artificial neural network
Artificial neural networks (ANN) or connectionist systems are computing systems that are inspired by, but not identical to, biological neural networks that constitute animal brains. Such systems “learn” to perform tasks by considering examples, generally without being programmed with task-specific rules.
Artificial neural network classification using a minimal training set- Comparison to conventional supervised classification
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Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is
Implementation of a fast artificial neural network library (fann)
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This report describes the implementation of a fast artificial neural network library in ANSI C called fann. The library implements multilayer feedforward networks with support for both fully connected and sparse connected networks. Fann offers support for execution in fixed
Use of artificial neural network in pattern recognition
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Among the various traditional approaches of pattern recognition the statistical approach has been most intensively studied and used in practice. More recently, the addition of artificial neural network techniques theory have been receiving significant attention. The design of a
Artificial convolution neural network techniques and applications for lung nodule detection
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We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the
An efficient weather forecasting system using artificial neural network
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Temperature warnings are important forecasts because they are used to protect life and property. Temperature forecasting is the application of science and technology to predict the state of the temperature for a future time and a given location. Temperature forecasts are
Artificial neural network approach for short term load forecasting for Illam region
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In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model
Landslide risk analysis using artificial neural network model focussing on different training sites
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This paper presents landslide hazard and risk analysis using remote sensing data, GIS tools and artificial neural network model. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and
Relating the land-cover composition of mixed pixels to artificial neural network classification output
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Abstract ses, the classification procedures generally used to produce a~~ tifi~ i~ l neural networks are attractive for use in the classi- land-cover map are hard techniques which force allocation fication of land cover from remotely sensed data. In common to One class
Research paper on basic of artificial neural network
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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
Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller
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This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error hackpropa-gation learning algorithm designed for
Classification of Remotely Sensed Data by an Artificial Neural Network : Issues Related to Training Data
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Arlificial neurul networks have considerable potential for the classification of remotely sensed data. In this paper a feedforward artificial neural network using a variant of ihe backpropagation learning algorithm vas used to classify agricultural crops from synthetic
Tuning the stator resistance of induction motors using artificial neural network
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Tuning the stator resistance of induction motors is very important, especially when it is used to implement direct torque control (DTC) in which the stator resistance is a main parameter. In this paper, an artificial network (ANN) is used to accomplish tuning of the stator resistanceTo determine some mechanical parameters such as modulus of elasticity (E) and unconfined compressive strength (UCS) of gypsum is difficult, expensive, time consuming and involves destructive tests. The use of indirect estimation methods due to some index
Predicting students academic performance: comparing artificial neural network , decision tree and linear regression
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Predicting students academic performance is critical for educational institutions because strategic programs can be planned in improving or maintaining students performance during their period of studies in the institutions. The performance of the academic
Face recognition using eigen faces and artificial neural network
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Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology
Evaluation of artificial neural network applications in transportation engineering
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The increased interest in artificial neural networks (ANNs) seen in government and private research as well as business and industry has included relatively little activity in transportation engineering. The position that ANNs, as a branch of artificial intelligence, hold
Application of artificial neural network in stage-discharge relationship
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The prediction of discharge and its variability in a river is an essential component of surface- water planning. For that purpose, a functional relationship between stage and discharge is established with the help of field measurement and the relationship is expressed as a rating
Functional link artificial neural network for classification task in data mining
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Let us first recall a general model of an artificial neural network that consists of s simple computational units or neurons, indexed as V={1,, s}, where s=| V| is called the network size. Some of these units may serve as external inputs or outputs and hence we assume that
Agricultural crop yield prediction using artificial neural network approach
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By considering various situations of climatologically phenomena affecting local weather conditions in various parts of the world. These weather conditions have a direct effect on crop yield. Various researches have been done exploring the connections between large
Monthly inflow forecasting using autoregressive artificial neural network
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There are many forecasting models, but not all of them are able to monthly inflow forecasting. In this study, the abilities of static and dynamic artificial neural network model for Dez reservoir inflow forecasting compared. The 47-years monthly discharges used, so that