His scientific interests lie mostly in Nonlinear system, Artificial neural network, Control theory, Mathematical optimization and Control engineering. He combines subjects such as Artificial intelligence and Radial basis function with his study of Nonlinear system. He has researched Artificial neural network in several fields, including Jacobian matrix and determinant and Feed forward.
His Control theory, Multivariable calculus, Lyapunov function and Open-loop controller study in the realm of Control theory connects with subjects such as Power station. His Mathematical optimization research is multidisciplinary, incorporating elements of Economic dispatch and Benchmark. His work on PID controller and Autopilot as part of his general Control engineering study is frequently connected to Obstacle and Scheme, thereby bridging the divide between different branches of science.
George W. Irwin focuses on Control theory, Artificial neural network, Nonlinear system, Artificial intelligence and Control engineering. Control theory is closely attributed to Model predictive control in his research. His Artificial neural network research incorporates elements of Algorithm, Broyden–Fletcher–Goldfarb–Shanno algorithm and Process control.
Computation and Fuzzy logic is closely connected to Mathematical optimization in his research, which is encompassed under the umbrella topic of Nonlinear system. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Data mining and Pattern recognition. In his research on the topic of Control engineering, Wireless network is strongly related with Control system.
George W. Irwin spends much of his time researching Mathematical optimization, Artificial intelligence, Nonlinear system, Control engineering and Algorithm. His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition. The Nonlinear system study combines topics in areas such as Die, Point and Process engineering.
His Control engineering research incorporates themes from Control system, Control theory and Waypoint. His study in Algorithm is interdisciplinary in nature, drawing from both Resource and Radial basis function. His work deals with themes such as Neuro-fuzzy and Two stage algorithm, which intersect with Artificial neural network.
His primary areas of investigation include Mathematical optimization, Control engineering, Artificial intelligence, Artificial neural network and Nonlinear system. His work on Harmony search as part of general Mathematical optimization study is frequently linked to Scalability, therefore connecting diverse disciplines of science. His Control engineering study combines topics from a wide range of disciplines, such as Control theory and Waypoint.
His Control theory research includes elements of Control and Remotely operated underwater vehicle. His study in the field of Feedforward neural network is also linked to topics like Kernel principal component analysis. The study incorporates disciplines such as Sparse PCA, Principal component analysis, Pattern recognition, Point and Process engineering in addition to Nonlinear system.
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A Review on Improving the Autonomy of Unmanned Surface Vehicles through Intelligent Collision Avoidance Manoeuvres
Sable Campbell;Wasif Naeem;George Irwin.
Annual Reviews in Control (2012)
Process monitoring approach using fast moving window PCA
Xun Wang;Uwe Kruger;George W. Irwin.
Industrial & Engineering Chemistry Research (2005)
Multiple model bootstrap filter for maneuvering target tracking
S. McGinnity;G.W. Irwin.
IEEE Transactions on Aerospace and Electronic Systems (2000)
A fast nonlinear model identification method
Kang Li;Jian-Xun Peng;G.W. Irwin.
IEEE Transactions on Automatic Control (2005)
Model selection approaches for non-linear system identification: a review
X. Hong;R. J. Mitchell;S. Chen;C. J. Harris.
International Journal of Systems Science (2008)
Neural network applications in control
G. W. Irwin;Kevin Warwick;K. J. Hunt.
COLREGs-based collision avoidance strategies for unmanned surface vehicles
Wasif Naeem;George W. Irwin;Aolei Yang.
international conference on intelligent computing for sustainable energy and environment (2012)
A hybrid linear/nonlinear training algorithm for feedforward neural networks
S. McLoone;M.D. Brown;G. Irwin;A. Lightbody.
IEEE Transactions on Neural Networks (1998)
A neural network regulator for turbogenerators
Q.H. Wu;B.W. Hogg;G.W. Irwin.
IEEE Transactions on Neural Networks (1992)
Direct neural model reference adaptive control
G. Lightbody;G.W. Irwin.
IEE Proceedings - Control Theory and Applications (1995)
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