際際滷shows by User: arghasit110 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: arghasit110 / Mon, 08 Apr 2013 12:01:38 GMT 際際滷Share feed for 際際滷shows by User: arghasit110 Training artificial neural network using particle swarm optimization algorithm /slideshow/training-artificial-neural-network-using-particle-swarm-optimization-algorithm-18416411/18416411 trainingartificialneuralnetworkusingparticleswarmoptimizationalgorithm-130408120138-phpapp02
Abstract - In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in classification of IRIS dataset. Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.]]>
Abstract - In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in classification of IRIS dataset. Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.]]> Mon, 08 Apr 2013 12:01:38 GMT /slideshow/training-artificial-neural-network-using-particle-swarm-optimization-algorithm-18416411/18416411 arghasit110@slideshare.net(arghasit110) Training artificial neural network using particle swarm optimization algorithm arghasit110 Abstract - In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in classification of IRIS dataset. Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/trainingartificialneuralnetworkusingparticleswarmoptimizationalgorithm-130408120138-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Abstract - In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in classification of IRIS dataset. Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.
Training artificial neural network using particle swarm optimization algorithm from A. Roy
]]> 818 5 https://cdn.slidesharecdn.com/ss_thumbnails/trainingartificialneuralnetworkusingparticleswarmoptimizationalgorithm-130408120138-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Visualizing a cloud using eucalyptus and xen /slideshow/visualizing-a-cloud-using-eucalyptus-and-xen/18416195 visualizingacloudusingeucalyptusandxen-130408115412-phpapp01
Abstract - The clouds are a large pool of virtualized resources which are easy to use and access. As per NIST Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. There are various ways of setting up clouds in an academic or IT infrastructure. We are proposing a method to setup a cloud infrastructure using Eucalyptus and Xen. Eucalyptus is an open source cloud computing framework that gives users the ability to create, run and manage virtual machine instances across physical machines. Xen is the hypervisor upon which the virtual machines run on the host computer. ]]>
Abstract - The clouds are a large pool of virtualized resources which are easy to use and access. As per NIST Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. There are various ways of setting up clouds in an academic or IT infrastructure. We are proposing a method to setup a cloud infrastructure using Eucalyptus and Xen. Eucalyptus is an open source cloud computing framework that gives users the ability to create, run and manage virtual machine instances across physical machines. Xen is the hypervisor upon which the virtual machines run on the host computer. ]]> Mon, 08 Apr 2013 11:54:12 GMT /slideshow/visualizing-a-cloud-using-eucalyptus-and-xen/18416195 arghasit110@slideshare.net(arghasit110) Visualizing a cloud using eucalyptus and xen arghasit110 Abstract - The clouds are a large pool of virtualized resources which are easy to use and access. As per NIST Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. There are various ways of setting up clouds in an academic or IT infrastructure. We are proposing a method to setup a cloud infrastructure using Eucalyptus and Xen. Eucalyptus is an open source cloud computing framework that gives users the ability to create, run and manage virtual machine instances across physical machines. Xen is the hypervisor upon which the virtual machines run on the host computer. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/visualizingacloudusingeucalyptusandxen-130408115412-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Abstract - The clouds are a large pool of virtualized resources which are easy to use and access. As per NIST Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. There are various ways of setting up clouds in an academic or IT infrastructure. We are proposing a method to setup a cloud infrastructure using Eucalyptus and Xen. Eucalyptus is an open source cloud computing framework that gives users the ability to create, run and manage virtual machine instances across physical machines. Xen is the hypervisor upon which the virtual machines run on the host computer.
Visualizing a cloud using eucalyptus and xen from A. Roy
]]> 1052 4 https://cdn.slidesharecdn.com/ss_thumbnails/visualizingacloudusingeucalyptusandxen-130408115412-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Training artificial neural network using particle swarm optimization algorithm /slideshow/training-artificial-neural-network-using-particle-swarm-optimization-algorithm/18415991 trainingartificialneuralnetworkusingparticleswarmoptimizationalgorithm-130408114829-phpapp01
Abstract - In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in classification of IRIS dataset. Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.]]>
Abstract - In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in classification of IRIS dataset. Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.]]> Mon, 08 Apr 2013 11:48:29 GMT /slideshow/training-artificial-neural-network-using-particle-swarm-optimization-algorithm/18415991 arghasit110@slideshare.net(arghasit110) Training artificial neural network using particle swarm optimization algorithm arghasit110 Abstract - In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in classification of IRIS dataset. Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/trainingartificialneuralnetworkusingparticleswarmoptimizationalgorithm-130408114829-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Abstract - In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in classification of IRIS dataset. Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.
Training artificial neural network using particle swarm optimization algorithm from A. Roy
]]> 2365 6 https://cdn.slidesharecdn.com/ss_thumbnails/trainingartificialneuralnetworkusingparticleswarmoptimizationalgorithm-130408114829-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png I am passionate about the Latest Technological Products and love to innovate in my methodologies to bring out exciting results. https://cdn.slidesharecdn.com/ss_thumbnails/trainingartificialneuralnetworkusingparticleswarmoptimizationalgorithm-130408120138-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/training-artificial-neural-network-using-particle-swarm-optimization-algorithm-18416411/18416411 Training artificial ne... https://cdn.slidesharecdn.com/ss_thumbnails/visualizingacloudusingeucalyptusandxen-130408115412-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/visualizing-a-cloud-using-eucalyptus-and-xen/18416195 Visualizing a cloud us... https://cdn.slidesharecdn.com/ss_thumbnails/trainingartificialneuralnetworkusingparticleswarmoptimizationalgorithm-130408114829-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/training-artificial-neural-network-using-particle-swarm-optimization-algorithm/18415991 Training artificial ne...