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        <title>Frontiers in Control Engineering | AI and Machine Learning Control section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/control-engineering/sections/ai-and-machine-learning-control</link>
        <description>RSS Feed for AI and Machine Learning Control section in the Frontiers in Control Engineering journal | New and Recent Articles</description>
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        <pubDate>2026-05-02T03:30:47.876+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2023.1162318</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2023.1162318</link>
        <title><![CDATA[Optimization design of crude oil distillation unit using bi-level surrogate model]]></title>
        <pubdate>2023-03-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yingjian Xiong</author><author>Xuhua Shi</author><author>Yongjian Ma</author><author>Yifan Chen</author>
        <description><![CDATA[Crude Oil Distillation Unit (CDU) is one of the most important separation installations in the petroleum refinery industries. In this work, a Bi-level Surrogate column model Aided Constrained Optimization Design (Bi-SACOD) is proposed for time-consuming objectives and constraints in the evolutionary optimization design of CDUs. The main components of Bi-SACOD include bi-level surrogate model construction (Bi-SMC), bi-level model management (Bi-MM), and particle swarm optimization (PSO) mixed-integer constrained evolutionary (PSO-MICE) search. Bi-SMC implements surrogate column model construction and feasible domain identification. Bi-MM combines surrogate column models with rigorous CDU simulations to perform model management, and PSO-MICE implements optimum search works. The optimization results of the CDUs indicate that Bi-SACOD outperforms the single-level surrogate column model approaches, and are more consistent with the rigorous CDU model optimization approach, whereas the evaluation numbers of the time-consuming rigorous models are significantly reduced.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2022.1017256</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2022.1017256</link>
        <title><![CDATA[Inference of regulatory networks through temporally sparse data]]></title>
        <pubdate>2022-12-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohammad Alali</author><author>Mahdi Imani</author>
        <description><![CDATA[A major goal in genomics is to properly capture the complex dynamical behaviors of gene regulatory networks (GRNs). This includes inferring the complex interactions between genes, which can be used for a wide range of genomics analyses, including diagnosis or prognosis of diseases and finding effective treatments for chronic diseases such as cancer. Boolean networks have emerged as a successful class of models for capturing the behavior of GRNs. In most practical settings, inference of GRNs should be achieved through limited and temporally sparse genomics data. A large number of genes in GRNs leads to a large possible topology candidate space, which often cannot be exhaustively searched due to the limitation in computational resources. This paper develops a scalable and efficient topology inference for GRNs using Bayesian optimization and kernel-based methods. Rather than an exhaustive search over possible topologies, the proposed method constructs a Gaussian Process (GP) with a topology-inspired kernel function to account for correlation in the likelihood function. Then, using the posterior distribution of the GP model, the Bayesian optimization efficiently searches for the topology with the highest likelihood value by optimally balancing between exploration and exploitation. The performance of the proposed method is demonstrated through comprehensive numerical experiments using a well-known mammalian cell-cycle network.]]></description>
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