Randa Herzallah

According to our database1, Randa Herzallah authored at least 32 papers between 2002 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.



In proceedings 
PhD thesis 


On csauthors.net:


Decentralised Probabilistic Consensus Control for Stochastic Complex Dynamical Networks.
IEEE Control. Syst. Lett., 2021

PMAC: probabilistic multimodality adaptive control.
Int. J. Control, 2020

DOBC Based Fully Probability Design for Stochastic System With the Multiplicative Noise.
IEEE Access, 2020

Optimal Electricity Trading Strategy for a Household Microgrid.
Proceedings of the 16th IEEE International Conference on Control & Automation, 2020

Probabilistic Message Passing for Decentralized Control of Stochastic Complex Systems.
IEEE Access, 2019

Fully Probabilistic Design for Stochastic Discrete System with Multiplicative Noise.
Proceedings of the 15th IEEE International Conference on Control and Automation, 2019

A Fully Probabilistic Decentralised Control Design for Complex Stochastic Systems.
Proceedings of the 15th IEEE International Conference on Control and Automation, 2019

ECG-Derived Respiration Using a Real-Time QRS Detector Based on Empirical Mode Decomposition.
Proceedings of the 12th International Conference on Signal Processing and Communication Systems, 2018

Performance Prediction using Neural Network and Confidence Intervals: a Gas Turbine application.
Proceedings of the IEEE International Conference on Big Data, 2018

Scalable Harmonization of Complex Networks With Local Adaptive Controllers.
IEEE Trans. Syst. Man Cybern. Syst., 2017

Towards probabilistic synchronisation of local controllers.
Int. J. Syst. Sci., 2017

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise.
Neural Networks, 2015

Generalised Riccati solution and pinning control of complex stochastic networks.
Int. J. Model. Identif. Control., 2015

Driving licensing renewal policy using neural network-based probabilistic decision support system.
Int. J. Comput. Appl. Technol., 2015

Probabilistic DHP adaptive critic for nonlinear stochastic control systems.
Neural Networks, 2013

Probabilistic synchronisation of pinning control.
Int. J. Control, 2012

Fully Probabilistic Adaptive Control with Arbitrary Density Functions of Forward Models and Inverse Controllers.
Proceedings of the 7th IFAC Symposium on Robust Control Design, 2012

Fully probabilistic control design in an adaptive critic framework.
Neural Networks, 2011

Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty.
Int. J. Model. Identif. Control., 2011

Probabilistic dual heuristic programming-based adaptive critic.
Int. J. Syst. Sci., 2010

Estimation of Quantum Time Length for Round-robin Scheduling Algorithm using Neural Networks .
Proceedings of the ICFC-ICNC 2010, 2010

A novel cautious controller to uncertain models arising in stochastic control.
Int. J. Model. Identif. Control., 2009

Riccati Solution for Discrete Stochastic Systems with State and Control Dependent Noise.
Proceedings of the ICINCO 2009, 2009

A Bayesian Perspective on Stochastic Neurocontrol.
IEEE Trans. Neural Networks, 2008

Stochastic Control Strategies and Adaptive Critic Methods.
Proceedings of the ICINCO 2008, 2008

Distribution Modeling of Nonlinear Inverse Controllers Under a Bayesian Framework.
IEEE Trans. Neural Networks, 2007

Dual Version of Neural Adaptive controller for stochastic nonlinear Systems with Functional Uncertainty.
Control. Intell. Syst., 2007

Adaptive critic methods for stochastic systems with input-dependent noise.
Autom., 2007

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems.
Eng. Appl. Artif. Intell., 2004

Exploiting uncertainty in nonlinear stochastic control problem.
PhD thesis, 2003

Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals.
Neural Comput. Appl., 2003

A Novel Approach to Modelling and Exploiting Uncertainty in Stochastic Control Systems.
Proceedings of the Artificial Neural Networks, 2002