The Experts below are selected from a list of 31509 Experts worldwide ranked by ideXlab platform
Michael V. Mannino - One of the best experts on this subject based on the ideXlab platform.
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Sequential decision models for expert System Optimization
IEEE Transactions on Knowledge and Data Engineering, 1997Co-Authors: Vijay S. Mookerjee, Michael V. ManninoAbstract:Presents information pertaining to expert System Optimization with focus on sequential decision models which are an important element to the System when the cost or time to collect inputs is significant and inputs are known until the System operates. Information on the different type of professions that use expert Systems; Techniques demonstrate how search methods and heuristics are influenced by economic objective, knowledge source and optimized form.
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Sequential decision models for expert System Optimization
IEEE Transactions on Knowledge and Data Engineering, 1997Co-Authors: Vijay S. Mookerjee, Michael V. ManninoAbstract:Sequential decision models are an important element of expert System Optimization when the cost or time to collect inputs is significant and inputs are not known until the System operates. Many expert Systems in business, engineering, and medicine have benefited from sequential decision technology. In this survey, we unify the disparate literature on sequential decision models to improve comprehensibility and accessibility. We separate formulation of sequential decision models from solution techniques. For model formulation, we classify sequential decision models by objective (cost minimization versus value maximization) knowledge source (rules, data, belief network, etc.), and optimized form (decision tree, path, input order). A wide variety of sequential decision models are discussed in this taxonomy. For solution techniques, we demonstrate how search methods and heuristics are influenced by economic objective, knowledge source, and optimized form. We discuss open research problems to stimulate additional research and development.
Vijay S. Mookerjee - One of the best experts on this subject based on the ideXlab platform.
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Sequential decision models for expert System Optimization
IEEE Transactions on Knowledge and Data Engineering, 1997Co-Authors: Vijay S. Mookerjee, Michael V. ManninoAbstract:Presents information pertaining to expert System Optimization with focus on sequential decision models which are an important element to the System when the cost or time to collect inputs is significant and inputs are known until the System operates. Information on the different type of professions that use expert Systems; Techniques demonstrate how search methods and heuristics are influenced by economic objective, knowledge source and optimized form.
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Sequential decision models for expert System Optimization
IEEE Transactions on Knowledge and Data Engineering, 1997Co-Authors: Vijay S. Mookerjee, Michael V. ManninoAbstract:Sequential decision models are an important element of expert System Optimization when the cost or time to collect inputs is significant and inputs are not known until the System operates. Many expert Systems in business, engineering, and medicine have benefited from sequential decision technology. In this survey, we unify the disparate literature on sequential decision models to improve comprehensibility and accessibility. We separate formulation of sequential decision models from solution techniques. For model formulation, we classify sequential decision models by objective (cost minimization versus value maximization) knowledge source (rules, data, belief network, etc.), and optimized form (decision tree, path, input order). A wide variety of sequential decision models are discussed in this taxonomy. For solution techniques, we demonstrate how search methods and heuristics are influenced by economic objective, knowledge source, and optimized form. We discuss open research problems to stimulate additional research and development.
Chunchun Tian - One of the best experts on this subject based on the ideXlab platform.
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System Optimization-Based Model and Algorithm of Network Flow Assignment of Transport Hub
2009 Second International Conference on Intelligent Computation Technology and Automation, 2009Co-Authors: Xuemei Zhou, Xianzhun Zhang, Chunchun TianAbstract:Transport hub is the critical node to achieve seamless transfer among different modes of transport, determining the efficiency of the city's passenger transport System. In review of the lack of quantitative basis for transport hub design and transport coordination problems and combined with characteristics of transport hub, the System Optimization-based model of network flow assignment of transport hub is set up from the cost perspective to realize System Optimization. Then the model is applied to an example, and is solved by genetic algorithm, and the results prove that there are important theoretical meaning and practical value of the study to provide quantitative basis for optimized design of transport hub.
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System Optimization-Based Model and Algorithm of Network Flow Assignment of Transport Hub
2009 Second International Conference on Intelligent Computation Technology and Automation, 2009Co-Authors: Xuemei Zhou, Xianzhun Zhang, Chunchun TianAbstract:In review of the lack of quantitative basis for transport hub design and transport coordination problems and combined with characteristics of transport hub, the System Optimization-based model of network flow assignment of transport hub is set up from the cost perspective to realize System Optimization. Then the model is applied to an example, and is solved by genetic algorithm, proving that there are important theoretical meaning and practical value of the study to provide quantitative basis for optimized design of transport hub.
Sanjay Mehrotra - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Robust Mathematical Programming Model for Power System Optimization
IEEE Transactions on Power Systems, 2016Co-Authors: Haoyong Chen, Sanjay MehrotraAbstract:This letter presents a stochastic robust framework for two-stage power System Optimization problems with uncertainty. The model optimizes the probabilistic expectation of different worst-case scenarios with different uncertainty sets. A case study of unit commitment shows the effectiveness of the proposed model and algorithms.
Junita Mohamad-saleh - One of the best experts on this subject based on the ideXlab platform.
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Hybrid bio-Inspired computational intelligence techniques for solving power System Optimization problems: A comprehensive survey
Applied Soft Computing Journal, 2018Co-Authors: Imran Rahman, Junita Mohamad-salehAbstract:Optimization problems of modern day power System are very challenging to resolve because of its design complexity, wide geographical dispersion and influence from many unpredictable factors. For that reason, it is essential to apply most effective Optimization techniques by taking full benefits of simplified formulation and execution of a particular problem. This study presents a summary of significant hybrid bio-inspired computational intelligence (CI) techniques utilized for power System Optimization. Authors have reviewed an extensive range of hybrid CI techniques and examined the motivations behind their improvements. Various applications of hybrid bio-inspired CI algorithms have been highlighted in this paper. In addition, few drawbacks regarding the hybrid CI algorithms are explained. Current trends in CI techniques from the past researches have also been discussed in the domain of power System Optimization. Lastly, some future research directions are suggested for further advancement of hybrid techniques.
