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Quasi-Non-Destructive Evaluation of Yield Strength Using Neural Networks 基于神经网络的屈服强度准无损评价
Adv. Artif. Neural Syst. Pub Date : 2011-01-01 DOI: 10.1155/2011/607374
G. Partheepan, D. K. Sehgal, R. Pandey
{"title":"Quasi-Non-Destructive Evaluation of Yield Strength Using Neural Networks","authors":"G. Partheepan, D. K. Sehgal, R. Pandey","doi":"10.1155/2011/607374","DOIUrl":"https://doi.org/10.1155/2011/607374","url":null,"abstract":"The objective of this paper is to delineate a method for determining the yield strength of a material in a virtually nondestructive manner. Conventional test methods for predicting the yield strength require the removal of large material samples from the inservice component, which is impractical. In this paper, the power of neural networks in predicting the yield strength from the data obtained by conducting tension test on newly developed dumb-bell-shaped miniature specimen is demonstrated using the self-organizing capabilities of the ANN. The input to the neural network is the breakaway load obtained from the miniature test, and the output obtained from the model is yield strength value. The value of the yield strength estimated by neural network is found to be in good agreement (<5% error) with that of the actual value from the standard test. The neural network models are convenient and powerful tools for practical applications in solving various problems in engineering.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"49 1","pages":"607374:1-607374:8"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80721115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Navigation Behaviors Based on Fuzzy ArtMap Neural Networks for Intelligent Autonomous Vehicles 基于模糊ArtMap神经网络的智能自动驾驶汽车导航行为
Adv. Artif. Neural Syst. Pub Date : 2011-01-01 DOI: 10.1155/2011/523094
A. Chohra, O. Azouaoui
{"title":"Navigation Behaviors Based on Fuzzy ArtMap Neural Networks for Intelligent Autonomous Vehicles","authors":"A. Chohra, O. Azouaoui","doi":"10.1155/2011/523094","DOIUrl":"https://doi.org/10.1155/2011/523094","url":null,"abstract":"The use of hybrid intelligent systems (HISs) is necessary to bring the behavior of intelligent autonomous vehicles (IAVs) near the human one in recognition, learning, adaptation, generalization, decision making, and action. First, the necessity of HIS and some navigation approaches based on fuzzy ArtMap neural networks (FAMNNs) are discussed. Indeed, such approaches can provide IAV with more autonomy, intelligence, and real-time processing capabilities. Second, an FAMNN-based navigation approach is suggested. Indeed, this approach must provide vehicles with capability, after supervised fast stable learning: simplified fuzzy ArtMap (SFAM), to recognize both target-location and obstacle-avoidance situations using FAMNN1 and FAMNN2, respectively. Afterwards, the decision making and action consist of two association stages, carried out by reinforcement trial and error learning, and their coordination using NN3. Then, NN3 allows to decide among the five (05) actions to move towards 30°, 60°, 90°, 120°, and 150°. Third, simulation results display the ability of the FAMNN-based approach to provide IAV with intelligent behaviors allowing to intelligently navigate in partially structured environments. Finally, a discussion, dealing with the suggested approach and how its robustness would be if implemented on real vehicle, is given.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"93 1","pages":"523094:1-523094:11"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74102015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Using Artificial Neural Networks to Predict Direct Solar Irradiation 利用人工神经网络预测太阳直射辐射
Adv. Artif. Neural Syst. Pub Date : 2011-01-01 DOI: 10.1155/2011/142054
J. Mubiru
{"title":"Using Artificial Neural Networks to Predict Direct Solar Irradiation","authors":"J. Mubiru","doi":"10.1155/2011/142054","DOIUrl":"https://doi.org/10.1155/2011/142054","url":null,"abstract":"This paper explores the possibility of developing a prediction model using artificial neural networks (ANNs), which could be used to estimate monthly average daily direct solar radiation for locations in Uganda. Direct solar radiation is a component of the global solar radiation and is quite significant in the performance assessment of various solar energy applications. Results from the paper have shown good agreement between the estimated and measured values of direct solar irradiation. A correlation coefficient of 0.998 was obtained withmean bias error of 0.005 MJ/m2 and rootmean square error of 0.197 MJ/m2. The comparison between the ANN and empirical model emphasized the superiority of the proposed ANN prediction model. The application of the proposed ANN model can be extended to other locations with similar climate and terrain.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"41 1","pages":"142054:1-142054:6"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84164762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 38
Stock Price Prediction Based on Procedural Neural Networks 基于过程神经网络的股票价格预测
Adv. Artif. Neural Syst. Pub Date : 2011-01-01 DOI: 10.1155/2011/814769
Jiuzhen Liang, Weiguo Song, Mei Wang
{"title":"Stock Price Prediction Based on Procedural Neural Networks","authors":"Jiuzhen Liang, Weiguo Song, Mei Wang","doi":"10.1155/2011/814769","DOIUrl":"https://doi.org/10.1155/2011/814769","url":null,"abstract":"We present a spatiotemporal model, namely, procedural neural networks for stock price prediction. Compared with some successful traditional models on simulating stock market, such as BNN (backpropagation neural networks, HMM (hidden Markov model) and SVM (support vector machine)), the procedural neural network model processes both spacial and temporal information synchronously without slide time window, which is typically used in the well-known recurrent neural networks. Two different structures of procedural neural networks are constructed for modeling multidimensional time series problems. Learning algorithms for training the models and sustained improvement of learning are presented and discussed. Experiments on Yahoo stock market of the past decade years are implemented, and simulation results are compared by PNN, BNN, HMM, and SVM.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"60 1","pages":"814769:1-814769:11"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87127670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
A Novel Learning Scheme for Chebyshev Functional Link Neural Networks 一种新的Chebyshev函数链接神经网络学习方案
Adv. Artif. Neural Syst. Pub Date : 2011-01-01 DOI: 10.1155/2011/107498
Satchidananda Dehuri
{"title":"A Novel Learning Scheme for Chebyshev Functional Link Neural Networks","authors":"Satchidananda Dehuri","doi":"10.1155/2011/107498","DOIUrl":"https://doi.org/10.1155/2011/107498","url":null,"abstract":"A hybrid learning scheme (ePSO-BP) to train Chebyshev Functional Link Neural Network (CFLNN) for classification is presented. The proposed method is referred as hybrid CFLNN (HCFLNN). The HCFLNN is a type of feed-forward neural networks which have the ability to transform the nonlinear input space into higher dimensional-space where linear separability is possible. Moreover, the proposed HCFLNN combines the best attribute of particle swarm optimization (PSO), back propagation learning (BP learning), and functional link neural networks (FLNNs). The proposed method eliminates the need of hidden layer by expanding the input patterns using Chebyshev orthogonal polynomials. We have shown its effectiveness of classifying the unknown pattern using the publicly available datasets obtained from UCI repository. The computational results are then compared with functional link neural network (FLNN) with a generic basis functions, PSO-based FLNN, and EFLN. From the comparative study, we observed that the performance of the HCFLNN outperforms FLNN, PSO-based FLNN, and EFLN in terms of classification accuracy.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"91 1","pages":"107498:1-107498:10"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81605724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
A New Procedure for Damage Assessment of Prestressed Concrete Beams Using Artificial Neural Network 基于人工神经网络的预应力混凝土梁损伤评估新方法
Adv. Artif. Neural Syst. Pub Date : 2011-01-01 DOI: 10.1155/2011/786535
K. Sumangala, C. A. J. Chellam
{"title":"A New Procedure for Damage Assessment of Prestressed Concrete Beams Using Artificial Neural Network","authors":"K. Sumangala, C. A. J. Chellam","doi":"10.1155/2011/786535","DOIUrl":"https://doi.org/10.1155/2011/786535","url":null,"abstract":"A damage assessment procedure has been developed using artificial neural network (ANN) for prestressed concrete beams. The methodology had been formulated using the results obtained from an experimental study conducted in the laboratory. Prestressed concrete (PSC) rectangular beams were cast, and pitting corrosion was introduced in the prestressing wires and was allowed to be snapped using accelerated corrosion process. Both static and dynamic tests were conducted to study the behaviour of perfect and damaged beams. The measured output from both static and dynamic tests was taken as input to train the neural network. Back propagation network was chosen for this purpose, which was written using the programming package MATLAB. The trained network was tested using separate test data obtained from the tests. A damage assessment procedure was developed using the trained network, it was validated using the data available in literature, and the outcome is presented in this paper.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"180 1","pages":"786535:1-786535:9"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77667026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Genetic Algorithm-Based Artificial Neural Network for Voltage Stability Assessment 基于遗传算法的人工神经网络电压稳定性评估
Adv. Artif. Neural Syst. Pub Date : 2011-01-01 DOI: 10.1155/2011/532785
Garima Singh, L. Srivastava
{"title":"Genetic Algorithm-Based Artificial Neural Network for Voltage Stability Assessment","authors":"Garima Singh, L. Srivastava","doi":"10.1155/2011/532785","DOIUrl":"https://doi.org/10.1155/2011/532785","url":null,"abstract":"With the emerging trend of restructuring in the electric power industry, many transmission lines have been forced to operate at almost their full capacities worldwide. Due to this, more incidents of voltage instability and collapse are being observed throughout the world leading to major system breakdowns. To avoid these undesirable incidents, a fast and accurate estimation of voltage stability margin is required. In this paper, genetic algorithm based back propagation neural network (GABPNN) has been proposed for voltage stability margin estimation which is an indication of the power system's proximity to voltage collapse. The proposed approach utilizes a hybrid algorithm that integrates genetic algorithm and the back propagation neural network. The proposed algorithm aims to combine the capacity of GAs in avoiding local minima and at the same time fast execution of the BP algorithm. Input features for GABPNN are selected on the basis of angular distance-based clustering technique. The performance of the proposed GABPNN approach has been compared with the most commonly used gradient based BP neural network by estimating the voltage stability margin at different loading conditions in 6-bus and IEEE 30-bus system. GA based neural network learns faster, at the same time it provides more accurate voltage stability margin estimation as compared to that based on BP algorithm. It is found to be suitable for online applications in energy management systems.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"43 1","pages":"532785:1-532785:9"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81587073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Adaptive Neurofuzzy Inference System-Based Pollution Severity Prediction of Polymeric Insulators in Power Transmission Lines 基于自适应神经模糊推理系统的输电线路聚合物绝缘子污染程度预测
Adv. Artif. Neural Syst. Pub Date : 2011-01-01 DOI: 10.1155/2011/431357
C. Muniraj, S. Chandrasekar
{"title":"Adaptive Neurofuzzy Inference System-Based Pollution Severity Prediction of Polymeric Insulators in Power Transmission Lines","authors":"C. Muniraj, S. Chandrasekar","doi":"10.1155/2011/431357","DOIUrl":"https://doi.org/10.1155/2011/431357","url":null,"abstract":"This paper presents the prediction of pollution severity of the polymeric insulators used in power transmission lines using adaptive neurofuzzy inference system (ANFIS) model. In this work, laboratory-based pollution performance tests were carried out on 11 kV silicone rubber polymeric insulator under AC voltage at different pollution levels with sodium chloride as a contaminant. Leakage current was measured during the laboratory tests. Time domain and frequency domain characteristics of leakage current, such as mean value, maximum value, standard deviation, and total harmonics distortion (THD), have been extracted, which jointly describe the pollution severity of the polymeric insulator surface. Leakage current characteristics are used as the inputs of ANFIS model. The pollution severity index \"equivalent salt deposit density\" (ESDD) is used as the output of the proposed model. Results of the research can give sufficient prewarning time before pollution flashover and help in the condition based maintenance (CBM) chart preparation.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"2011 1","pages":"431357:1-431357:9"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85871219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
A Sequential Algorithm for Training the SOM Prototypes Based on Higher-Order Recursive Equations 基于高阶递归方程的SOM原型序列训练算法
Adv. Artif. Neural Syst. Pub Date : 2010-01-01 DOI: 10.1155/2010/142540
M. Tucci, Marco Raugi
{"title":"A Sequential Algorithm for Training the SOM Prototypes Based on Higher-Order Recursive Equations","authors":"M. Tucci, Marco Raugi","doi":"10.1155/2010/142540","DOIUrl":"https://doi.org/10.1155/2010/142540","url":null,"abstract":"A novel training algorithm is proposed for the formation of Self-Organizing Maps (SOM). In the proposed model, the weights are updated incrementally by using a higher-order difference equation, which implements a low-pass digital filter. It is possible to improve selected features of the self-organization process with respect to the basic SOM by suitably designing the filter. Moreover, from this model, new visualization tools can be derived for cluster visualization and for monitoring the quality of the map.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"32 1","pages":"142540:1-142540:10"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86207406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
OP-KNN: Method and Applications OP-KNN:方法与应用
Adv. Artif. Neural Syst. Pub Date : 2010-01-01 DOI: 10.1155/2010/597373
Qi Yu, Y. Miché, A. Sorjamaa, A. Guillén, A. Lendasse, E. Séverin
{"title":"OP-KNN: Method and Applications","authors":"Qi Yu, Y. Miché, A. Sorjamaa, A. Guillén, A. Lendasse, E. Séverin","doi":"10.1155/2010/597373","DOIUrl":"https://doi.org/10.1155/2010/597373","url":null,"abstract":"This paper presents a methodology named Optimally Pruned K-Nearest Neighbors (OP-KNNs) which has the advantage of competing with state-of-the-art methods while remaining fast. It builds a one hidden-layer feedforward neural network using K-Nearest Neighbors as kernels to perform regression. Multiresponse Sparse Regression (MRSR) is used in order to rank each kth nearest neighbor and finally Leave-One-Out estimation is used to select the optimal number of neighbors and to estimate the generalization performances. Since computational time of this method is small, this paper presents a strategy using OP-KNN to perform Variable Selection which is tested successfully on eight real-life data sets from different application fields. In summary, the most significant characteristic of this method is that it provides good performance and a comparatively simple model at extremely high-learning speed.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"51 1","pages":"597373:1-597373:6"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73466539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
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