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Artificial neural network

Xem 1-20 trên 193 kết quả Artificial neural network
  • The potential value of artificial neural networks (ANNs) as a predictor of malignancy has now been widely recognised. The concept of ANNs dates back to the early part of the 20th century; however, their latest resurrection started in earnest in the 1980s when they were applied to many problems in the areas of pattern recognition, control, and optimisation.

    pdf212p echbuon 02-11-2012 70 7   Download

  • Chapter 3: Artificial neural networks Introduction; ANN representations, Perceptron Training, Multilayer networks and Backpropagation algorithm, Remarks on the Backpropagation algorithm, Neural network application development, Benefits and limitations of ANN, ANN Applications.

    ppt71p cocacola_10 08-12-2015 49 3   Download

  • The SVM suffers from the complex computational processes. Therefore, this paper presents a new space vector modulation controller based soft computing-high accuracy implementation of artificial neural network. An artificial neural network (ANN) structure is proposed to identify and estimated the conventional SVM for avoiding the complex computational problem and hence improve the performance of the photovoltaic inverter generation. The ANN model receives the αβ voltages information at the input side and generates the duty ratios (Ta, Tb, and Tc) as an output.

    pdf16p hongnhan878 12-04-2019 23 2   Download

  • Rule Extraction from Artificial Neural Networks includes The train problem, Motivations, Feedforward neural networks for classification, Rulle exttractition ffrom neurall nettworkks Examples, Different types of classification rules, RegressionRegression rulesrules, Hierarchical rules.

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  • This study corrected and supplemented the broken segments, then use the corrected and supplemented curves to calculate porosity. The porosity calculated in this study for 9 wells has been used by JVPC to build the mining production technology diagrams, whle the existing softwares can not calculate this parameter. The testing result proves that the Artificial Neural Network model (ANN) of this study is great tool for correction and supplementing of the well log curves.

    pdf10p nguyenvanhoangvnu 12-06-2017 25 1   Download

  • This paper presents method of feature subset selection in dynamic stability assessment (DSA) power system using artificial neural networks (ANN). In the application of ANN on DSA power system, feature subset selection aims to reduce the number of training features, cost and memory computer.

    pdf10p sansan2 26-05-2018 27 1   Download

  • Bài giảng "Máy học nâng cao: Artificial neural network" cung cấp cho người học các kiến thức: Introduction, perceptron, neural network, backpropagation algorithm. Mời các bạn cùng tham khảo nội dung chi tiết.

    pdf62p abcxyz123_08 11-04-2020 13 1   Download

  • Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR.

    pdf11p viwyoming2711 16-12-2020 9 1   Download

  • Being faced with practical problems in pest identification, we present a methodical paper based on artificial neural networks to discriminate morphologically very similar species, Thrips sambuci Heeger, 1854 and Thrips fuscipennis Haliday, 1836 (Thysanoptera: Thripinae), as an applied case for more general use.

    pdf14p vimb123 11-01-2019 5 0   Download

  • This study aimed to model the performance indices of deep bed drying of rough rice using artificial neural networks (ANNs), compare the ANN approach to the multivariate regression method, and determine the sensitivity of the ANN model to the input variables.

    pdf11p vimb123 12-01-2019 28 0   Download

  • The objective of this paper was to develop an artificial neural network (ANN) model in order to predict monthly mean soil temperature for the present month by using various previous monthly mean meteorological variables.

    pdf11p masterbarista 15-01-2019 11 0   Download

  • This research starts with the prior knowledge generation process using the Latent Semantic Indexing (LSI) method. LSI is a technique using Singular Value Decomposition (SVD) to find meaning in a sentence. LSI works to generate the prior knowledge of each learner. After the prior knowledge is raised, then one can predict learning style using the artificial neural network (ANN) method. The results of this study are more accurate than the results of detection conducted with an external approach.

    pdf11p nguyentunanh2502 27-04-2019 12 0   Download

  • The current trend of increasing construction project size and complexity results in higher level of project risk. As a result, risk management is a crucial determinant of the success of a project. It seems necessary for construction companies to integrate a risk management system into their organizational structure. The main aim of this paper is to propose a risk assessment framework using Artificial Neural Network (ANN) technique. Three main phases of the proposed framework are risk management phase, ANN training phase and framework application phase.

    pdf12p vinobita2711 31-05-2019 10 0   Download

  • This paper discusses the usefulness of artificial neural networks (ANNs) for response surface modeling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behavior of a mixture of salbutamol (SAL) and guaiphenesin (GUA), combination I, and a mixture of ascorbic acid (ASC), paracetamol (PAR) and guaiphenesin (GUA), combination II, was investigated. The results were compared with those produced using multiple regression (REG) analysis.

    pdf11p kethamoi1 17-11-2019 10 0   Download

  • Precipitation records often suffer from missing data values for certain time periods due to various reasons, one of them being the malfunctioning of rain gauges. This is an important issue in practical hydrology as it affects the continuity of rainfall data. The missing data values ultimately influence the results of hydrologic studies that use rainfall data as one of the input variables. Therefore, it is crucial to estimate the missing rainfall data for qualitative hydrologic assessment.

    pdf9p nguaconbaynhay1 04-12-2019 10 0   Download

  • The aim of this paper is to find an effective model between MIKE 21 - numerical model and an Artificial Neural Network (ANN) model in predicting sea level height in Qui Nhon, Vietnam during four storm events: Ketsana-2009, SonTinh2012, Nari-2013 and Wutip – 2013.

    pdf6p vimante2711 05-03-2020 7 0   Download

  • Water resource assessment involved various variables that can be simplified and tackled by developing a suitable mathematical model. Rainfall-Runoff (RR) modeling considered as a major hydrologic process and is essential for water resources management. This study presents the development of rainfall-runoff model based on artificial neural networks (ANNs) models in Shipra river basin of Madhya Pradesh. The ability of model was evaluated based on sum of squares error (SSE) and relative error.

    pdf9p caygaocaolon4 04-04-2020 16 0   Download

  • Forecasting of stream flow and ground water level changes became an important component of water resources system control and challenging task for water resources engineers and managers. The ground water level data and rainfall data of twenty years from 1996 to 2015 were collected. Artificial neural network (ANN) is used to predict water resources variable. The model was trained, validated and tested for randomly divided samples. The regression analysis shows good correlation between each other within the range 0.12 to 0.97 of Abhanpur block.

    pdf8p kethamoi4 16-04-2020 6 0   Download

  • An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. The artificial neural network is increasingly used as a powerful tool for many real-world problems. In Textiles and Clothing industries, it involves the interaction of a large number of variables.

    pdf10p nguathienthan4 21-04-2020 9 0   Download

  • In this study, total annual exports and imports of the Kingdom of Saudi Arabia are forecasted using Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models.

    pdf12p tohitohi 22-05-2020 5 0   Download

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