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Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria
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作者 Djeldjli Halima Benatiallah Djelloul +3 位作者 Ghasri Mehdi Tanougast Camel Benatiallah Ali Benabdelkrim Bouchra 《Computers, Materials & Continua》 SCIE EI 2024年第6期4725-4740,共16页
When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global s... When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes. 展开更多
关键词 Solar energy systems genetic algorithm neural networks hybrid adaptive neuro fuzzy inference system solar radiation
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An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ
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作者 Afia Zafar Muhammad Aamir +6 位作者 Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi 《Computers, Materials & Continua》 SCIE EI 2023年第3期5641-5661,共21页
In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural ne... In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature. 展开更多
关键词 Non-dominated sorted genetic algorithm convolutional neural network hyper-parameter OPTIMIZATION
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Groundwater level prediction based on hybrid hierarchy genetic algorithm and RBF neural network 被引量:1
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作者 屈吉鸿 黄强 +1 位作者 陈南祥 徐建新 《Journal of Coal Science & Engineering(China)》 2007年第2期170-174,共5页
As the traditional non-linear systems generally based on gradient descent optimization method have some shortage in the field of groundwater level prediction, the paper, according to structure, algorithm and shortcomi... As the traditional non-linear systems generally based on gradient descent optimization method have some shortage in the field of groundwater level prediction, the paper, according to structure, algorithm and shortcoming of the conventional radial basis function neural network (RBF NN), presented a new improved genetic algorithm (GA): hybrid hierarchy genetic algorithm (HHGA). In training RBF NN, the algorithm can automatically determine the structure and parameters of RBF based on the given sample data. Compared with the traditional groundwater level prediction model based on back propagation (BP) or RBF NN, the new prediction model based on HHGA and RBF NN can greatly increase the convergence speed and precision. 展开更多
关键词 hybrid hierarchy genetic algorithm radial basis function neural network groundwater level prediction model
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Evolving Neural Networks Using an Improved Genetic Algorithm 被引量:2
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作者 温秀兰 宋爱国 +1 位作者 段江海 王一清 《Journal of Southeast University(English Edition)》 EI CAS 2002年第4期367-369,共3页
A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal gen... A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal generation gap (MGP) and blend crossover operators (BLX α). Compared with traditional GA implemented in binary number, the processing time of the improved GA is faster because coding and decoding are unnecessary. In addition, it needn t set parameters such as the probability value of crossove... 展开更多
关键词 genetic algorithms neural network nonlinear forecasting
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Combining the genetic algorithms with artificial neural networks for optimization of board allocating 被引量:2
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作者 曹军 张怡卓 岳琪 《Journal of Forestry Research》 SCIE CAS CSCD 2003年第1期87-88,共2页
This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in boa... This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum. 展开更多
关键词 Artificial neural network genetic algorithms Back propagation model (BP model) OPTIMIZATION
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A Short-Term Traffic Flow Prediction ModelBased on Quantum Genetic Algorithm andFuzzy RBF Neural Networks
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作者 Kun Zhang 《计算机科学与技术汇刊(中英文版)》 2016年第1期24-39,共16页
关键词 神经网络 流动模拟 基因算法 rbf 交通 预言 短期 ARIMA
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Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm
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作者 Zan Zhou Thomas Man-Hoi Lok Wan-Huan Zhou 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2024年第2期345-358,共14页
Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known bef... Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable.However,an improper selection of the number of layers may lead to an incorrect shear wave velocity profile.In this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers.First,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number.Then,the shear-wave velocity profile is determined by a genetic algorithm with the known layer number.By applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion. 展开更多
关键词 surface wave inversion analysis shear-wave velocity profile deep neural network genetic algorithm
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A Genetic Algorithm-Based Optimized Transfer Learning Approach for Breast Cancer Diagnosis
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作者 Hussain AlSalman Taha Alfakih +2 位作者 Mabrook Al-Rakhami Mohammad Mehedi Hassan Amerah Alabrah 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第12期2575-2608,共34页
Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics,integral for early detection and effective treatment.While deep learning has significantly advanced the analy... Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics,integral for early detection and effective treatment.While deep learning has significantly advanced the analysis of mammographic images,challenges such as low contrast,image noise,and the high dimensionality of features often degrade model performance.Addressing these challenges,our study introduces a novel method integrating Genetic Algorithms(GA)with pre-trained Convolutional Neural Network(CNN)models to enhance feature selection and classification accuracy.Our approach involves a systematic process:first,we employ widely-used CNN architectures(VGG16,VGG19,MobileNet,and DenseNet)to extract a broad range of features from the Medical Image Analysis Society(MIAS)mammography dataset.Subsequently,a GA optimizes these features by selecting the most relevant and least redundant,aiming to overcome the typical pitfalls of high dimensionality.The selected features are then utilized to train several classifiers,including Linear and Polynomial Support Vector Machines(SVMs),K-Nearest Neighbors,Decision Trees,and Random Forests,enabling a robust evaluation of the method’s effectiveness across varied learning algorithms.Our extensive experimental evaluation demonstrates that the integration of MobileNet and GA significantly improves classification accuracy,from 83.33%to 89.58%,underscoring the method’s efficacy.By detailing these steps,we highlight the innovation of our approach which not only addresses key issues in breast cancer imaging analysis but also offers a scalable solution potentially applicable to other domains within medical imaging. 展开更多
关键词 Deep learning convolution neural network(CNN) support vector machine(SVM) genetic algorithmic(GA) breast cancer an optimized smart diagnosis
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NEURAL NETWORK PREDICTIVE CONTROL WITH HIERARCHICAL GENETIC ALGORITHM
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作者 刘宝坤 王慧 李光泉 《Transactions of Tianjin University》 EI CAS 1998年第2期48-50,共3页
A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence da... A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness. 展开更多
关键词 neural networks(NN) predictive control hierarchical genetic algorithms nonlinear system
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Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks
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作者 Gibrael Abosamra 《Computers, Materials & Continua》 SCIE EI 2023年第2期3539-3560,共22页
This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input ... This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution candidates.Due to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions.The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype.Using any of the previous versions will give moderate results but with very low speed.Hence,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols.The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques.Also,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns.Various image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new improvements.Encouraging results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by 1.45%. 展开更多
关键词 genetic algorithms memetic algorithm convolutional neural network object detection adaptive binarization filters license plate detection
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
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作者 Shehab Abdulhabib Alzaeemi Kim Gaik Tay +2 位作者 Audrey Huong Saratha Sathasivam Majid Khan bin Majahar Ali 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期1163-1184,共22页
Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algor... Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT. 展开更多
关键词 Satisfiability logic programming symbolic radial basis function neural network evolutionary programming algorithm genetic algorithm evolution strategy algorithm differential evolution algorithm
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Mechanical Properties Prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network 被引量:22
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作者 LONG Jiangqi LAN Fengchong CHEN Jiqing YU Ping 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2009年第1期36-41,共6页
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,... 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. 展开更多
关键词 genetic algorithm BP neural network mechanical clinching JOINT properties prediction
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Design of artificial neural networks using a genetic algorithm to predict saturates of vacuum gas oil 被引量:15
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作者 Dong Xiucheng Wang Shouchun +1 位作者 Sun Renjin Zhao Suoqi 《Petroleum Science》 SCIE CAS CSCD 2010年第1期118-122,共5页
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... 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. 展开更多
关键词 Saturates vacuum gas oil PREDICTION artificial neural networks genetic algorithm
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Composite Structural Optimization by Genetic Algorithm and Neural Network Response Surface Modeling 被引量:13
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作者 徐元铭 李烁 荣晓敏 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2005年第4期310-316,共7页
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 s... 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. 展开更多
关键词 neural network genetic algorithm response surface composite structural optimization
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Relationship between fatigue life of asphalt concrete and polypropylene/polyester fibers using artificial neural network and genetic algorithm 被引量:6
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作者 Morteza Vadood Majid Safar Johari Ali Reza Rahai 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第5期1937-1946,共10页
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 po... 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). 展开更多
关键词 hot mix asphalt fatigue property reinforced fiber artificial neural network genetic algorithm
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Application of a neural network system combined with genetic algorithm to rank coalbed methane reservoirs in the order of exploitation priority 被引量:4
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作者 Li Weichao Wu Xiaodong Shi Junfeng 《Petroleum Science》 SCIE CAS CSCD 2008年第4期334-339,共6页
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 weigh... 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. 展开更多
关键词 Coalbed methane neural network system genetic algorithm evaluation index WEIGHT
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Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm 被引量:9
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作者 Hong-Bin Wang Mian Liu 《International Journal of Automation and computing》 EI 2012年第1期24-29,共6页
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 req... 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. 展开更多
关键词 Visual servo image Jacobian back propagation (BP) neural network genetic algorithm robot control
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Optimization of Processing Parameters of Power Spinning for Bushing Based on Neural Network and Genetic Algorithms 被引量:3
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作者 Junsheng Zhao Yuantong Gu Zhigang Feng 《Journal of Beijing Institute of Technology》 EI CAS 2019年第3期606-616,共11页
A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization o... A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications. 展开更多
关键词 power SPINNING process parameters optimization BP neural network genetic algorithms (GA) response surface methodology (RSM)
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The Development of Highly Loaded Turbine Rotating Blades by Using 3D Optimization Design Method of Turbomachinery Blades Based on Artificial Neural Network & Genetic Algorithm 被引量:3
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作者 周凡贞 冯国泰 蒋洪德 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2003年第4期198-202,共5页
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 alg... 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%. 展开更多
关键词 optimization design highly loaded rotating blades artificial neural network genetic algorithm
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Simulation and Optimization for Thermally Coupled Distillation Using Artificial Neural Network and Genetic Algorithm 被引量:3
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作者 王延敏 姚平经 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2003年第3期307-311,共5页
In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neura... In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neural network based on the simulation results with ASPEN PLUS. Modified genetic algorithm was used to optimize the model. With the proposed model and optimization arithmetic, mathematical model can be calculated, decision variables and target value can be reached automatically and quickly. A practical example is used to demonstrate the algorithm. 展开更多
关键词 thermally coupled distillation neural network genetic algorithm SIMULATION OPTIMIZATION ASPEN PLUS
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