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.
Papers
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Journal ArticleDOI
Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems
TL;DR: An automatic implementation method for automatically fulfilling the hard task, named membrane computing fault diagnosis (MCFD) method is developed, which is a very significant attempt in the development of FRSN P systems and even of the membrane computing applications.
Journal ArticleDOI
A Novel Membrane Algorithm Based on Particle Swarm Optimization for Solving Broadcasting Problems
Gexiang Zhang,Fen Zhou,Xiaoli Huang,Jixiang Cheng,Marian Gheorghe,Florentin Ipate,Raluca Lefticaru +6 more
TL;DR: Experimental results from various broadcasting problems show that HPSOPS performs better than its counter- part HPSOWM and genetic algorithms reported in the literature, in terms of search capability, efficiency, solution stability and precision.
BookDOI
Membrane Computing Models: Implementations
Gexiang Zhang,Mario J. Pérez-Jiménez,Agustín Riscos-Núñez,Sergey Verlan,Savas Konur,Thomas Hinze,Marian Gheorghe +6 more
Book ChapterDOI
Real-Observation Quantum-Inspired Evolutionary Algorithm for a Class of Numerical Optimization Problems
Gexiang Zhang,Haina Rong +1 more
TL;DR: Experimental results show that RQEA is able to find optimal or close-to-optimal solutions, and is more powerful than conventional real-coded genetic algorithm in terms of fitness, convergence and robustness.
An extended spiking neural p system for fuzzy knowledge representation
TL;DR: The FSN P system is proposed, which is especially suitable to model fuzzy production rules in a rule-based system, and content of neuron is fuzzy number instead of natural number (the number of spikes) in SN P systems.