To effectively solve the problem of insufficient node coverage in wireless sensor networks, a network coverage optimization method based on a hybrid strategy to improve the sparrowsearch algorithm named CF-SSA is pro...
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ISBN:
(纸本)9789811903908;9789811903892
To effectively solve the problem of insufficient node coverage in wireless sensor networks, a network coverage optimization method based on a hybrid strategy to improve the sparrowsearch algorithm named CF-SSA is proposed. Firstly, a logistic chaotic mapping is introduced to increase the population diversity and thus improve the search speed of the algorithm. Secondly, a hybrid strategy of Cauchy variance factor and firefly perturbation is introduced in the local search phase of the algorithm, which enables the algorithm to effectively jump out of the local optimum. Finally, the improved algorithm is applied to wireless sensor network coverage optimization. The simulation results show that CF-SSA improves wireless sensor network coverage and optimizes more uniform node distribution compared to other optimization algorithms.
Aiming to extract useful features from bearing signals for fault classification, an intelligent fault diagnosis method is proposed with a stacked denoising auto-encoder (SDAE) and adaptive deep hybrid kernel extreme l...
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Aiming to extract useful features from bearing signals for fault classification, an intelligent fault diagnosis method is proposed with a stacked denoising auto-encoder (SDAE) and adaptive deep hybrid kernel extreme learning machine (ADHKELM). The deep network architecture of the SDAE is used automatically to extract deeply important features, and a new HKELM is constructed by combining a polynomial with a wavelet kernel function to overcome a single kernel function not being universal. After that a DHKELM, from stacking multiple HKELMs, and the sparrowsearch algorithm are introduced to iteratively determine the optimal value of core hyper-parameter combinations of the DHKELM to generate the final fault classifier ADHKELM to enhance the performance of the model. Two experimental verification results show that the SDAE-ADHKELM has better fault classification precision, robustness and generalizability than other related methods.
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