| PANFIS: A novel incremental learning machine M Pratama, SG Anavatti, PP Angelov, E Lughofer IEEE Transactions on Neural Networks and Learning Systems 25 (1), 55-68, 2013 | 281 | 2013 |
| GENEFIS: Toward an effective localist network M Pratama, SG Anavatti, E Lughofer IEEE Transactions on Fuzzy Systems 22 (3), 547-562, 2013 | 189 | 2013 |
| State-of-the-art intelligent flight control systems in unmanned aerial vehicles F Santoso, MA Garratt, SG Anavatti IEEE Transactions on Automation Science and Engineering 15 (2), 613-627, 2017 | 154 | 2017 |
| Multimodal fusion for objective assessment of cognitive workload: A review E Debie, RF Rojas, J Fidock, M Barlow, K Kasmarik, S Anavatti, M Garratt, ... IEEE transactions on cybernetics 51 (3), 1542-1555, 2019 | 143 | 2019 |
| pClass: an effective classifier for streaming examples M Pratama, SG Anavatti, M Joo, ED Lughofer IEEE Transactions on Fuzzy Systems 23 (2), 369-386, 2014 | 132 | 2014 |
| An incremental meta-cognitive-based scaffolding fuzzy neural network M Pratama, J Lu, S Anavatti, E Lughofer, CP Lim Neurocomputing 171, 89-105, 2016 | 109 | 2016 |
| Visual–inertial navigation systems for aerial robotics: Sensor fusion and technology F Santoso, MA Garratt, SG Anavatti IEEE Transactions on Automation Science and Engineering 14 (1), 260-275, 2016 | 105 | 2016 |
| Design and construction of an autonomous underwater vehicle K Alam, T Ray, SG Anavatti Neurocomputing 142, 16-29, 2014 | 103 | 2014 |
| Scaffolding type-2 classifier for incremental learning under concept drifts M Pratama, J Lu, E Lughofer, G Zhang, S Anavatti Neurocomputing 191, 304-329, 2016 | 100 | 2016 |
| Recurrent classifier based on an incremental metacognitive-based scaffolding algorithm M Pratama, SG Anavatti, J Lu IEEE Transactions on Fuzzy Systems 23 (6), 2048-2066, 2015 | 99 | 2015 |
| Towards real-time monocular depth estimation for robotics: A survey X Dong, MA Garratt, SG Anavatti, HA Abbass IEEE Transactions on Intelligent Transportation Systems 23 (10), 16940-16961, 2022 | 96 | 2022 |
| Electroencephalographic workload indicators during teleoperation of an unmanned aerial vehicle shepherding a swarm of unmanned ground vehicles in contested environments R Fernandez Rojas, E Debie, J Fidock, M Barlow, K Kasmarik, S Anavatti, ... Frontiers in neuroscience 14, 40, 2020 | 93 | 2020 |
| An incremental type-2 meta-cognitive extreme learning machine M Pratama, G Zhang, MJ Er, S Anavatti IEEE transactions on cybernetics 47 (2), 339-353, 2016 | 86 | 2016 |
| Observer-based adaptive guidance law considering target uncertainties and control loop dynamics D Chwa, JY Choi, SG Anavatti IEEE Transactions on Control Systems Technology 14 (1), 112-123, 2005 | 77 | 2005 |
| A brief taxonomy of autonomous underwater vehicle design literature K Alam, T Ray, SG Anavatti Ocean Engineering 88, 627-630, 2014 | 72 | 2014 |
| Robust hybrid nonlinear control systems for the dynamics of a quadcopter drone F Santoso, MA Garratt, SG Anavatti, I Petersen IEEE Transactions on Systems, Man, and Cybernetics: Systems 50 (8), 3059-3071, 2018 | 65 | 2018 |
| Palm: An incremental construction of hyperplanes for data stream regression MM Ferdaus, M Pratama, SG Anavatti, MA Garratt IEEE Transactions on Fuzzy Systems 27 (11), 2115-2129, 2019 | 60 | 2019 |
| Modified continuous ant colony optimisation for multiple unmanned ground vehicle path planning J Liu, S Anavatti, M Garratt, HA Abbass Expert Systems with Applications 196, 116605, 2022 | 59 | 2022 |
| Evolving type-2 web news mining C Za’in, M Pratama, E Lughofer, SG Anavatti Applied Soft Computing 54, 200-220, 2017 | 59 | 2017 |
| Fuzzy logic implementation of proportional navigation guidance V Rajasekhar, AG Sreenatha Acta Astronautica 46 (1), 17-24, 2000 | 59 | 2000 |
M.GarrattProfessor, University of New South WalesVerified email at adfa.edu.au
