Artificial neural network

Xem 1-20 trên 99 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.

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  • 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.

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  • 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.

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  • 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 21 1   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.

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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  • This book covers 27 articles in the applications of artificial neural networks (ANN) in various disciplines which includes business, chemical technology, computing, engineering, environmental science, science and nanotechnology. They modeled the ANN with verification in different areas. They demonstrated that the ANN is very useful model and the ANN could be applied in problem solving and machine learning. This book is suitable for all professionals and scientists in understanding how ANN is applied in various areas....

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  • Tham khảo sách 'artificial neural networks methodological advances and biomedical applications_1', công nghệ thông tin, kỹ thuật lập trình phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications. The purpose of this book is to provide recent advances of architectures, methodologies, and applications of artificial neural networks.

    pdf264p phoebe75 19-02-2013 61 15   Download

  • Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. The purpose of this book is to provide recent advances of artificial neural networks in biomedical applications. The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization.

    pdf286p cucdai_1 20-10-2012 54 14   Download

  • Artificial neural networks are learning machines inspired by the operation of the human brain, and they consist of many artificial neurons connected in parallel. These networks work via non-linear mapping techniques between the inputs and outputs of a model indicative of the operation of a real system. Although introduced over 40 years ago, many wonderful new developments in neural networks have taken place as recently as during the last decade or so.

    pdf349p waduroi 03-11-2012 49 7   Download

  • This lecture introduces you to the fascinating subject of classification and regression with artificial neural networks. In particular, it introduces multi-layer perceptrons (MLPs); teaches you how to combine probability with neural networks so that the nets can be applied to regression, binary classification and multivariate classification; discusses the modular approach to backpropagation and neural network construction in Torch, which was introduced in the previous lecture.

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  • (BQ) Present study attempts to model and optimize the complex electrical discharge machining (EDM) process using soft computing techniques. Artificial neural network (ANN) with back propagation algorithm is used to model the process. As the output parameters are conflicting in nature so there is no single combination of cutting parameters, which provides the best machining performance. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process.

    pdf9p xuanphuongdhts 27-03-2017 24 2   Download

  • Leaf characteristics provide many useful clues for taxonomy. We used a back-propagation artificial neural network (BPANN) and C-support vector machines (C-SVMs) to classify 47 species from 3 sections of genus Camellia (16 from sect. Chrysanthae, 16 from sect. Tuberculata, and 15 from sect. Paracamellia).

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  • This thesis examines how artificial neural networks can benefit a large vocabulary, speaker independent, continuous speech recognition system. Currently, most speech recognition systems are based on hidden Markov models (HMMs), a statistical framework that supports both acoustic and temporal modeling. Despite their state-of-the-art performance, HMMs make a number of suboptimal modeling assumptions that limit their potential effectiveness.

    pdf186p kuckucucu 15-05-2012 110 26   Download



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