一种汉语方言编码与转换机制的研究

回顾了汉语拼音方案的组成和特点,分析了方言编码的可行性。引入二进制编码方式对汉字发音进行编码。编码序列包括了声母、韵母、音调、儿化音等编码部分。建立了方言音、修饰音、标准音之间的转换关系。以湘汨地区的方言为例,详细论述了方言编码和转换的过程。以人工代谢算法作为寻优策略,结合特定语言环境,将方言音转换成标准音。通过实例分析论述了该方案的可行性和合理性。     

GAFSA优化SVR的网络流量预测模型研究

现有的诸多网络流量预测模型存在预测稳定性不好、精度较低等问题。针对此类问题, 研究了一种通过GAFSA(全局人工鱼群算法)优化SVR模型的网络流量预测方法。GAFSA是一种群智能优化算法, 寻优效果显著。采用GAFSA对SVR预测模型进行参数寻优, 可以得到使预测效果最佳的训练参数; 使用这组最优参数训练SVR, 建立网络流量预测模型, 可以很好地改善基于其他智能优化算法改进的SVR网络流量预测模型多次预测结果相差较大的问题, 使预测结果趋于稳定, 同时也可以提高预测精准度。仿真结果表明, GAFSA-SVR网络流量预测模型与其他模型相比, 预测结果基本稳定, 精准度提高到89%以上, 对于指导网络控制行为、分析网络安全态势有重要意义。     

一种基于RAN架构无线接入网系统容量最大化的功率分配算法

针对新型RAN架构无线接入网中功率分配问题进行研究, 建立系统容量最大化的最优化数学模型, 提出了一种基于RAN架构无线接入网的系统容量最大化的功率分配算法, 并且应用人工鱼群算法求解了算法中目标函数的全局最优解, 即一组使系统容量最大化的发射功率。仿真结果表明, 与现有的分布式网络架构中非合作的功率控制博弈算法相比, 新算法可以显著提高系统容量, 即证明了该算法的有效性。     

浅谈机器学习方法简

本文以什么是机器学习、机器学习的发展历史和机器学习的主要策略这一线索,对机器学习进行系统性的描述。接着,着重介绍了流形学习、李群机器学习和核机器学习三种新型的机器学习方法,为更好的研究机器学习提供了新的思路。     

人工神经网络预测阴离子表面活性剂的临界胶束浓度

根据定量结构-性能关系原理,利用人工神经网络模型,采用Wn、S和Sn三种拓扑指数描述阴离子表面活性剂的分子结构并作为网络输入,预测阴离子表面活性剂的临界胶束浓度。确定了网络参数,利用43组数据对网络进行训练和预测,并与文献值进行了比较。结果表明,预测精度较高,说明人工神经网络方法具有很好的预测能力。     

基于新型免疫粒子群优化的热连轧负荷分配策略*

针对带钢热连轧精轧中的负荷分配问题,提出了一种新型的优化策略。首先提出了一种新型的免疫粒子群混合优化算法,通过克隆选择算子来调节群体的浓度,实现混合算法的个体高亲和力和群体的多样性; 然后通过提出的混合算法对负荷分配进行优化,得到优化的压下量数据,通过这些数据建立了计算负荷分配输出的人工神经网络。实验表明,提出的混合算法和负荷分配优化策略给出了很好的优化效果,能有效地指导实际生产应用。     

Fault-Diagnosis Method Based on Support Vector Machine and Artificial Immune for Batch Process

A new fault-diagnosis method to be used in batch processes based on multi-phase regression is presented to overcome the difficulty arising in the processes due to non-uniform sample data in each phase. Support vector machine is first used for phase identification, and for each phase, improved artificial immune network is developed to analyze and recognize fault patterns. A new cell elimination role is proposed to enhance the incremental clustering capability of the immune network. The proposed method has been applied to glutamic acid fermentation, comparison results have indicated that the proposed approach can better classify fault samples and yield higher diagnosis precision.     

基于移动机器人定位和导航的人工路标设计

在复杂环境下,如何快速、准确地识别路标是移动机器人定位和导航的关键问题.基于图像分割原理,利用投影和方向特征的处理方法,提出了一种新的人工路标方案.通过统计学的相关理论验证了该路标图案的可行性,实验结果也显示了该图案可以较好地解决移动机器人的路标识别问题.     

Application of artificial neural networks to the prediction of tunnel boring machine penetration rate

Rate of penetration of a Tunnel Boring Machine(TBM)in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project.This paper presents the results of a study into the application of an Artificial Neural Network(ANN)technique for modeling the penetration rate of tunnel boring machines.A database,including actual,measured TBM penetration rates,uniaxial compressive strengths of the rock,the distance between planes of weakness in the rock mass and rock quality designation was established.Data collected from three different TBM projects(the Queens Water Tunnel,USA,the Karaj-Tehran water transfer tunnel,Iran,and the Gilgel Gibe Ⅱ hydroelectric project,Ethiopia).A five-layer ANN was found to be optimum,with an architecture of three neurons in the input layer,9,7 and 3 neurons in the first,second and third hidden layers,respectively,and one neuron in the output layer.The correlation coefficient determined for penetration rate predicted by the ANN was 0.94.     

A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.