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Thamarai, P.
- Analysis of Automatic Aircraft Landing using Neural Networks and Signal Processor
Abstract Views :516 |
PDF Views:94
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Bharath University, Selaiyur, Chennai - 600 073, IN
1 Department of Electronics and Communication Engineering, Bharath University, Selaiyur, Chennai - 600 073, IN
Source
Indian Journal of Innovations and Developments, Vol 2, No 2 (2013), Pagination: 824-827Abstract
This paper presents an adaptive neural network, designed to improve the performance of conventional automatic landing systems (ALS). Real-time learning was applied to train the neural network using the gradient-descent of an error function to adaptively update weights. Adaptive learning rates were obtained through the analysis of Lyapunov stability to guarantee the convergence of learning. In addition, we applied a DSP controller using the VisSim/TI C2000 Rapid Prototyper to develop an embedded control system and establish on-line real-time control. Simulations show that the proposed control scheme has superior performance to conventional ALS under conditions of wind disturbance of up to 75 ft/s. Automatic aircraft landing operation, depends upon the proper functioning of various networks related to it. The safe landing of aircraft is very much important. This project deals with the detection of various obstructions related to safe landing. This is achieved by using automatic landing system through neural network, in corporation with embedded system. The sensor is used to sense the real altitude ,altitude rate and command signal. Any one of these signal is fed to the reference trajectory and other signal is fed to ARAN controller from the there the signal is fed to error comparator and other signal for error comparator comes from the reference trajectory, both the signals are compared and the difference in signal is pitch command signal that signal along with disturbance signal is given to aircraft model. If there is any changes found in aircraft model again the signal is fed to real altitude block for further comparison. The ARAN controller is used for varying the weights.Keywords
Neural Networks, Automatic Landing System, Resource Allocating Network, Instrument Landing System- Emerging Learning System
Abstract Views :315 |
PDF Views:67
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Bharath University, Selaiyur, Chennai - 600 073, IN
2 Department of Electronics and Communication Engineering, Bharath University, Selaiyur, Chennai - 600 073, Tamilnadu, IN
3 Department of Electronics and Communication Engineering, Bharath University, Selaiyur, Chennai - 600 073, Tamilnadu, IN
1 Department of Electronics and Communication Engineering, Bharath University, Selaiyur, Chennai - 600 073, IN
2 Department of Electronics and Communication Engineering, Bharath University, Selaiyur, Chennai - 600 073, Tamilnadu, IN
3 Department of Electronics and Communication Engineering, Bharath University, Selaiyur, Chennai - 600 073, Tamilnadu, IN
Source
Indian Journal of Innovations and Developments, Vol 1, No 9 (2012), Pagination: 706-709Abstract
In today's rapidly changing e-Learning environment, we do not have time to endure months of implementation to complete our mission-critical training initiatives. The application of information and communications technology to education and training, both in the corporate and public sectors is now big business on a global scale. It is however, an industry which is young and relatively immature. The rapid emergence of new technologies outpaces the ability of learning communities to apply the technological infrastructure in any systemic or sustainable fashion. E-learning communities are still grappling with significant pedagogical, cultural and business issues which are often under-estimated by the technologists. M-learning has now emerged as a new wave of development, based on the use of mobile devices combined with wireless infrastructure, and much of the current literature on M-learning reveals all the strengths and weaknesses associated with the more mature E-learning communities. There are, of course, close links between E-learning and M-learning and it can be argued that they represent a continuum based on the deployment of ever-more sophisticated technologies. For innovation to have an effect, however, there must be distribution channels that provide access to end-users. This is where connectivity comes in and why the Internet is different. In other media, such as print, radio, cinema, music and television, the companies who own the distribution channels (publishers, radio and television networks, film studios, and the recording industry) control the content.References
- Education Technology by Usha rao, Himalaya publications.
- Modern Distance Education by Pradeep kumar Johri.
- New dimension of Extension Education by S.Venkatiah
- Contemporary Education by S.Venkataiah
- Anmol publications pvt Ltd, New Delhi-110 002.
- www.skillsoft.com
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- www.energingtech.ippoolvox.com.