2 results about "Clonal selection algorithm" patented technology

This invention discloses a method for ultra-dense wireless audio terminal networking and multi-source data fusion, belonging to the field of ultra-dense networking and audio processing technology. The method involves the terminal collecting sound field temporal characteristics, wireless channel status, and location information; performing distributed time-slot collaborative scheduling based on sound field spatial correlation, allocating orthogonal short time slots to suppress co-channel interference; implementing unsupervised clustering of multi-source data and generating fusion weights through a Dirichlet process variational autoencoder; dynamically adjusting point density and transmission power using the Navier-Stokes equation, and optimizing relay links by combining small-world networks and quantum heuristics; compensating for high-frequency audio gaps using a conditional generative adversarial network, and uploading the data after joint encoding of the source and channel; and employing a clonal selection algorithm to achieve self-healing of network anomalies. This invention can reduce interference and redundant transmission, improving audio transmission quality and system robustness in ultra-dense scenarios.

This invention belongs to the interdisciplinary field of artificial intelligence and distributed computing, specifically a method for high-dimensional data feature selection in computing power networks based on immune federated learning. This method includes: constructing a federated immune feature space; designing a dynamic clonal selection algorithm incorporating a spatiotemporal decay factor to achieve the co-evolution and optimization of feature affinity; using a federated graph attention network to quantize and extract feature embeddings; establishing a three-level immune memory bank to enhance adaptability; designing a computing power-aware antibody diffusion scheduling mechanism to control the diffusion range based on node computing power and privacy constraints; and dynamically adjusting the immune response threshold using reinforcement learning. Ultimately, it achieves collaborative and green optimization of high-dimensional features and computing resources under privacy protection. This invention effectively solves the problems of local optima, high communication overhead, and low computing power utilization in cross-institutional high-dimensional data feature selection, significantly improving the performance and generalization ability of federated models while reducing system energy consumption.