Efficacy of utilizing a hybrid algorithmic method in enhancing the functionality of multi-instance multi-label radial basis function neural networks

The facts show that multi-instance multi-label (MIML) learning plays a pivotal role in Artificial Intelligence studies. Evidently, the MIML learning introduces a framework in which data is described by a bag of instances associated with a set of labels. In this framework, the modeling of the connection is the challenging problem for MIML. The RBF neural network can explain the complex relations between the instances and labels in the MIMLRBF. The parameters estimation of the RBF network is a difficult task. In this paper, the computational convergence and the modeling accuracy of the RBF network has been improved. The present study aimed to investigate the impact of a novel hybrid algorithm consisting of Gases Brownian Motion optimization (GBMO) algorithm and the gradient based fast converging parameter estimation method on multi-instance multi-label learning. In the current study, a hybrid algorithm was developed to estimate the RBF neural network parameters (the weights, widths and centers of the hidden units) simultaneously. The algorithm uses the robustness of the GBMO to search the parameter space and the efficiency of the gradient. For this purpose, two real-world MIML tasks and a Corel dataset were utilized within a two-step experimental design. In the first step, the GBMO algorithm was used to determine the widths and centers of the network nodes. In the second step, for each molecule with fixed inputs and number of hidden nodes, the parameters were optimized by a structured nonlinear parameter optimization method (SNPOM). The findings demonstrated the superior performance of the hybrid algorithmic method. Additionally, the results for training and testing the dataset revealed that the hybrid method enhances RBF network learning more efficiently in comparison with other conventional RBF approaches. The results obtain better modeling accuracy than some other algorithms.     

Economical treatment of reverse osmosis reject of textile industry effluent by electrodialysis–evaporation integrated process

Membrane separation methods such as electrodialysis (ED) can reduce the volume load on evaporators by facilitating further concentration of rejects from reverse osmosis (RO) plants. ED studies were carried out on a bench-scale system using five membrane cell pairs to obtain a textile effluent concentrate containing approximately 6 times the quantity of salts present in the RO reject. The limiting current densities were determined to be in the range 2.15–3.35 amp/m² for feed flow rates varying from 18 to 108 L/h. Apart from feed rate, the influence of volume of concentrate and current on membrane performance was evaluated to optimize current utilization. An estimation of energy requirement of an integrated process constituting ED and evaporation for concentration of inorganics present in textile effluent from 4.35% to 24% was made and found to be approximately one eighth of the operating cost incurred by evaporation alone. Detailed design of a commercial ED system revealed that a membrane area of 13.1 m² was required to treat a feed rate of 1500 L/h. The payback period to recover capital investment was found to be 110 days.     

冷却塔蒸发损失水量计算方法探讨

冷却塔蒸发损失水量计算目前多采用估算法,也有采用理论计算法的,且不同的设计规范提出的要求不尽相同,因此在工程项目设计计算蒸发损失水量时,对该问题的认识一直比较混乱。针对这种现状,通过对南方湿热地区和北方干热地区2个典型地区采用不同计算方法的比较分析,提出工程项目在可行性研究阶段可采用估算法计算冷却塔蒸发损失水量,而在总体设计及以后阶段采用理论计算法。这种分阶段采用不同方法计算冷却塔蒸发损失水量的方法,不仅满足了工程项目的设计要求,同时,还减少了项目投资,降低了水资源使用量。     

工程机械用液压马达剩余寿命预测

为了进行工程机械用液压马达剩余寿命预测,基于容积效率、Wiener过程以及支持向量机回归,提出一种剩余寿命预测方法。开展液压马达全寿命试验,运用非线性支持向量机回归算法处理原始数据,基于极大似然估计法和贝叶斯更新策略建立Wiener过程模型,完成液压马达剩余寿命预测。结果表明：液压马达容积效率随冲击次数变化有明显下降趋势;非线性支持向量机回归在考虑退化数据随机性的基础上提高了规律性;基于Wiener过程的剩余寿命预测方法能够准确、可靠地预测液压马达的剩余服役寿命。     

废弃纤维再生混凝土受压徐变及预测模型

为研究再生粗骨料替代率、废弃纤维体积掺入量对废弃纤维再生混凝土受压徐变破坏时间、徐变变形和徐变度的影响规律,对普通混凝土、再生混凝土和废弃纤维再生混凝土试件在实验室条件下进行了85%、90%和95%应力水平的徐变试验。试验结果表明：随着再生粗骨料替代率的增加,徐变破坏的时间缩短、徐变变形及徐变度增大;随着废弃纤维体积掺入量的增加,徐变变形及徐变度减小,徐变破坏时间增加。废弃纤维的加入能有效缓解再生混凝土受压徐变的破坏程度。在考虑再生粗骨料替代率及废弃纤维体积掺入量的基础上,对ACI209R徐变预测模型进行修正,模型预测结果与试验值吻合较好。     

基于灰关联的PSO-BP神经网络的高层住宅造价估算

针对高层住宅工程造价管理的难点及传统造价估算方法存在的不足,采用灰关联分析与粒子群优化的 BP 神经网络相结合的方法,以高层住宅工程特征指标为网络的输入向量,达到快速、准确地估算高层住宅工程造价的目标。借助文献回顾法与灰关联分析法系统地确定工程特征指标体系并作为神经网络的输入向量;引入 PSO 算法优化 BP 网络的权值及阈值,解决网络收敛速度慢、易陷入局部极小值等缺点。并通过实例验证构建的模型,提高了前期决策阶段造价估算的精确度,实现了快速估算     

铸件多功能作业机器人多臂协调轨迹规划与仿真

以铸件多功能作业机器人的工作臂为研究对象,采用笛卡尔空间变量法对机器人铸件搬运过程进行轨迹规划。结合多臂协调的运动约束条件以及伪逆法,对机器人的4个工作臂关节运动进行优化,得到多机械臂协调运动的关节位置和速度变化曲线。由MATLAB软件仿真得到的运动参数曲线可知,算法优化后得到的运动轨迹均能保持连续、平滑,有利于实现对机器人的精准控制。利用机械臂正运动学方程得到了机械臂末端执行器的轨迹曲线,曲线满足铸件搬运的要求,验证了算法的正确性。     

基于Root MUSIC法的轧辊偏心参数估计

针对轧辊偏心信号是混杂在各种随机干扰中的复杂高频周期信号,因FFT法对信号分析的局限性,提出了一种Root MUSIC(Multiple Signal Classification)法和Prony法相结合的轧辊偏心信号估计新方法。利用Root MUSIC法准确估计出偏心谐波的频率及谐波的个数,同时使用Prony方法估计出偏心信号的各次谐波幅值和相位。仿真结果验证了可行性和有效性,在信噪比较低的情况下仍能准确地同时估计出偏心谐波的频率、幅值及相位,尤其在频率分辨率和抗噪声上比FFT法具有优越性     

神经网络在工程造价和主要工程量快速估算中的应用研究

采用动量法和学习自适应调整策略改正的BP神经网络建立了工程造价和主要工程量估算数学模型。针对以往估算模型精度不高的原因,模型中增加了工程造价指数和工程硬、软件环境对工程造价的影响。采用m atlab6.1计算软件,以深圳市已完典型工程资料为例,验证了该模型的正确性及实用性。     

一种估计结构面迹长的新方法及其工程应用

为了研究结构面迹长与其产状之间的概率关系。根据结构面交切测量窗口的几何条件与交切频度之间的概率关系，拓展了H-H迹长估计方法。建立了结构面平均迹长与其在测量窗口中视倾角的关系表达式。在此基础上,结合Laslett迹长估计方法，提出了广义H-H迹长估计方法，进一步完善了H-H方法，并通过工程实例证明了其正确性。该方法使用简单方便，有很高的推广价值。