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Stimulus reconstruction from a Hodgkin-Huxley neural response: A numerical solution (2010)
Presentation / Conference Contribution
Sarangdhar, M., & Kambhampati, C. (2010, June). Stimulus reconstruction from a Hodgkin-Huxley neural response: A numerical solution. Presented at World Congress on Engineering 2010

Neural responses are the fundamental expressions of any neural activity. Information carried by a neural response is determined by the nature of a neural activity. In majority of cases the underlying stimulus that triggers it remains largely... Read More about Stimulus reconstruction from a Hodgkin-Huxley neural response: A numerical solution.

Spiking neurons and synaptic stimuli: Neural response comparison using coincidence-factor (2009)
Book Chapter
Sarangdhar, M., & Kambhampati, C. (2009). Spiking neurons and synaptic stimuli: Neural response comparison using coincidence-factor. In S.-I. Ao, & L. Gelman (Eds.), Lecture Notes in Electrical Engineering; Advances in Electrical Engineering and Computational Science (681-692). Springer Verlag. https://doi.org/10.1007/978-90-481-2311-7_58

In this chapter, neural responses are generated by changing the Inter-Spike-Interval (ISI) of the stimulus. These responses are subsequently compared and a coincidence factor is obtained. Coincidence-factor, a measure of similarity, is expected to ge... Read More about Spiking neurons and synaptic stimuli: Neural response comparison using coincidence-factor.

Predicting cardiovascular risks using pattern recognition and data mining. (2009)
Thesis
Nguyen, T. T. T. (2009). Predicting cardiovascular risks using pattern recognition and data mining. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4209582

This thesis presents the use of pattern recognition and data mining techniques into risk prediction models in the clinical domain of cardiovascular medicine. The data is modelled and classified by using a number of alternative pattern recognition and... Read More about Predicting cardiovascular risks using pattern recognition and data mining..

Quantum recurrent neural networks for filtering (2009)
Thesis
Ahamed, W. U. (2009). Quantum recurrent neural networks for filtering. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4209270

The essence of stochastic filtering is to compute the time-varying probability densityfunction (pdf) for the measurements of the observed system. In this thesis, a filter isdesigned based on the principles of quantum mechanics where the schrodinger w... Read More about Quantum recurrent neural networks for filtering.

Robust FDI for FTC coordination in a distributed network system (2008)
Journal Article
Klinkhieo, S., Patton, R. J., & Kambhampati, C. (2008). Robust FDI for FTC coordination in a distributed network system. IFAC Proceedings Volumes/ International Federation of Automatic Control, 41(2), 13551-13556. https://doi.org/10.3182/20080706-5-KR-1001.0468

This paper focuses on the development of a suitable Fault Detection and Isolation (FDI) strategy for application to a system of inter-connected and distributed systems, as a basis for a fault-tolerant Network Control System (NCS) problem. The work fo... Read More about Robust FDI for FTC coordination in a distributed network system.

Spiking neurons: Is coincidence-factor enough for comparing responses with fluctuating membrane voltage? (2008)
Presentation / Conference Contribution
Sarangdhar, M., & Kambhampati, C. Spiking neurons: Is coincidence-factor enough for comparing responses with fluctuating membrane voltage?

Similarity between two spike trains is generally estimated using a ‘coincidence factor’. This factor relies on counting coincidences of firing-times for spikes in a given time window. However, in cases where there are significant fluctuations in memb... Read More about Spiking neurons: Is coincidence-factor enough for comparing responses with fluctuating membrane voltage?.

Spiking neurons and synaptic stimuli : determining the fidelity of coincidence-factor in neural response comparison (2008)
Journal Article
Kambhampati, C., & Sarangdhar, M. (2008). Spiking neurons and synaptic stimuli : determining the fidelity of coincidence-factor in neural response comparison. Engineering Letters International Association of Engineers, 16(4), 512-517

Similarity between two spike trains is generally estimated using a ‘coincidence factor’. This factor relies on counting coincidences of firing-times for spikes in a given time window. However, in cases where there are significant fluctuations in memb... Read More about Spiking neurons and synaptic stimuli : determining the fidelity of coincidence-factor in neural response comparison.

Stable quantum filters with scattering phenomena (2008)
Journal Article
Ahamed, W. U., & Kambhampati, C. (2008). Stable quantum filters with scattering phenomena. International Journal of Automation and Computing, 5(2), 132-137. https://doi.org/10.1007/s11633-008-0132-x

Quantum neural network filters for signal processing have received a lot of interest in the recent past. The implementations of these filters had a number of design parameters that led to numerical inefficiencies. At the same time the solution proced... Read More about Stable quantum filters with scattering phenomena.

Autonomous clustering using rough set theory (2008)
Journal Article
Bean, C., & Kambhampati, C. (2008). Autonomous clustering using rough set theory. International Journal of Automation and Computing, 5(1), 90-102. https://doi.org/10.1007/s11633-008-0090-3

This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory (RST). The proposed algorithm is unified in its approach to clustering and makes use of both local and g... Read More about Autonomous clustering using rough set theory.

A generic strategy for fault-tolerance in control systems distributed over a network (2007)
Journal Article
Patton, R. J., Kambhampati, C., Casavola, A., Zhang, P., Ding, S., & Sauter, D. (2007). A generic strategy for fault-tolerance in control systems distributed over a network. European journal of control / EUCA, European Control Association, 13(2-3), 280-296. https://doi.org/10.3166/ejc.13.280-296

This paper provides a tutorial overview, of a number of aspects and approaches to Control over the Network for Network Control Systems (NCS) that are likely to lead to good fault-tolerant control properties, subject to network faults. In order to ana... Read More about A generic strategy for fault-tolerance in control systems distributed over a network.

An interaction predictive approach to fault-tolerant control in network control systems (2007)
Journal Article
Kambhampati, C., Perkgoz, C., Patton, R. J., & Ahamed, W. (2007). An interaction predictive approach to fault-tolerant control in network control systems. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 221(6), 885-894. https://doi.org/10.1243/09596518jsce377

This paper illustrates some of the capabilities of previously proposed network control system (NCS) architectures to carry on functioning in the event of faults, without recourse to system reconfiguration. The principle of interaction prediction is u... Read More about An interaction predictive approach to fault-tolerant control in network control systems.

Neural observer by coordinate transformation (2005)
Journal Article
Delgado, A., Hou, M., & Kambhampati, C. (2005). Neural observer by coordinate transformation. IEE Proceedings Control Theory and Applications, 152(6), 698-706. https://doi.org/10.1049/ip-cta%3A20045069

Nonlinear control affine systems with maximum relative degree and a class of nonlinear differential equations can be transformed into a state representation known as the normal form. Based on the normal form an observer is designed using neural netwo... Read More about Neural observer by coordinate transformation.

Artificial intelligence in medicine (2004)
Journal Article
Ramesh, A., Kambhampati, C., Monson, J., & Drew, P. (2004). Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England, 86(5), 334-338. https://doi.org/10.1308/147870804290

INTRODUCTION Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in ma... Read More about Artificial intelligence in medicine.

A stable one-step-ahead predictive control of non-linear systems (2000)
Journal Article
Kambhampati, C., Mason, J. D., & Warwick, K. (2000). A stable one-step-ahead predictive control of non-linear systems. Automatica : the journal of IFAC, the International Federation of Automatic Control, 36(4), 485-495. https://doi.org/10.1016/s0005-1098%2899%2900173-9

In this paper stability of one-step ahead predictive controllers based on non-linear models is established. It is shown that, under conditions which can be fulfilled by most industrial plants, the closed-loop system is robustly stable in the presence... Read More about A stable one-step-ahead predictive control of non-linear systems.

Trained Hopfield neural networks need not be black-boxes (1999)
Presentation / Conference Contribution
Craddock, R., & Kambhampati, C. (1999, June). Trained Hopfield neural networks need not be black-boxes. Presented at Proceedings of the 1999 American Control Conference, San Diego, CA, USA

Stable linearization using multilayer neural networks (1996)
Presentation / Conference Contribution
Delgado, A., Kambhampati, C., & Warwick, K. (1996, September). Stable linearization using multilayer neural networks. Presented at UKACC International Conference on Control. Control '96, Exeter, UK

The main limitation of linearization theory that prevents its application in practical problems is the need for an exact knowledge of the plant. This requirement is eliminated and it is shown that a multilayer network can synthesise the state feedbac... Read More about Stable linearization using multilayer neural networks.