Improved quantum-inspired immune clonal clustering algorithm applied to SAR image segmentation

Li, Y.Y.; Wu, Na-Na; Liu, R.C.

doi:10.1109/cie-radar.2011.6159862

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…To address the problem of image segmentation, [106] introduces watershed algorithm into QICA to improve oversegmentation. A load balancing strategy is adopted in [107] to accomplish task scheduling and allocation.…”

Section: C: Combination With Other Algorithmsmentioning

confidence: 99%

Quantum Optimization and Quantum Learning: A Survey

Tian

Liu

et al. 2020

IEEE Access

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Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios and brand-new vitality. Inspired by nature, intelligent algorithm has always been one of the research hotspots. It is a frontier interdisciplinary subject with a perfect integration of biology, mathematics and other disciplines. Naturally, the idea of combining quantum mechanism with intelligent algorithms will inject new vitality into artificial intelligence system. This paper lists major breakthroughs in the development of quantum domain firstly, then summarizes the existing quantum algorithms from two aspects: quantum optimization and quantum learning. After that, related concepts, main contents and research progresses of quantum optimization and quantum learning are introduced respectively. At last, experiments are conducted to prove that quantum intelligent algorithms have strong competitiveness compared with traditional intelligent algorithms and possess great potential by simulating quantum computing. INDEX TERMS Quantum optimization, quantum learning, quantum evolutionary algorithm (QEA), quantum particle swarm algorithm (QPSO), quantum immune clonal algorithm (QICA), quantum neural network (QNN), quantum clustering (QC).

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Section: C: Combination With Other Algorithmsmentioning

confidence: 99%

Quantum Optimization and Quantum Learning: A Survey

Tian

Liu

et al. 2020

IEEE Access

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Chaotic Image Encryption: State-of-the-Art, Ecosystem, and the Promise of Quantum-Inspired AI

Zolfaghari,

Nemati,

Yanai

et al. 2023

Crypto and AI

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Research of Fuzzy Neural Network Model Based on Quantum Clustering

Sun¹,

Hao²

2009

2009 Second International Workshop on Knowledge Discovery and Data Mining

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Fuzzy neural network can handle non-linear, complex data, but the structure of model determination is an important and difficult issues identified. More complete results can be made .in a short period of time by the optimization network model. To address this issue, this paper presents the fusion of a quantum clustering algorithm and fuzzy c-means clustering algorithm, the fuzzy neural network structure is carried out at different levels data processing. Through the model of mining in complex industrial process, the validity of the model is tested.

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Improved quantum-inspired immune clonal clustering algorithm applied to SAR image segmentation

Cited by 3 publications

References 11 publications

Quantum Optimization and Quantum Learning: A Survey

Quantum Optimization and Quantum Learning: A Survey

Chaotic Image Encryption: State-of-the-Art, Ecosystem, and the Promise of Quantum-Inspired AI

Research of Fuzzy Neural Network Model Based on Quantum Clustering

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