。     

优化混合蛙跳算法的WSN三维定位方法

根据传统混合蛙跳算法（Shuffled Frog Leaping Algorithm,SFLA）收敛速度较慢、局部最优的不足,提出了优化混合蛙跳算法（Optimized Shuffled Frog Leaping Algorithm,OSFLA）,并将其应用于无线传感器网络（WSN）节点三维定位。在三维定位中运用极大似然法进行粗略定位,对锚节点进行加权处理,设定搜索区域,再使用优化蛙跳算法进行迭代求精。仿真实验结果表明：优化混合蛙跳算法（OSFLA）比混合蛙跳算法（SFLA）具有更高的收敛速度和定位精度,同时更加适合于锚节点数较少场合。且在三维定位中与常用的几种算法相比OSFLA算法在定位精确度和稳定性方面都具有一定的提高。     

Identification environment and robust forecasting for nonlinear time series

In this paper, the methods of time series for nonlinearity are briefly surveyed, with particular attention paid to a new test design based on a neural network specification. The proposed integrated expert system contains two main components: an identification environment and a robust forecasting design. The identification environment can be viewed as a integrated dynamic design in which cognitive capabilities arise as a direct consequence of their self-organizational properties. The integrated framework used for discussing the similarities and differences in the nonlinear time series behavior is presented. Moreover, its performance in prediction proves to be superior than the former work. For the investigation of robust forecasting, we perform a simulation study to demonstrate the applicability and the forecasting performance.     

Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix

Forecasting the number of outpatient visits can help the expert of healthcare administration to make a strategic decision. If the number of outpatient visits could be forecast accurately, it would provide the administrators of healthcare with a basis to manage hospitals effectively, to make up a schedule for human resources and finances reasonably, and distribute hospital material resources suitably. This paper proposes a new fuzzy time series method, which is based on weighted-transitional matrix, also proposes two new forecasting methods: the Expectation Method and the Grade-Selection Method. From the verification and results, the proposed methods exhibit a relatively lower error rate in comparison to the listing methods, and could be more stable in facing the ever-changing future trends. The characteristics of the proposed methods could overcome the drawback of the insufficient handling of information to construct a forecasting rule in previous researches.     

A Modified Flower Pollination Algorithm for Global Optimization

Expert and intelligent systems try to simulate intelligent human experts in solving complex real-world problems. The domain of problems varies from engineering and industry to medicine and education. In most situations, the system is required to take decisions based on multiple inputs, but the search space is usually very huge so that it will be very hard to use the traditional algorithms to take a decision; at this point, the metaheuristic algorithms can be used as an alternative tool to find near-optimal solutions. Thus, inventing new metaheuristic techniques and enhancing the current algorithms is necessary. In this paper, we introduced an enhanced variant of the Flower Pollination Algorithm (FPA). We hybridized the standard FPA with the Clonal Selection Algorithm (CSA) and tested the new algorithm by applying it to 23 optimization benchmark problems. The proposed algorithm is compared with five famous optimization algorithms, namely, Simulated Annealing, Genetic Algorithm, Flower Pollination Algorithm, Bat Algorithm, and Firefly Algorithm. The results show that the proposed algorithm is able to find more accurate solutions than the standard FPA and the other four techniques. The superiority of the proposed algorithm nominates it for being a part of intelligent and expert systems.     

基于免疫进化算法的PID参数整定

借鉴生物免疫系统中的免疫网络调节机理，提出了一种新的自适应免疫进化算法，该算法用抗体激励水平作为选择压，并同时采用了种群数的自适应控制方法和基于免疫记忆机理的精英保留策略。将该算法应用于PID控制器参数的整定，结果表明这种算法能克服遗传算法的不足，有效保持种群的多样性，并且由此设计出的PID控制器性能优于基于遗传算法的PID控制器。     

混合实时事务的延期单调速率调度算法及其可调度性分析

对于含有实时和非实时两部分的混合实时应用，传统的单调速率调度算法(RM)已不再适用.为此，该文引入“混合实时事务”的概念，并针对这类事务提出一种延期单调速率调度算法(DRM)；着重分析了DRM算法对混合实时事务的可调度性；进行了实验测试与性能分析比较．结果表明，事务集中混合实时事务占的比例越高，混合事务中非实时子事务占的比例越大，该算法的CPU使用率阈值就越高，且在各种情况下，DRM算法与RM算法相比性能都更优，最低情况也与之一样．     

高级加密标准Rijndael算法的一种改进

随着计算机网络等技术的发展,传统的数据加密标准DES算法将被新的高级加密标准Rijndael算法所代替。本文在原Rijndael算法的密钥扩展算法上进行了改进,使得在牺牲少许密钥装填速度的前提下,提高了Rijndael算法的安全性。     

Extracting certainty from uncertainty: regret bounded by variation in costs

Prediction from expert advice is a fundamental problem in machine learning. A major pillar of the field is the existence of learning algorithms whose average loss approaches that of the best expert in hindsight (in other words, whose average regret approaches zero). Traditionally the regret of online algorithms was bounded in terms of the number of prediction rounds. Cesa-Bianchi, Mansour and Stoltz (Mach. Learn. 66(2–3):21–352, 2007) posed the question whether it is be possible to bound the regret of an online algorithm by the variation of the observed costs. In this paper we resolve this question, and prove such bounds in the fully adversarial setting, in two important online learning scenarios: prediction from expert advice, and online linear optimization.