Predicting the impact of vegetations in open channels with different distributaries’ operations on water surface profile using artificial neural networks

Most of the open water irrigation channels in Egypt suffer from the infestation of aquatic weeds, especially the submerged ones that cause numerous hydraulic problems for the open channels themselves and their water distributaries such as increasing water losses, obstructing water flow, and reducing channels’ water distribution efficiencies. Accurate simulation and prediction of flow behavior in such channels is very essential for water distribution decision makers. Artificial neural networks (ANN) have proven to be very successful in the simulation of several physical phenomena, in general, and in the water research field in particular. Therefore, the current study aims towards introducing the utilization of ANN in simulating the impact of vegetation in main open channel, which supplies water to different distributaries, on the water surface profile in this main channel. Specifically, the study, presented in the current paper utilizes ANN technique for the development of various models to simulate the impact of different submerged weeds’densities, different flow discharges, and different distributaries operation scheduling on the water surface profile in an experimental main open channel that supplies water to different distributaries. In the investigated experiment, the submerged weeds were simulated as branched flexible elements. The investigated experiment was considered as an example for implementing the same methodology and technique in a real open channel system. The results showed that the ANN technique is very successful in simulating the flow behavior of the pre-mentioned open channel experiment with the existence of the submerged weeds. In addition, the developed ANN models were capable of predicting the open channel flow behavior in all the submerged weeds’cases that were considered in the ANN development process     

Application of a genetic algorithm in predicting the percentage of shear force carried by walls in smooth rectangular channels

Shear stress comprises basic information for predicting average depth velocity and discharge in channels. With knowledge of the percentage of shear force carried by walls (%SF_w) it is possible to more accurately estimate shear stress values. The %SF_w in smooth rectangular channels was predicted by extending two soft computing methods: Genetic Algorithm Artificial (GAA) neural network and Genetic Programming (GP). In order to investigate the percentage of shear force, 8 data series with a total of 69 different data were used. The outcomes of the GAA model (an equation) and the GP model (a program) were presented. In order to detect these models’ ability to predict %SF_w, the obtained results were compared with several equations derived by other researchers. The GAA model with RMSE of 2.5454 and the GP model with RMSE of 3.0559 performed better than other equations with mean RMSE of about 9.630.     

模糊控制智能开发系统

本文介绍了模糊控制智能化开发系统,包括：模糊控制原理,基于计算智能（人工神经网,遗传算法）的模糊规则自动生成的方法,系统的功能和界面等。     

A neural network approach for prediction of discharge in straight compound open channel flow

Most natural rivers and streams consist of two stage channels known as main channel and flood plains. Accurate prediction of discharge in compound open channels is extremely important from river engineering point of view. It helps the practitioners to provide essential information regarding flood mitigation, construction of hydraulic structures and prediction of sediment load so as to plan for effective preventive measures. Discharge determination models such as the single channel method (SCM), the divided channel method (DCM), the coherence method (COHM) and the exchange discharge method (EDM) are widely used; however, they are insufficient to predict discharge accurately. Therefore, an attempt has been made in this work to predict the total discharge in compound channels with an artificial neural network (ANN) and compare with the above models. The mean absolute percentage error with artificial neural networks is found to be consistently low as compared to other models.     

进化小波网络及其在设备状态预测中的应用

结合小波网络和进化计算提出了进化小波网络策略,该策略采用控制级基因和参数级基因分别对网络结构和网络参数进行编码,并将遗传算法与进化规划结合进行进化操作,实现同时对网络结构与网络参数进行进化设计和学习训练。该策略不仅克服了网络训练中的局部极小和不收敛问题,也使网络结构更优,从而提高了网络训练和工作性能。最后分别就函数逼近问题、太阳黑子数预测问题及水轮机组的状态预测问题进行了事例研究,验证了所提出的进化小波网络策略的优越性能和可行性。     

型材挤压过程工艺参数优化模型

提出了一种集数值仿真、人工神经网络和遗传算法为一体的工艺参数优化模型,用于型材挤压成形过程工艺参数优化,合理配置了非对称角铝型材模模孔位置。通过现场试验,验证了提出的工艺参数优化模型是行之有效和正确的。并在模孔位置优化配置结果基础上对其挤压成形过程进行数值仿真,分析了挤压成形过程中各阶段网格畸变情况,给出了挤压变形时应力和应变分布,对指导型材挤压工艺和模具优化设计具有重要意义。     

考虑弹流润滑的齿轮传动多目标优化设计的遗传算法实现

应用弹性流体动力润滑理论,分析了模数、齿数等参数对齿轮副润滑性能的影响。在齿轮传动优化设计中,通过协调弹流效应和重量最轻建立了齿轮传动多目标优化的数学模型。采用遗传算法求解合连续及离散变量的优化设计问题,并用神经网络建立计算中各种曲线的插值模型。该系统发挥了神经网络与遗传算法各自的特点,数值例子表明了该方法的有效性。     

基于神经网络和遗传算法的薄壳件注塑成型工艺参数优化

建立基于神经网络和遗传算法并结合正交试验的薄壳件注塑成型工艺参数优化系统.正交试验法用来设计神经网络的训练样本,人工神经网络有效创建翘曲预测模型;遗传算法完成对影响薄壳塑件翘曲变形的工艺参数(模具温度、注射温度、注射压力、保压时间、保压压力和冷却时间等)的优化,并计算出其优化值.按该参数进行试验,效果良好,可以有效地减小薄壳塑件翘曲变形,其试验数值与计算数值基本相符,说明所提出的方法是可行的.     

基于模糊神经网络和遗传算法的仿人智能PID控制器设计

阐述一种新型的模糊神经网络加遗传算法的智能PID控制器     

End depth computation in inverted semicircular channels using ANNs

The paper presents the application of artificial neural network (ANN) to determine the end-depth-ratio (EDR) for a smooth inverted semicircular channel in all flow regimes (subcritical and supercritical). The experimental data were used to train and validate the network. In subcritical flow, the end depth is related to the critical depth, and the value of EDR is found to be 0.705 for a critical depth–diameter ratio up to 0.40, which agrees closely with the value of 0.695 given by Dey [Flow Meas. Instrum. 12 (4) (2001) 253]. On the other hand, in supercritical flow, the empirical relationships for EDR and non-dimensional discharge with the non-dimensional streamwise slope of the channel are established.     

Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm

In this study, optimum cutting parameters of Inconel 718 are determined to enable minimum surface roughness under the constraints of roughness and material removal rate. In doing this, advantages of statistical experimental design technique, experimental measurements, artificial neural network and genetic optimization method are exploited in an integrated manner. Cutting experiments are designed based on statistical three-level full factorial experimental design technique. A predictive model for surface roughness is created using a feed forward artificial neural network exploiting experimental data. Neural network model and analytical definition of material removal rate are employed in the construction of optimization problem. The optimization problem was solved by an effective genetic algorithm for variety of constraint limits. Additional experiments have been conducted to compare optimum values and their corresponding roughness and material removal rate values predicted from the genetic algorithm. Generally a good correlation is observed between the predicted optimum and the experimental measurements. The neural network model coupled with genetic algorithm can be effectively utilized to find the best or optimum cutting parameter values for a specific cutting condition in end milling Inconel 718.     

The free vibration analysis and optimal design of an adhesively bonded functionally graded single lap joint

In this study, three-dimensional free vibration and stress analyses of an adhesively bonded functionally graded single lap joint were carried. The effects of the adhesive material properties, such as modulus of elasticity, Poisson's ratio and density were found to be negligible on the first ten natural frequencies and mode shapes of the adhesive joint. Both the finite element method and the back-propagation artificial neural network (ANN) method were used to investigate the effects of the geometrical parameters, such as overlap length, plate thickness and adhesive thickness; and the material composition variation through the plate thickness on the natural frequencies, mode shapes and modal strain energy of the adhesive joint. The suitable ANN models were trained successfully using a series of free vibration and stress analyses for various random geometrical parameters and compositional gradient exponents. The ANN models showed that the support length, the plate thickness and the compositional gradient exponent played important role on the natural frequencies, mode shapes and modal strain energies of the adhesive joint whereas the adhesive thickness had a minor effect. In addition, the optimal joint dimensions and compositional gradient exponent were determined using genetic algorithm and ANN models so that the maximum natural frequency and the minimum modal strain energy conditions are satisfied for each natural frequency of the adhesively bonded functionally graded single lap joint.     

基于人工神经网络的机械设计领域知识表达方法的研究

结合人工神经网络技术开展了机械设计领域经验型知识表达方法的研究，提出了一种基于多层神经网络的知识表达方法，该方法适合于数值型经验知识的表达，并对多层神经网络学习的ＢＰ算法进行了改进。     

一种遗传-模糊神经网络图像滤波器

提出了一种新的模糊神经网络与遗传算法相结合的非线性图像滤波器 ,通过有效的编码方案 ,遗传算法可以在很短的进化代数内完成对网络参数的学习。包含于网络中的模糊推理有效地恢复了受污染的像素点 ,同时并不降低原始图像的品质。仿真实验表明 ,该滤波器对冲击噪声有很好的滤除效果 ,明显地优于传统的图像滤波器     

航空发动机振动趋势预测的过程神经网络法

提出一种基于过程神经网络思想的航空发动机振动趋势预测方法.利用过程神经网络具有输出函数对输入函数在时间上的聚合效应和非线性映射能力,预测方法的网络结构选择为9个输入节点,第2层和第3层各有9个隐层节点,1个输出节点,参数外推预测,将选取的振动历史数据分为学习样本和检测样本两组,学习样本用于网络训练,检测样本用于检验预测模型的精度.在相同条件下,与传统人工神经网络进行趋势预测比较,提高了网络训练速度,降低了预测误差.将所提出的预测方法应用到某型航空发动机的振动趋势预测中,预测结果与实际值的误差符合要求.     

基于GA-BP神经网络薄煤层采煤机装煤性能预测

提高装煤量是薄煤层采煤机设计中的重要内容。针对传统方法难以解决多因素影响采煤机装煤量的问题,提出了基于遗传算法(GA)与BP神经网络相结合的薄煤层采煤机装煤率预测方法。建立了薄煤层采煤机装煤量的数学模型,利用遗传算法对神经网络的权值和阀值进行优化,以仿真数据为训练和检测样本,用GA-BP算法训练网络,避免了单独使用BP神经网络训练容易陷入局部极小值以及单独利用仿真的办法工作量大,仿真时间长的问题。结果表明:利用提出的方法即加快了收敛速度也提高了训练精度,对薄煤层采煤机装煤性能的预测具有重要意义。