Estimation of Weibull Distribution Parameters for Wind Speed Characteristics Using Neural Network Algorithm

Harvesting the power coming from the wind provides a green andenvironmentally friendly approach to producing electricity. To facilitate theongoing advancement in wind energy applications, deep knowledge aboutwind regime behavior is essential. Wind speed is typically characterized bya statistical distribution, and the two-parameters Weibull distribution hasshown its ability to represent wind speeds worldwide. Estimation of Weibullparameters, namely scale (c) and shape (k) parameters, is vital to describethe observed wind speeds data accurately. Yet, it is still a challenging task.Several numerical estimation approaches have been used by researchers toobtain c and k. However, utilizing such methods to characterize wind speedsmay lead to unsatisfactory accuracy. Therefore, this study aims to investigatethe performance of the metaheuristic optimization algorithm, Neural NetworkAlgorithm (NNA), in obtaining Weibull parameters and comparing itsperformance with five numerical estimation approaches. In carrying out thestudy, the wind characteristics of three sites in Saudi Arabia, namely HaferAl Batin, Riyadh, and Sharurah, are analyzed. Results exhibit that NNA hashigh accuracy fitting results compared to the numerical estimation methods.The NNA demonstrates its efficiency in optimizing Weibull parameters at allthe considered sites with correlations exceeding 98.54.

A Self-organization Mapping Neural Network Algorithm and Its Application to Identify Ecosystem Service Zones

The self-organization mapping （SOM） neural network algorithm is a new method used to identify the ecosystem service zones at regional extent. According to the ecosystem assessment framework of Millennium Ecosystem Assessment （ MA）, this paper develops an indicator system and conducts a spatial cluster analysis at the 1km by I km grid pixel scale with the SOM neural network algorithm to sort the core ecosystem services over the vertical and horizontal dimensions. A case study was carried out in Xilingol League. The ecosystem services in Xilingol League could be divided to six different ecological zones. The SOM neural network algorithm was capable of identifying the similarities among the input data automatically. The research provides both spatially and temporally valuable information targeted sustainable ecosystem management for decision-makers.

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.

Genetic Nelder-Mead neural network algorithm for fault parameter inversion using GPS data

The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment.

Recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm

Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.

Composite Structural Optimization by Genetic Algorithm and Neural Network Response Surface Modeling

Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to select the most appropriate design samples for network training. The trained response surfaces can either be objective function or constraint conditions. Together with other conven- tional constraints, an optimization model is then set up and can be solved by Genetic Algorithm （GA）. This allows the separation between design analysis modeling and optimization searching. Through an example of a hat-stiffened composite plate design, the weight response surface is constructed to be objective function, and strength and buckling response surfaces as constraints; and all of them are trained through NASTRAN finite element analysis. The results of optimization study illustrate that the cycles of structural analysis ean be remarkably reduced or even eliminated during the optimization, thus greatly raising the efficiency of optimization process. It also observed that NNRS approximation can achieve equal or even better accuracy than conventional functional response surfaces.

Design of artificial neural networks using a genetic algorithm to predict saturates of vacuum gas oil

Accurate prediction of chemical composition of vacuum gas oil （VGO） is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks （ANN） based on a genetic algorithm （GA） is developed for predicting VGO saturates. The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are five physical properties, namely, average boiling point, density, molecular weight, viscosity and refractive index. It is verified that the genetic algorithm could find the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be efficiently predicted. Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy.

Optimization of a crude distillation unit using a combination of wavelet neural network and line-up competition algorithm

The modeling and optimization of an industrial-scale crude distillation unit （CDU） are addressed. The main spec- ifications and base conditions of CDU are taken from a crude oil refinery in Wuhan, China. For modeling of a com- plicated CDU, an improved wavelet neural network （WNN） is presented to model the complicated CDU, in which novel parametric updating laws are developed to precisely capture the characteristics of CDU. To address CDU in an economically optimal manner, an economic optimization algorithm under prescribed constraints is presented. By using a combination of WNN-based optimization model and line-up competition algorithm （LCA）, the supe- rior performance of the proposed approach is verified. Compared with the base operating condition, it is validat- ed that the increments of products including kerosene and diesel are up to 20% at least by increasing less than 5% duties of intermediate coolers such as second pump-around （PA2） and third Dump-around （PA3）.

Mechanical Properties Prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network

For optimal design of mechanical clinching steel-aluminum joints, the back propagation （BP） neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body. Genetic algorithm （GA） is adopted to optimize the back-propagation neural network connection weights. The training and validating samples are made by the BTM Tog-L-Loc system with different technologic parameters. The training samples＇ parameters and the corresponding joints＇ mechanical properties are supplied to the artificial neural network （ANN） for training. The validating samples＇ experimental data is used for checking up the prediction outputs. The calculation results show that GA can improve the model＇s prediction precision and generalization ability of BP neural network. The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints. The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.

Relationship between fatigue life of asphalt concrete and polypropylene/polyester fibers using artificial neural network and genetic algorithm

While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network(ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm(GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy(correlation coefficient of 0.96).

The Development of Highly Loaded Turbine Rotating Blades by Using 3D Optimization Design Method of Turbomachinery Blades Based on Artificial Neural Network & Genetic Algorithm

In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%.

A genetic-algorithm-based neural network approach for EDXRF analysis

In energy dispersive X-ray fiuorescence(EDXRF), quantitative elemental content analysis becomes difficult due to the existence of the noise, the spectrum peak superposition, element matrix effect, etc. In this paper, a hybrid approach of genetic algorithm(GA) and back propagation(BP) neural network is proposed without considering the complex relationship between the elemental content and peak intensity. The aim of GA-optimized BP is to get better network initial weights and thresholds. The starting point of this approach is that the reciprocal of the mean square error of the initialization BP neural network is set as the fitness value of the individuals in GA; and the initial weights and thresholds are replaced by individuals, then the optimal individual is searched by selecting, crossover and mutation operations, finally a new BP neural network model is established with the optimal initial weights and thresholds. The quantitative analysis results of titanium and iron contents in five types of mineral samples show that the relative errors of 76.7% samples are below 2%, compared to chemical analysis data, which demonstrates the effectiveness of the proposed method.

Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.

Neural Network Predictive Control of Variable-pitch Wind Turbines Based on Small-world Optimization Algorithm

通过将混沌映射用于产生初始节点集和进行算子构造,提出一种新的基于实数编码的混沌小世界优化算法。采用4种算法对多例复杂函数的优化问题进行仿真试验,表明所提算法具有能够有效避免陷入局部极小值、快速搜索到最优值的能力。将上述方法应用于变桨距风电机组启动并网时的转速控制,提出一种基于混沌小世界优化算法的神经网络预测控制策略,其预测模型由基于现场数据的神经网络模型建立。仿真与实际测试结果表明,该系统可以根据风速扰动提前预测电机的转速变化,使控制器超前动作,保证系统输出跟踪参考轨迹的方向稳步改变,确保风电机组平稳并网。

Volterra Feedforward Neural Networks:Theory and Algorithms

The Volterra feedforward neural network with nonlinear interconnections and related homotopy learning algorithm are proposed in the paper. It is shown that Volterra neural network and the homolopy learning algorithms are significant potentials in nonlinear approximation ability,convergent speeds and global optimization than the classical neural networks and the standard BP algorithm, and related computer simulations and theoretical analysis are given too.

Architectures and Algorithms of Generalized Congruence Neural Networks

In this paper a novel class of neural networks called generalized congruence neural networks (GCNN) is proposed. All neurons in the neural networks are activated in the form of congruence. The architectures, learning rules and two algorithms are presented. Simulation results indicate that such network has satisfactory generalization properties near the sample points. Since this kind of neural nets can be easily operated and implemented, it is appropriate to make further research concerning the theory and applications of GCNN.

Application of a neural network system combined with genetic algorithm to rank coalbed methane reservoirs in the order of exploitation priority

A new method based on the combination of a neural network and a genetic algorithm was proposed to rank the order of exploitation priority of coalbed methane reservoirs. The neural network was used to acquire the weights of reservoir parameters through sample training and genetic algorithm was used to optimize the initial connection weights of nerve cells in case the neural network fell into a local minimum. Additionally, subordinate functions of each parameter were established to normalize the actual values of parameters of coalbed methane reservoirs in the range between zero and unity. Eventually, evaluation values of all coalbed methane reservoirs could be obtained by using the comprehensive evaluation method, which is the basis to rank the coalbed methane reservoirs in the order of exploitation priority. The greater the evaluation value, the higher the exploitation priority. The ranking method was verified in this paper by ten exploited coalbed methane reservoirs in China. The evaluation results are in agreement with the actual exploitation cases. The method can ensure the truthfulness and credibility of the weights of parameters and avoid the subjectivity caused by experts. Furthermore, the probability of falling into local minima is reduced, because genetic the algorithm is used to optimize the neural network system.

A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal

A genetic algorithm based on the nested intervals chaos search (NICGA) hasbeen given. Because the nested intervals chaos search is introduced into the NICGA to initialize thepopulation and to lead the evolution of the population, the NICGA has the advantages of decreasingthe population size, enhancing the local search ability, and improving the computational efficiencyand optimization precision. In a multi4ayer feed forward neural network model for predicting thesilicon content in hot metal, the NICGA was used to optimize the connection weights and thresholdvalues of the neural network to improve the prediction precision. The application results show thatthe precision of predicting the silicon content has been increased.

Forecasting increasing rate of power consumption based on immune genetic algorithm combined with neural network

Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.