A Novel Dynamic Evolutionary Model Integrating Discrete Hopfield Neural Networks With Satisfiability Problems And Its Applications In Image Encryption And Decryption
This thesis proposes a series of innovative DHNN-SAT variants and their applications. To address the inefficiency of traditional DHNN-SAT networks in solving SAT problems with dynamic constraints, a Dynamically Evolving Discrete Hopfield-SAT Neural Network with a flexible and scalable architecture i...
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| Format: | Thesis |
| Language: | English |
| Published: |
2025
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| Online Access: | http://eprints.usm.my/63794/ |
| Abstract | Abstract here |
| Summary: | This thesis proposes a series of innovative DHNN-SAT variants and their applications. To address the inefficiency of traditional DHNN-SAT networks in solving SAT problems with dynamic constraints, a Dynamically Evolving Discrete Hopfield-SAT Neural Network with a flexible and scalable architecture is specifically designed. To tackle challenges posed by varying network scales and logical complexities, an optimized network based on a Crow Search Algorithm-guided Fuzzy Clustering Hybrid Method is proposed. |
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