G
Gexiang Zhang
Researcher at Chengdu University of Information Technology
Publications - 213
Citations - 4704
Gexiang Zhang is an academic researcher from Chengdu University of Information Technology. The author has contributed to research in topics: Membrane computing & Evolutionary algorithm. The author has an hindex of 31, co-authored 182 publications receiving 3546 citations. Previous affiliations of Gexiang Zhang include Chengdu University of Technology & Northern General Hospital.
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An optimization spiking neural p system for approximately solving combinatorial optimization problems.
TL;DR: An extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking Neural P system, are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems.
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Quantum-inspired evolutionary algorithms: a survey and empirical study
TL;DR: A unified framework and a comprehensive survey of recent work in quantum-inspired evolutionary algorithms is provided and conclusions are drawn about some of the most promising future research developments in this rapidly growing field.
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Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems
TL;DR: The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods reported in the literature in terms of the correctness of diagnosis results.
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Short-Term State Forecasting-Aided Method for Detection of Smart Grid General False Data Injection Attacks
TL;DR: The approximate dc model is extended to a more general linear model that can handle both supervisory control and data acquisition and phasor measurement unit measurements and a general FDIA based on this model is derived and the error tolerance of such attacks is discussed.
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Power System Real-Time Monitoring by Using PMU-Based Robust State Estimation Method
Junbo Zhao,Gexiang Zhang,Kaushik Das,George N. Korres,Nikolaos M. Manousakis,Avinash Kumar Sinha,Zhengyou He +6 more
TL;DR: An adaptive weight assignment function to dynamically adjust the measurement weight based on the distance of big unwanted disturbances from the PMU measurements is proposed to increase algorithm robustness and a statistical test-based interpolation matrix H updating judgment strategy is proposed.