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The Multiple Model Control (MMC) structure comprises three main components: the model bank, controller bank, and supervisor algorithm. Precise design of these components is crucial for achieving high control performance within the MMC framework, albeit this effort is not without its challenges. These challenges involve optimizing the model and controller banks ensuring system stability when dealing with uncertainties in the local models and enabling smooth switching between model-controller pairs. This paper addresses these challenges by presenting a comprehensive approach. Firstly, the optimal model bank is designed using an automatic clustering approach. Then, the design of the adaptive multi-model predictive controller bank and a supervisor algorithm capable of performing soft switching are discussed. The proposed control system exhibits the capability to ensure closed-loop system stability within each individual subspace, as well as during the transitioning between distinct subspaces. This stability is preserved even in the face of inherent uncertainties associated with the local models comprising the model bank. To evaluate and validate the performance of the proposed control system, it is applied to a satellite attitude control system. The results confirm the effectiveness and performance of the control system. The proposed control system holds promise for controlling highly nonlinear, complex, or switched systems, ensuring closed-loop system stability, and achieving high control performance., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)

In the Multiple Model Control (MMC) strategies, a bank of simple local models is used to describe the behavior of complex systems with vast operation space. In this approach, the system operation space is divided into several subspaces, and in each subspace, a simple local model is assigned to describe the system behavior. This study addresses the two main challenges in this field which involve determining the optimal number of required local models to form the model bank and identifying the optimal distribution of the local models across the system operation space. Providing appropriate answers to these questions directly affects the performance of the MMC system. In this paper, GA-based automatic clustering method is suggested to form an optimal model bank. In this regard, an appropriate mapping is established between the concepts of MMC and automatic clustering, and a novel unsupervised algorithm is designed to determine the optimal model bank. Unlike the existing methods in the literature, the proposed method can form the global optimal model bank without entrapment into local optima regardless of the initial conditions of the used search algorithm. In this paper, the formation of the optimal model bank using the proposed method is investigated by considering the spacecraft attitude dynamics as a complex, MIMO, non-linear case study and its satisfactory and promising performance is demonstrated., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Clustering is a difficult and widely studied data mining task, with many varieties of clustering algorithms proposed in the literature. Nearly all algorithms use a similarity measure such as a distance metric (e.g., Euclidean distance) to decide which instances to assign to the same cluster. These similarity measures are generally predefined and cannot be easily tailored to the properties of a particular dataset, which leads to limitations in the quality and the interpretability of the clusters produced. In this article, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming. We introduce a new genetic programming-based method which automatically selects a small subset of features (feature selection) and then combines them using a variety of functions (feature construction) to produce dynamic and flexible similarity functions that are specifically designed for a given dataset. We demonstrate how the evolved similarity functions can be used to perform clustering using a graph-based representation. The results of a variety of experiments across a range of large, high-dimensional datasets show that the proposed approach can achieve higher and more consistent performance than the benchmark methods. We further extend the proposed approach to automatically produce multiple complementary similarity functions by using a multi-tree approach, which gives further performance improvements. We also analyse the interpretability and structure of the automatically evolved similarity functions to provide insight into how and why they are superior to standard distance metrics.

Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarkers for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). SNR may be improved by averaging functional information in a large region of interest when it is functionally homogeneous. We propose a novel method for automatic segmentation of DCE image sequences into functionally homogeneous regions, called DCE-HiSET. Using an observation model which depends on one parameter a and is justified a posteriori, DCE-HiSET is a hierarchical clustering algorithm. It uses the p-value of a multiple equivalence test as dissimilarity measure and consists of two steps. The first exploits the spatial neighborhood structure to reduce complexity and takes advantage of the regularity of anatomical features, while the second recovers (spatially) disconnected homogeneous structures at a larger (global) scale. Given a minimal expected homogeneity discrepancy for the multiple equivalence test, both steps stop automatically by controlling the Type I error. This provides an adaptive choice for the number of clusters. Assuming that the DCE image sequence is functionally piecewise constant with signals on each piece sufficiently separated, we prove that DCE-HiSET will retrieve the exact partition with high probability as soon as the number of images in the sequence is large enough. The minimal expected homogeneity discrepancy appears as the tuning parameter controlling the size of the segmentation. DCE-HiSET has been implemented in C++ for 2D and 3D image sequences with competitive speed., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Scoping reviews of research help determine the feasibility and the resource requirements of conducting a systematic review, and the potential to generate a description of the literature quickly is attractive., Aims: To test the utility and applicability of an automated clustering tool to describe and group research studies to improve the efficiency of scoping reviews., Methods: A retrospective study of two completed scoping reviews was conducted. This compared the groups and descriptive categories obtained by automatically clustering titles and abstracts with those that had originally been derived using traditional researcher-driven techniques., Results: The clustering tool rapidly categorised research into themes, which were useful in some instances, but not in others. This provided a dynamic means to view each dataset. Interpretation was challenging where there were potentially multiple meanings of terms. Where relevant clusters were unambiguous, there was a high precision of relevant studies, although recall varied widely., Conclusions: Policy-relevant scoping reviews are often undertaken rapidly, and this could potentially be enhanced by automation depending on the nature of the dataset and information sought. However, it is not a replacement for researcher-developed classification. The possibilities of further applications and potential for use in other types of review are discussed., (Copyright © 2013 John Wiley & Sons, Ltd.)