Agent协同机制在基于MAS调度系统中的研究

在Agent协同机制,通用部分全局规划GPGP(Generalized Partial Global Planning)和任务环境描述语言TAEMS(Task Analysis, Environment Modeling and Simulation language)基础上,提出一种结合合同网协议、GPGP协同机制和TAEMS任务描述语言的任务分配和调度方法,用以克服基本合同网协议在基于MAS(Multi Agent System)的车间调度中应用的缺点和不足.并建立了任务工作环境的模型,为基于知识的调度提供基本框架.该方法和模型从整体的角度分析问题,提高了调度结果的一致性和优化性.初步的验证表明该方法是有效的.     

基于遗传算法的神经网络优化

针对神经网络结构的设计基本上依赖于人的经验的缺点，提出了一种利用遗传算法优化神经网络的算法。该算法结合了神经网络的快速并行性和遗传算法的全局搜索性，对神经网络的连接权和结构进行了优化，剔除整个网络冗余节点和冗余连接权，提高了网络的处理能力。通过实例证明该算法具有很高的精度，在机械故障诊断中具有良好的应用前景。     

基于改进人工势场算法的无人机群避障算法研究

针对无人机运动避障人工势场算法本身存在的极小值问题和局部最小值问题，采用改进的人工势场算法，提出了一种新的路径规划方法。不同于目前的人工势场法，该模型从双机相互作用开始，在障碍物斥力的基础上，增加了无人机之间的斥力，同时定义集群的前置形心作为另一个引力源。算法分析表明，该方法能够有效避免无人机陷入局部最小值，并增强了无人机机群的控制和避障能力。基于该无人机控制模型，给出了路径规划设计并进行了仿真实验。实验结果表明，基于该模型的无人机机群控制具有更好的避障性能和追踪目标的能力。     

非线性规划的人工释能及单变量曲径寻优算法

给出了求解一般不等式约束的非线性规划问题的一个常微分程的解法。其一维搜索的路径是约束央面上的一条最短线,其方程是由变分法建立的一组常微分方程的初值问题所确定的,。在初始点位于可行域内部时,采用人工释能法来求得下一个改进的可行点。数值例子表明核算法具有较了的计策效果。     

英语习得与人为目的语环境的创造

"语言习得"与"语言学习"是外语教学理论研究中的两个重要概念,克拉申认为只有"习得"来的语言才能成为流利的口头表达,而"学习"只能起"监查"作用。在"学习"的条件下,通过营造人为目的语习得环境,以用为本,立足实践,学习者同样可以达到习得外语的目标。     

Salt balance: From space experiments to revolutionizing new clinical concepts on earth – A historical review

For a long time, sodium balance appeared to be a “done deal” and was thought to be well understood. However, experiments in preparation of space missions showed that the concept of osmotic sodium storage and close correlations of sodium with water balance are only part of the regulatory mechanisms of body salt.     

人工智能在卫星任务规划中的应用

智能规划与调度是实现卫星自主规划其飞行任务的关键。将人工智能方法应用于卫星的任务规划,以执行对地观测任务为例,将任务规划问题归于组合优化问题,建立了相应的数学模型,并应用Hopfield神经网络算法进行求解,结果表明,该方法可用于单个仪器的任务规划。     

管制-飞行状态相依网络演化过程

动态、准确的管制系统运行态势预测是航空运输系统各相关单位开展协同决策的关键基础。基于航空器间的冲突情况、管制员对航空器的管控情况以及管制移交情况构建管制-飞行状态相依网络,探究、分析其演化规律,采用相关性分析和主成分分析证明了所选5项指标的合理性。设置自由飞行和固定航线飞行两种仿真场景,通过计算平均节点度、平均点强等拓扑指标的最大李雅普诺夫指数证明各时间序列均具有混沌特性,选择长短期记忆（LSTM）人工神经网络方法对各时间序列的演化规律进行预测,并与其他预测算法进行对比。仿真结果表明LSTM算法能对管制系统的演化过程进行有效的预测,且预测精度高于贝叶斯算法和支持向量机算法;在自由飞行条件下,5项指标的预测误差绝大部分在20%以内,固定航线飞行的预测效果优于自由飞行。     

航空器预测性维修技术研发应用态势分析

以航空器实时数据采集、传输和分析为基础的预测性维修技术已成为业界的发展趋势之一。本文从航空器维修的技术内涵和系统定位出发,以论文和专利数据为表征,提取预测性维修研究热点,系统梳理预测性维修技术的发展现状和趋势,分析人工智能、维修自动化、无人机、工业机器人、智能供应链等新一代信息技术在航空器预测性维修中的应用。在归纳预测性维修框架和关键技术的基础上,给出航空器预测性维修技术自主化策略建议。     

Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: A comparison of different transfer functions

Back propagation artificial natural network (BPANN) is a well known and widely used machine learning methodology in the field of remote sensing. In this paper an attempt is made to retrieve the spinach crop parameters like biomass, leaf area index, average plant height and soil moisture content by using the X-band scattering coefficients with BPANN at different growth stages of this crop. The maturity age of this crop was found to be 45 days from the date of sowing. After 45 days from the date of sowing, this crop was cut at a certain height for production. Then, it is a point of interest to investigate the microwave response of variation in production. Significant variations in all the crop parameters were observed after cutting the crop and consequently made the problem more critical. Our work confirms the utility of BPANN in handling such a non-linear data set. The BPANN is essentially a network of simple processing nodes arranged into different layers as input, hidden and the output. The input layer propagates components of a particular input vector after weighting these with synaptic weights to each node in the hidden layer. At each node, these weighted input vector components are added. Each hidden layer computes output corresponding to these weighted sum through a non-linear/linear function (e.g. LOGSIG, TANSIG and PURLIN). These functions are known as transfer functions. Thus, each of the hidden layer nodes compute output values, which become inputs to the nodes of the output layer. At nodes of output layer also a weighted sum of outputs of previous layer (hidden layer) are obtained and processed through a transfer function. Thus, the output layer nodes compute the network output for the particular input vector. In this paper, output nodes use linear transfer function. Different transfer functions e.g. TANSIG, LOGSIG and PURELIN were used and the performance of the ANN was optimized by changing the number of neurons in the hidden layers. The present analysis suggests the need of critical analysis of the BPANN in terms of selection of the best transfer function and other network parameters for the better results.