改进的RBFNN在运动员竞技状态预测中的应用

提出了一种改进的径向基函数(RBF)神经网络,该神经网络以模糊系统模型为基础。首先利用减法聚类算法确定径向基函数的中心数,然后通过模糊C均值聚类算法优化基函数中心与宽度,最后依据样本数据的聚类结果设计RBF神经网络并进行训练。将该神经网络应用于网球队运动员的竞技状态的预测。仿真结果表明:该算法先进有效、具有较高的精度,用其建立的模型具有较强的实用性。     

并联混合神经网络模型及应用研究

单一神经网络难以对复杂模型做出准确的预测,提出了一种并联型混合神经网络模型用于对复杂的系统进行预测,该模型由径向基函数网络、BP网络和控制模块组成。控制模块用于线性映射层,将两种单一神经网络的输出结合并得到最终的输出结果。详细地给出了混合模型的预测方法：首先,利用改进算法分别训练径向基函数网络和BP网络;其次,采用自适应遗传算法优化线性映射层以获得更好的预测精度;最后,利用两个实例比较单一神经网络和提出的混合网络的预测性能。实验表明,混合神经网络在预测精度上比单一网络具有更优的性能,同时,该混合模型为复杂系统提供了一种通用的预测工具。     

用RBF神经网络预测材料力学性能的研究

由于径向基函数(RBF)神经网络有易学,动态仿真性强,较强的输入输出映射功能和全局最优逼近的结构特点,因此将之用于预测麦杆增强复合板材力学性能。高斯函数表示形式简单,径向对称,光滑性好和解析性好,所以模型采用高斯函数作为隐含层基函数,k均值聚类法确定径向基函数的参数,运用最小二乘法确定权值。结合影响复合板材力学性能因素的特点和变化规律,以成型温度、成型压力、纤维含量、保温时间、拉伸强度、冲击韧性等为对象建立预测复合板材力学性能的模型,用它来优化模压成型的工艺参数,找出最佳工艺参数的范围。结果表明,径向基函数神经网络具有较好的学习和泛化能力,在预测力学性能中效果较好。     

BP神经网络在预测物流成本中的应用

影响物流成本的因素过多且较复杂,采用简单的猜测试赋值有较大的主观性,因此物流成本预测问题是一个非常复杂的非线性问题.本文根据物流成本与其影响因素之间映射关系,建立了BP神经网络模型,将其应用于物流成本的预测.实验结果表明,该模型具有较高的精度.     

自适应变系数粒子群—径向基神经网络模型在负荷预测中的应用

为了提高短期电力负荷预测精度,提出了一种自适应变系数粒子群-径向基函数神经网络混合优化算法(AVCPSO-RBF).实现了径向基神经网络参数优化.建立了基于该优化算法的短期负荷预测模型,利用贵州电网历史数据进行短期负荷预测.仿真表明,该方法的收敛速度和预测精度优于传统径向基神经网络方法和粒子群-RBF神经网络方法及基于混沌理论的神经网络模型,该优化算法克服了径向基神经网络和传统的粒子群优化方法的缺点,改善了径向基神经网络的泛化能力,提高了贵州电网短期负荷预测的精度,各日预测负荷的平均百分比误差可控制在1.7%以内.该算法可有效用于电力系统的短期负荷预测.     

基于径向基函数神经网络的机器人系统辨识

介绍了一种三层径向基函数神经网络,其学习算法采用正交最小二乘算法.首先根据正交最小二乘算法得到径向基函数神经网络的结构;然后对该网络的权值进行训练使它逼近给定的函数.为了验证径向基函数神经网络所具有的对任意非线性映射的任意逼近能力和自学习、自适应能力,以两关节机械手为辨识对象来进行实验研究.实验结果表明,该径向基函数神经网络具有良好的模型学习和逼近能力,并且学习速度快、收敛性好、鲁棒性强,尤其适合于具有连续线性与非线性对象的复杂系统的控制实时性要求.     

径向函数网络对国民经济生产总值预测研究

为了建立国民经济生产总值（GDP）神经网络预测模型,构造了双层网络结构的基于径向基网络,通过学习训练,确定径向基数神经网络参数和结构。仿真结果表明,生成的径向基函数模型应用于国民经济预测比BP神经网络模型具有更高的预测精度和良好的泛化能力。     

一种基于RBF神经网络的预测器模型及其研究

非线性复杂系统的预测控制是一种高性能的控制方法,其关键在于非线性预测器模型的实现。论文从径向基函数(RBF)神经网络原理分析出发,探讨了一种用于神经网络的预测模型设计方法,并将此方法用于实际非线性系统的预测控制。结果表明,基于RBF的神经网络预测模型可快速准确地完成对非线性动态过程的预测描述,因而可以在非线性系统的预测控制中得到良好的应用。     

一种复合算法在短期负荷预测中的应用

为提高电力系统短期负荷预测精度,文中提出一种基于改进遗传算法优化的径向基函数神经网络短期电力负荷预测模型,该模型采用改进的选择策略、自适应交叉和变异概率防止出现早熟现象;将自适应交叉和变异操作的改进遗传算法与梯度下降法混合交互运算,作为径向基函数神经网络的学习算法,将上述模型和算法应用于某地区电网的短期负荷预测,取得良好的预测效果.     

基于蚁群径向基函数网络的地下水预测模型

提出了一种基于蚁群算法的径向基函数神经网络,用它来进行地下水位预测,既具有神经网络广泛映射能力,又具有蚁群算法全局寻优、分布式计算等特点。实验表明,蚁群算法与径向基函数神经网络相融合能达到良好的预测效果。     

GPS/GIS/GSM实时物流配送辅助系统

提出了一种以GPS、GIS和GSM技术为核心的实时物流配送辅助系统设计方案，在此基础上研究了该系统的通信协议，该协议紧密结合物流行业的特点，在实践中已获得成功的应用。     

枢纽成本约束下的零担物流轴辐式网络设计

针对考虑枢纽建造成本和货物流的不确定的枢纽新建方案问题,引入全寿命周期理论,建立以轴辐式运营网络总成本最小化为目标的混合整数线性规划模型,并提出改进的最大最小后悔值的不确定性决策方法。通过算例来分析投资年限、枢纽干线折扣系数和不确定枢纽建造成本对零担物流（LTL）轴辐式网络的设计的影响。实验结果表明, 采用改进的不确定性决策方法得到的最优方案的运营成本比5个场景的运营成本平均降低了2.17%,表明基于改进的最大最小后悔值的不确定性决策方法,能够降低整个零担物流轴辐式运营网络总成本。     

基于SOPC的嵌入式WTB网络控制节点机的研制

探讨一种基于可编程片上系统的嵌入式绞线式列车总线(WTB)网络控制节点机的研制方案。该方案采用FPGA作为核心器件，将Altera 的NisoII软核处理器与WTB控制逻辑集成在单片FPGA 内，取代了原来的工控节点机和控制部件相组成的方案。减小了产品的体积和重量，降低了硬件开发成本。该文给出了系统的总体设计方案，并分析了测试结果。     

韶钢物流系统开发设计与应用

韶钢物流网络信息系统是自主开发的大型数据库应用系统,主要构架为C/S模式,后台采用大型数据库Oracle,前台用delphi开发。系统是以规范韶钢物流公司模式,提高物流运输效率,降低物流运输成本,运费结算管理设计与财务系统的成本核算相匹配为系统设计开发为目标。建立一套包括汽车物流、车场管理、运输成本及考核等高度信息化的管理系统,提升韶钢的物流运输管理水平,使其更好地适应韶钢的发展需要。简要谈谈"韶钢物流网络系统"在韶钢信息化建设中的开发与应用情况。     

Robust guaranteed cost control for uncertain stochastic systems with multiple decision makers

The guaranteed cost control (GCC) problem for uncertain stochastic systems with N decision makers is investigated. It is noteworthy that the necessary conditions, which are determined from Karush-Kuhn-Tucker (KKT) conditions, for the existence of a guaranteed cost controller have been derived on the basis of the solutions of cross-coupled stochastic algebraic Riccati equations (CSAREs). It is shown that if CSAREs have an optimal solution, then the closed-loop system is exponentially mean square stable (EMSS) and has a cost bound. In order to simplify computations and attain a global optimum, the linear matrix inequality (LMI) technique is also considered. Finally, a numerical example for a practical megawatt-frequency control problem shows that the proposed methods can help in attaining an adequate cost bound. Furthermore, the features of these methods are characterized.     

Activity-based costing model for joint products

Activity-Based Costing (ABC) is developed to improve the accuracy of product cost data derived from the traditional cost system. ABC transforms indirect (overhead) cost in the traditional cost system into direct costs by using activities as the intermediums of cost assignment. ABC traces indirect costs to the activities and then distributes the activity costs to the products. ABC has been extensively used in the practice and discussed in the literature. The purpose of this paper is to present the ABC model for joint products. A simplified illustrative case is used to demonstrate the product costing for joint products under ABC.     

A model and methodologies for the location problem with logistical components

This paper significantly extends traditional facility location models by introducing several logistical cost components such as holding, ordering, and transportation costs in a multi-commodity, multi-location framework. Since location and logistical costs are highly inter-related, the paper provides an integrated model, and seeks to minimize total physical distribution costs by simultaneously determining optimal locations, flows, shipment compositions, and shipment cycle times. Two sophisticated heuristic methodologies, based on Lagrangian relaxation and simulated annealing, respectively, are provided and compared in an extensive computational experiment.Scope and purposeLogistics has recently acquired great significance in industry, in part due to the rapidly growing interest in Supply Chain Management. One of the important open issues in logistics is the effective integration of logistical cost components such as transportation cost with facility location models, since the two are highly inter-related in practice. In particular, locations, flows, shipment compositions, and shipment cycle times are highly inter-dependent. The determination of optimal values of these variables is crucial for minimizing physical distribution costs. This paper proposes an integrated location–consolidation model and provides two sophisticated methodologies to solve the problem. The relative performance of the two methodologies is investigated in an extensive computational experiment.     

基于大数据的火灾智能预警系统

传统基于GSM蜂窝网的无线火灾预警系统由于成本高、感知终端电池耗电快等原因,一直很难得到大规模的应用.而LPWAN技术的快速发展,极大地降低了系统成本和感知终端的能耗,推动了火灾智能预警系统的快速发展.本文设计了一种基于大数据的无线火灾智能预警系统,通过该系统不但能够及时发现火灾,实时掌握火灾发生位置,还可以实现多个部门及个人的联动.实际使用结果表明,该系统不但通信成本低、电池寿命长,而且有效地降低了城市火灾发生率,提高了城市火灾救援效率和消防服务水平,并得到了广泛的推广和应用.     

Modified neo-fuzzy neuron-based approach for economic and environmental optimal power dispatch

At the central energy management center in a power system, the real time controls continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is minimized while all the operating constraints are satisfied. However, due to the strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained economic dispatch formulation is to estimate the optimal generation schedule of generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. Conventional optimization techniques become very time consuming and computationally extensive for such complex optimization tasks. These methods are hence not suitable for on-line use. Neural networks and fuzzy systems can be trained to generate accurate relations among variables in complex non-linear dynamical environment, as both are model-free estimators. The existing synergy between these two fields has been exploited in this paper for solving the economic and environmental dispatch problem on-line. A multi-output modified neo-fuzzy neuron (NFN), capable of real time training is proposed for economic and environmental power generation allocation.This model is found to achieve accurate results and the training is observed to be faster than other popular neural networks. The proposed method has been tested on medium-sized sample power systems with three and six generating units and found to be suitable for on-line combined environmental economic dispatch (CEED).