People

Dr Michael Fairbank

Lecturer
School of Computer Science and Electronic Engineering (CSEE)
Dr Michael Fairbank
  • Email

  • Telephone

    +44 (0) 1206 872341

  • Location

    1NW.3.23, Colchester Campus

  • Academic support hours

    9am-5pm

Profile

Qualifications

  • BSc Mathematical Physics (Nottingham University, 1994)

  • MScKnowledge Based Systems(Edinburgh University, 1995)

  • PhD Computer Science (City University London, 2014)

Research and professional activities

Research interests

Neural Networks

Open to supervise

Adaptive Dynamic Programming + Reinforcement Learning

Open to supervise

Optimisation

Open to supervise

Control Theory

Open to supervise

Financial Forecasting

Open to supervise

AI in games

Open to supervise

Current research

I have been trying to apply neural networks to control problems. In this problem the spacecraft uses two simple range-finding scanners to look around with and locate the tunnel. A recurrent neural network controls everything here - scanning strategy, memories of scan results, physical control of the spacecraft.

Teaching and supervision

Current teaching responsibilities

  • Team Project Challenge (CS) (CE291)

  • Team Project Challenge (CSE) (CE292)

  • Team Project Challenge (EE) (CE293)

  • Team Project Challenge (WBL) (CE299)

  • Physics-Based Games (CE812)

Previous supervision

Piers Williams
Piers Williams
Thesis title: Artificial Intelligence in Co-Operative Games with Partial Observability
Degree subject: Intelligent Games and Game Intelligence
Degree type: Doctor of Philosophy
Awarded date: 14/2/2019

Publications

Journal articles (9)

Li, S., Won, H., Fu, X., Fairbank, M., Wunsch, DC. and Alonso, E., (2019). Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results. IEEE Transactions on Cybernetics, 1-13

Alonso, E., Fairbank, M. and Mondragon, E., (2015). Back to optimality: a formal framework to express the dynamics of learning optimal behavior. Adaptive Behavior. 23 (4), 206-215

Fu, X., Li, S., Fairbank, M., Wunsch, DC. and Alonso, E., (2015). Training Recurrent Neural Networks With the Levenberg?Marquardt Algorithm for Optimal Control of a Grid-Connected Converter. IEEE Transactions on Neural Networks and Learning Systems. 26 (9), 1900-1912

Li, S., Fairbank, M., Johnson, C., Wunsch, DC., Alonso, E. and Proao, JL., (2014). Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter Under Disturbance, Dynamic and Power Converter Switching Conditions. IEEE Transactions on Neural Networks and Learning Systems. 25 (4), 738-750

Fairbank, M., Prokhorov, D. and Alonso, E., (2014). Clipping in Neurocontrol by Adaptive Dynamic Programming. IEEE Transactions on Neural Networks and Learning Systems. 25 (10), 1909-1920

Fairbank, M., Li, S., Fu, X., Alonso, E. and Wunsch, D., (2014). An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances. Neural Networks. 49, 74-86

Fairbank, M., Alonso, E. and Prokhorov, D., (2013). An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time. IEEE Transactions on Neural Networks and Learning Systems. 24 (12), 2088-2100

Fairbank, M., Alonso, E. and Prokhorov, D., (2012). Simple and Fast Calculation of the Second-Order Gradients for Globalized Dual Heuristic Dynamic Programming in Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 23 (10), 1671-1676

Fairbank, M. and Alonso, E., (2012). Efficient Calculation of the Gauss-Newton Approximation of the Hessian Matrix in Neural Networks. Neural Computation. 24 (3), 607-610

Book chapters (1)

Fairbank, M., Prokhorov, D. and Alonso, E., (2013). Approximating Optimal Control with Value Gradient Learning. In: Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. John Wiley & Sons, Inc.. 142- 161. 9781118104200

Conferences (15)

Fairbank, MH. and Tuson, A., A Curvature Primal Sketch Neural Network Recognition System.

Samothrakis, S., Vodopivec, T., Fairbank, M. and Fasli, M., (2017). Convolutional-Match Networks for Question Answering

Doering, J., Fairbank, M. and Markose, S., (2017). Convolutional neural networks applied to high-frequency market microstructure forecasting

Fairbank, MH., Volkovas, R. and Perez-Liebana, D., (2017). Diversity maintenance using a population of repelling random-mutation hill climbers

Samothrakis, S., Vodopivec, T., Fasli, M. and Fairbank, M., (2016). Match memory recurrent networks

Li, S., Fu, X., Alonso, E., Fairbank, M. and Wunsch, DC., (2016). Neural-network based vector control of VSCHVDC transmission systems

Li, S., Alonso, E., Fu, X., Fairbank, M., Jaithwa, I. and Wunsch, DC., (2015). Hardware Validation for Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks

Li, S., Fu, X., Jaithwa, I., Alonso, E., Fairbank, M. and Wunsch, DC., (2015). Control of three-phase grid-connected microgrids using artificial neural networks

Li, S., Fairbank, M., Fu, X., Wunsch, DC. and Alonso, E., (2013). Nested-loop neural network vector control of permanent magnet synchronous motors

Alonso, E. and Fairbank, M., (2013). Emergent and Adaptive Systems of Systems

Fairbank, MH. and Alonso, E., (2012). Value-gradient learning

Alonso, E., Fairbank, M. and Mondragón, E., (2012). Conditioning for least action

Li, S., Wunsch, DC., Fairbank, M. and Alonso, E., (2012). Vector control of a grid-connected rectifier/inverter using an artificial neural network

Fairbank, M. and Alonso, E., (2012). The divergence of reinforcement learning algorithms with value-iteration and function approximation

Fairbank, M. and Alonso, E., (2012). A comparison of learning speed and ability to cope without exploration between DHP and TD(0)

Grants and funding

2019

Spark EV KTP application

Innovate UK (formerly Technology Strategy Board)

2017

67% The embedding of machine learning and principles of AI technology to deploy a data-driven growth strategy in a sector leading business with a vision to disrupt the insurance industry.

Technology STrategy Board

33% The embedding of machine learning and principles of AI technology to deploy a data-driven growth strategy in a sector leading business with a vision to disrupt the insurance industry.

Hood Group Ltd

67% - Embedding intelligent systems within an UAV thermographic solar energy inspection platform to reduce UAV weight, performance and flight time

Technology STrategy Board

33% - Embedding intelligent systems within an UAV thermographic solar energy inspection platform to reduce UAV weight, performance and flight time

Above Surveying Ltd

Embedding a Machine Learning capability into the Hood Group Ltd platform.

Innovate UK (formerly Technology Strategy Board)

Embedding a Machine Learning capability into the Hood Group Ltd platform.

Hoodgroup Ltd

Improved in-pen access free pig weighing

University of Essex

Improved real time detection of wind-turbine failures - Dicam Technologies

University of Essex

Create new methods of capturing insight from current and future Preqin datasets by embedding AI and Machine Learning techniques across the unique Preqin investor platform.

Prequin

Create new methods of capturing insight from current and future Preqin datasets by embedding AI and Machine Learning techniques across the unique Preqin investor platform.

Prequin

2016

Machine Learning for EV Range Calculation

Cab4one Limited

Contact

m.fairbank@essex.ac.uk
+44 (0) 1206 872341

Location:

1NW.3.23, Colchester Campus

Academic support hours:

9am-5pm