Comparative Analysis of Multiple Neural Networks for Online Identification of a UAV

Puttige, Vishwas; Anavatti, Sreenatha G.; Ray, Tapabrata

doi:10.1007/978-3-540-76928-6_14

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…For instance, it has been used for UAV visual-servo control [35], UAV path planning [36,37], UAV system identification [38,39] and many other UAV related work [40][41][42].…”

Section: Artifical Neural Networkmentioning

confidence: 99%

Unmanned Aerial Vehicles parameter estimation using Artificial Neural Networks and Iterative Bi-Section Shooting method

Hatamleh

Al-Shabi

Al-Ghasem

et al. 2015

Applied Soft Computing

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“…For instance, it has been used for UAV visual-servo control [35], UAV path planning [36,37], UAV system identification [38,39] and many other UAV related work [40][41][42].…”

Section: Artifical Neural Networkmentioning

confidence: 99%

Unmanned Aerial Vehicles parameter estimation using Artificial Neural Networks and Iterative Bi-Section Shooting method

Hatamleh

Al-Shabi

Al-Ghasem

et al. 2015

Applied Soft Computing

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“…ANN has no statistical assumption, therefore it can be used for a wide range of applications including classification, signal processing, and process control and regression problems [133,134]. Recent applications of ANN are conducted by Ray et al [135][136][137][138], in which the ability of ANN in capturing the dynamics of underwater vehicles is explored. Other applications of ANN by Ray et al [139,140] include solving process-design-optimization problems using an evolutionary optimization framework.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Modelling of manufacturing processes by a computational intelligence approach

Garg¹

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Modelling is a term widely used in System Identification (SI), which is referred to as the art and science of building mathematical models of systems using some measured data. The systems of interest in this thesis are additive manufacturing processes such as fused deposition modelling, machining processes such as turning, and finishing processes such as vibratory finishing. These processes comprise multiple input and output variables, making their operating mechanisms complex. In addition, it can be costly to obtain the process data and therefore there is a strong need for effective and efficient ways of modelling these systems. The models developed for a system can help to reveal hidden

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Fuzzy clustering based nonlinear system identification and controller development of Pixhawk based quadcopter

Ferdaus

Anavatti

Garratt

et al. 2017

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)

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Comparative Analysis of Multiple Neural Networks for Online Identification of a UAV

Cited by 4 publications

References 4 publications

Unmanned Aerial Vehicles parameter estimation using Artificial Neural Networks and Iterative Bi-Section Shooting method

Unmanned Aerial Vehicles parameter estimation using Artificial Neural Networks and Iterative Bi-Section Shooting method

Modelling of manufacturing processes by a computational intelligence approach

Fuzzy clustering based nonlinear system identification and controller development of Pixhawk based quadcopter

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