Yapay Sinir Ağları Nedir

Yapay Sinir Ağları Hakkında özet bilgiler

Fann Nedir

Fast Artifical Neural Networks Kütüphanesi hakkında


Nedir Ne işe yarar Nasıl Kullanırız

What are Artificial Neural Networks ?

short info about Artificial Neural Networks

What is FANN ?

about Fast Artifical Neural Networks library


What is and How to use

17 Ekim 2014 Cuma

Artificial Neural Network Approach to Mobile Robot Localization

Artificial Neural Network Approach to Mobile Robot Localization
David Swords
Aalto University / School of Electrical Engineering
Department of Automation and Systems Technology
Thesis submitted in partial fulfillment of the requirements for the degree of
Master of Science in Technology

In the robotics community, localization is considered a solved problem, however, the topic is still open to investigation. Mobile robot localization has been focused on developing low-cost approaches and there has been great success using probabilistic methods. Parallel to this, and to a much lesser extent, artificial neural networks (ANNs) have been applied to the problem area with varying success.

A system is proposed in this thesis where the typical probabilistic approach is replaced with one based purely on ANNs. This type of localization attempts to harness the simplicity, scalability and adaptability that ANNs are known for. The ANN approach allows for the encapsulation of a number of steps and elements well known in a probabilistic approach, resulting in the elimination of an internal explicit map, providing pose estimate on network output and network update at runtime.

First, a coordinate-based approach to localization is explored: 1D and 2D trained maps with pose estimates. Second, the coordinate-based approach is eliminated in an effort to replicate a more biologically inspired localization. Finally, a path-finding algorithm applying the new localizaiton approach is presented
The FANN library (FANN) provides tools implementing, designing, visualizing
and simulating ANNs.
FANN provides many commandline functions and visualization tools for creating,
training and simulating ANNs. With the availablity of visualization tools,
like FANNTool, make it much easier to deveop ANNs and applying them to
common tasks like curve fitting, pattern recognition, and clustering.
FANNTool is a GUI to the FANN library which allows ease of use without the
need to program the function calls necessary to perform simple tasks. FANNTool
is an open source project, supported by a community of FANN users.

14 Ekim 2014 Salı

Competing visions? Simulating alternative coastal futures using a GISANN web application

Paulo Morgado*, Eduardo Gomes, Nuno Costa
Centre of Geographic Studies, Institute of Geography and Spatial Planning of the University of Lisbon, 1600 Lisboa, Portugal

 Competing visions? Simulating alternative coastal futures using a GISANN web application

In this paper, we demonstrate the use of scenario building in the context of contested land use visions.
We examine a small coastal community located 20 kms south of Lisbon. In Almada e Trafaria/Costa da
Caparica, competing stakeholders such as central government, local government, environmental NGO's
and private companies each have competing development visions for the area. These include the
development of recreation and leisure facilities, a container terminal and the re-naturalization of unused
land. We illustrate the added value of the GIS-ANN tool in steering negotiations between these different
visions and the potential of a scenario building web application as a tool for problem solving.

The emergence of user-created GIS-based web content in Planning has transformed passive users and
consumers of geospatial information into active contributors to the development of spatial visions of the
future. It allows stakeholders to gauge alternative future land uses thus making planning and decisionmaking
processes potentially more transparent and democratic. In this paper, we detail a new method
that enhances GIS-web-based public participation. We build on a combination of GIS basic capabilities
and the data mining methods of Artificial Neural Networks (ANN), namely Multilayer Perceptron (MLP)
packaged in a friendly (GUI) user interface that runs on the Google Earth platform. Users will be able to
articulate different spatial development scenarios for a specific area, to conduct sensitivity analyses for various competing scenarios and to explore causal connections between them

To implement a GIS-ANN-MLP model that runs through a Web application we opted for a free open source neural network library, named Fast Artificial Neural Network (FANN). Before implementation, FANN was tested through the FANNTool GUI This enables us to prepare the data in FANN library standard, design, train, test and run the ANN. This enables us to prepare the data in FANN library standard, design, train, test and run the ANN.