K. Ponnambalam

K. Ponnambalam, L. Logan, S. Ali, 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

An application of Karmarkar's interior-point linear programming algorithm for multi-reservoir operations optimization

Kumaraswamy Ponnambalam, A. Vannelli, T. E. Unny, 1989 .

Stochastic Environmental Modeling

W. F. Curi, K. Ponnambalam, E. McBean, 1998 .

A New Formulation for the Stochastic Control of Systems with Bounded State Variables: An Application to Great Lakes Levels Regulation

K. Ponnambalam, K. Ponnambalam, S. Fletcher, 1995 .

Storage-yield analysis of surface water reservoirs: the role of reliability constraints and operating policies

Kumaraswamy Ponnambalam, Hosein Alizadeh, S. Jamshid Mousavi, 2014, Stochastic Environmental Research and Risk Assessment.

Prediction of Strawberry Yield and Farm Price Utilizing Deep Learning

F. Karray, K. Ponnambalam, I. Okwuchi, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

A fast algorithm for power system optimization problems using an interior point method

V. Quintana, K. Ponnambalam, A. Vannelli, 1991, [Proceedings] Conference Papers 1991 Power Industry Computer Application Conference.

Long-term uncertainty evaluation of pool electricity markets

K. Ponnambalam, Juan Álvarez Lopez, 2010 .

Estimating Mean and Covariance of the Solution of a Linear System with Random Coefficients

K. Ponnambalam, D. Thangaraj, C. Thamayanthi, 1994 .

Spatial Dependence Modeling of Wind Resource under Uncertainty Using C-Vine Copulas and Its Impact on Solar-Wind Energy Co-Generation

K. Ponnambalam, S. Pagsuyoin, Apurva Narayan, 2017 .

Application of the distributed parameter filter to predict simulated tidal induced shallow water flow

K. Ponnambalam, T. Unny, K. Hipel, 1995 .

Aggregation–Decomposition-Based Multi-Agent Reinforcement Learning for Multi-Reservoir Operations Optimization

Kumaraswamy Ponnambalam, S. Jamshid Mousavi, Masoud Mahootchi, 2020, Water.

Machine Learning Tools for the Prediction of Fresh Produce Procurement Price

Kumaraswamy Ponnambalam, Fatemeh Jafari, Seyed Jamshid Mousavi, 2020, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

Generation expansion under risk using stochastic programming

K. Ponnambalam, V.H. Quintana, J. Alvarez, 2005, Proceedings of the 37th Annual North American Power Symposium, 2005..

Hydro energy management optimization in a deregulated electricity market

K. Ponnambalam, Joyce Li Zhang, K. Ponnambalam, 2006 .

Stochastic Centralized Dispatch Scheme for AC/DC Hybrid Smart Distribution Systems

Kumaraswamy Ponnambalam, Mostafa F. Shaaban, E. F. El-Saadany, 2016, IEEE Transactions on Sustainable Energy.

Comparison of several heuristic approaches to calibration of WATCLASS hydrologic model1 ☆

K. Ponnambalam, E. D. Soulis, M. Kamali, 2013 .

Computationally efficient calibration of WATCLASS Hydrologic models using surrogate optimization

M. Kamali, K. Ponnambalam, E. D. Soulis, 2007 .

Design flow robustness evaluation

Keith W. Hipel, Lloyd Logan, K. Ponnambalam, 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

A clustering method for large-scale group decision-making with multi-stage hesitant fuzzy linguistic terms

Kumaraswamy Ponnambalam, Shitao Zhang, Zhenzhen Ma, 2019, Inf. Fusion.

Comment on “Error analysis of conventional discrete and gradient dynamic programming” by P. K. Kitanidis and Efi Foufoula‐Georgiou

Barry J. Adams, Kumaraswamy Ponnambalam, K. Ponnambalam, 1988 .

Group Decision-Making with Linguistic Cognition from a Reliability Perspective

Kumaraswamy Ponnambalam, Shitao Zhang, Zhenzhen Ma, 2019, Cognitive Computation.

A maintainability model for industrial software systems using design level metrics

Kumaraswamy Ponnambalam, Kostas Kontogiannis, S. Muthanna, 2000, Proceedings Seventh Working Conference on Reverse Engineering.

Optimal power flow for converter-dominated AC/DC hybrid microgrids

Ehab F. El-Saadany, Kumaraswamy Ponnambalam, Abdelsalam A. Eajal, 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

Opposition-Based Reinforcement Learning in the Management of Water Resources

K. Ponnambalam, H.R. Tizhoosh, M. Mahootchi, 2007, 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.

Experiences with integrated irrigation system optimization analysis

Barry J. Adams, Kumaraswamy Ponnambalam, K. Ponnambalam, 1987 .

Adaptive forecast-based real-time optimal reservoir operations: application to Lake Urmia

Kumaraswamy Ponnambalam, S. Jamshid Mousavi, Keyhan Gavahi, 2019, Journal of Hydroinformatics.

Inferring operating rules for reservoir operations using fuzzy regression and ANFIS

Fakhri Karray, Kumaraswamy Ponnambalam, Seyed Jamshid Mousavi, 2007, Fuzzy Sets Syst..

Oppositional extension of reinforcement learning techniques

Kumaraswamy Ponnambalam, Hamid R. Tizhoosh, Masoud Mahootchi, 2014, Inf. Sci..

Artificial Intelligence Modeling of Financial Profit and Fraud

F. Karray, K. Ponnambalam, F. Karray, 2011 .

Optimal randomized ordering policies for a capacitated two-echelon distribution inventory system

Kumaraswamy Ponnambalam, Taher Ahmadi, Masoud Mahootchi, 2018, Comput. Ind. Eng..

Strategies for Improving the Capabilities of a Weak Main Manufacturer in Complex Products Systems

Kumaraswamy Ponnambalam, Jinhua Zhou, Jianjun Zhu, 2020, IEEE Access.

Probabilistic design of systems with general distributions of parameters

Kumaraswamy Ponnambalam, Jirí Vlach, Abbas Seifi, 2001, Int. J. Circuit Theory Appl..

The evolution of R&D collaboration in inter-organizational project networks: Effects of reference points for competitive preference

K. Ponnambalam, K. Hipel, D. Kilgour, 2021, Physica A: Statistical Mechanics and its Applications.

Automated epileptic seizure onset detection

Kumaraswamy Ponnambalam, Arvind Dorai, K. Ponnambalam, 2010, 2010 International Conference on Autonomous and Intelligent Systems, AIS 2010.

Enhancements to explicit stochastic reservoir operation optimization method

K. Ponnambalam, Ximing Cai, S. Mousavi, 2022, Advances in Water Resources.

Lifetimes of Used Nuclear Fuel Containers Affected by Sulphate-Reducing Bacteria Reactions inside the Canadian Deep Geological Repository

K. Ponnambalam, J. Garcia-Hernandez, M. Sivaraman, 2021, Applied Sciences.

Computational Methods in Stochastic River Waters Quality Modeling

K. Ponnambalam, P. Zielinski, 1994 .

Introductory probabilistic and stochastic analysis and design of circuits

K. Ponnambalam, W. F. Curi, K. Ponnambalam, 1993 .

A probabilistic design optimization for MEMS tunable capacitors

K. Ponnambalam, S. M. Hashemi, M. Shavezipur, 2008, Microelectron. J..

Ensemble-based machine learning approach for improved leak detection in water mains

Kumaraswamy Ponnambalam, S. Jamshid Mousavi, Keyhan Gavahi, 2021, Journal of Hydroinformatics.

Deep Learning Based Approach for Fresh Produce Market Price Prediction

Kumaraswamy Ponnambalam, Fakhri Karray, Lobna Nassar, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Deep Learning Ensemble Based Model for Time Series Forecasting Across Multiple Applications

Kumaraswamy Ponnambalam, Fakhri Karray, Lobna Nassar, 2020, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

HydrologicmodelCalibration usingFuzzyTSK surrogate model

K. Ponnambalam, E. D. Soulis, M. Kamali, 2005 .

A Unified Approach to the Power Flow Analysis of AC/DC Hybrid Microgrids

Kumaraswamy Ponnambalam, E. F. El-Saadany, A. A. Eajal, 2016, IEEE Transactions on Sustainable Energy.

CHNS Modeling for Study and Management of Human–Water Interactions at Multiple Scales

S. Jamshid Mousavi, Kumaraswamy Ponnambalam, K. Ponnambalam, 2020, Water.

Comparison of methods for battery capacity design in renewable energy systems for constant demand and uncertain supply

K. Ponnambalam, M. Mahootchi, Y. E. Saad, 2010, 2010 7th International Conference on the European Energy Market.

Joint Flood Risks in the Grand River Watershed

F. Karray, K. Ponnambalam, N. Agrawal, 2023, Sustainability.

Statistical design of integrated circuits using maximum likelihood estimation of the covariance matrix

Kumaraswamy Ponnambalam, Abbas Seifi, Jiri Vlach, 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

Robust Option through Binomial Tree Method

Kumaraswamy Ponnambalam, Payam Hanafizadeh, Amir Hossein Mortazavi Qahi, 2015, Int. J. Strateg. Decis. Sci..

Agent-Based Modeling to Simulate Real-World Prices: A Strawberry Market Study*

K. Ponnambalam, F. Fathallahi, Y. Huang, 2020, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

A Bootstrap Approach and Reliability of Time Varying Design

Kumaraswamy Ponnambalam, Lloyd Logan, Ross Zhou, 1999 .

Optimization of filter designs with dependent and asymmetrically distributed parameters

Kumaraswamy Ponnambalam, Jirí Vlach, Abbas Seifi, 2013, J. Frankl. Inst..

Comparison of risk-based optimization models for reservoir management

Kumaraswamy Ponnambalam, Hamid R. Tizhoosh, Masoud Mahootchi, 2010 .

Opposition Mining in Reservoir Management

Kumaraswamy Ponnambalam, Hamid R. Tizhoosh, Masoud Mahootchi, 2008, Oppositional Concepts in Computational Intelligence.

Framework for statistical design of a flip-flop

Kumaraswamy Ponnambalam, Mohab H. Anis, Minoo Mirsaeedi, 2009, 2009 16th IEEE International Conference on Electronics, Circuits and Systems - (ICECS 2009).

Predictive MPC-Based Operation of Urban Drainage Systems Using Input Data-Clustered Artificial Neural Networks Rainfall Forecasting Models

K. Ponnambalam, S. Mousavi, F. Jafari, 2023, Hydrology.