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The study presents a method for iterative parameter tuning of tree ensemble-based models using Bayesian hyperparameter tuning for states prediction, using breast cancer as an example. The proposed method utilizes three different datasets, including the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the Surveillance, Epidemiology, and End Results (SEER) breast cancer dataset, and the Breast Cancer Coimbra dataset (BCCD), and implements tree ensemble-based models, specifically AdaBoost, Gentle-Boost, LogitBoost, Bag, and RUSBoost, for breast cancer prediction. Bayesian optimization was used to tune the hyperparameters of the models iteratively, and the performance of the models was evaluated using several metrics, including accuracy, precision, recall, and f1-score. Our results show that the proposed method significantly improves the performance of tree ensemble-based models, resulting in higher accuracy, precision, recall, and f1-score. Compared to other state-of-the-art models, the proposed method is more efficient. It achieved perfect scores of 100% for Accuracy, Precision, Recall, and F1-Score on the WDBC dataset. On the SEER BC dataset, the method achieved an accuracy of 95.9%, a precision of 97.6%, a recall of 94.2%, and an F1-Score of 95.9%. For the BCCD dataset, the method achieved an accuracy of 94.7%, a precision of 90%, a recall of 100%, and an F1-Score of 94.7%. The outcomes of this study have important implications for medical professionals, as early detection of breast cancer can significantly increase the chances of survival. Overall, this study provides a valuable contribution to the field of breast cancer prediction using machine learning.

The properties and applications of an adaptive multisection filter designed for real time operation in conditions typical for relay protection are considered. These conditions are the uncertainty of the spectral composition of the observed periodic or transient process and unknown frequency, especially in systems with a wide range and high rate of frequency change. The tuning of an adaptive filter is maximally simplified: the input is suppressed assuming that its model is the sum of its harmonic and exponential components. All sections are tuned in turn, according to the least-squares criterion. Examples of iterative determination of the frequencies of a three-frequency input, a transient variable with an exponential component, and an asynchronous process with an unknown carrier and envelope are given. [ABSTRACT FROM AUTHOR]

Control decoupling is the basic control step for precision MIMO (Multi-Input-Multi-Output) motion systems. After decoupling, the MIMO logical controlled plant can be diagonal dominant so that the control design for each DOF (Degree of Freedom) can be separately treated. However, due to the manufacturing tolerances and the assembling errors, the actual CoG (Center of Gravity) and actuator positions are inconsistent with the designed values. The nominal static control decoupling matrix is inaccurate, which significantly deteriorates the decoupling performance. To address the problem, this paper develops a matrix update based iterative control decoupling tuning method. Tuning with feedback control signals to achieve accurate tuning is its first distinct feature. By employing rigid-body model information to construct more accurate basis functions, fast tuning can also be achieved, especially for relatively low control bandwidth occasions. Differing from the feedforward compensation based iterative control decoupling tuning method, the matrix update method improves the dynamics of the logical controlled plant, does not increase the control complexity and is more robust to the inaccuracy of the model information. Experimental results on the short-stroke module of a precision motion stage which is the key subsystem of the lithographic projection lens testing system present significant control decoupling performance improvement (for example the peak value of the Rx-DOF servo error caused by the Z-DOF movement is reduced from 1.29 ×10 -5 m to 3.19 ×10 -6 m) after just two trials., Competing Interests: Declaration of Competing Interest The authors declared no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 ISA. Published by Elsevier Ltd. All rights reserved.)

Linear motors are promising in improving the manufacturing equipments' performance because of eliminating the flexible coupling component. However, the force ripple produced by the linear motors directly causes the feed fluctuation and thus degrades the motion precision. This article utilizes a feed-forward controller added to the feedback one to compensate its effect for the sake of simplicity and robustness. Considering that the force ripple is periodic to the position, a position-dependent multi-order harmonic model is built and used as the feed-forward controller. In order to obtain the controller parameters for various applications, an iterative tuning method is proposed. This method has the advantage that both the high performance for different trajectories tracking and the robustness to disturbances are guaranteed. Experiments on a linear motor illustrate that the parameters converge rapidly. The results show that the tracking performance is improved greatly and the tracking errors are reduced by 60% at least. [ABSTRACT FROM AUTHOR]

Servo system is widely used in NC machines and its performance directly determines the precision of the machines. In most situations, the control structure for the servo system usually contains a cascaded P-PI feedback controller and a feedforward controller. This paper focuses on the feedforward controller parameters tuning to improve the servo performance. The feedforward controller consists of a model inversion and a parameterized disturbance model. Its parameters are tuned iteratively using the last cycle motion results. This method has the good extrapolation capability to the references and the performance improvement capacity. Moreover, it is easy to implement in real machines due to the simplicity and thus is of interest to control engineers. Experiments are carried out on an industrial prototype system. The results show that the proposed tuning method can improve the servo performance rapidly and the references are not required to keep the same during the tuning process. [ABSTRACT FROM AUTHOR]

This paper proposes the application of fictitious reference iterative tuning (FRIT) method to optimize the gains of dc voltage controller of grid connected photo-voltaic (PV) system. It may be difficult to achieve good control of dc voltage using conventional PI controller having only one-degree-of-freedom (1-DOF) due to the trade-off between overshoot (in step response) and disturbance response. In this paper, the optimal control of dc voltage is proposed with improved disturbance response by implementing 2-DOF PI controller structure. FRIT method has been programmed in MATLAB based upon the particle swarm optimization (PSO) algorithm. The fundamental idea related to FRIT method is the extraction of input and output data, reference model setting and range of controller gains. The performance of dc voltage control for the optimized 2-DOF PI controller is also compared with the fuzzy logic controller (FLC) response. [ABSTRACT FROM AUTHOR]

Resource-limited mobile embedded systems can benefit greatly from dynamic adaptation of system parameters. We propose a novel approach that employs iterative tuning using lightweight formal verification at runtime with feedback for dynamic adaptation. One objective of this approach is to enable trade-off analysis across multiple layers (e.g., application, middleware, OS) and predict the possible property violations as the system evolves dynamically over time. Specifically, an executable formal specification is developed for each layer of the mobile system under consideration. The formal specification is then analyzed using statistical property checking and statistical quantitative analysis, to determine the impact of various resource management policies for achieving desired timing/QoS properties. Integration of formal analysis with dynamic behavior from system execution results in a feedback loop that enables model refinement and further optimization of policies and parameters. We demonstrate the applicability of this approach to the adaptive provisioning of resource-limited distributed real-time systems using a mobile multimedia case study. [ABSTRACT FROM AUTHOR]

A practical iterative tuning algorithm of a decoupling controller is presented to eliminate intrinsic coupling among 3-DOF precision motion stage. General decoupling control cannot eliminate coupling completely, which will prevent the enhancement of control accuracy in high-precision motion system. The proposed algorithm can be used to tune the parameters of a decoupling controller iteratively through minimizing a quadratic cost function of the tracking error in non-movement direction when the stage moves in one direction. The tuning algorithm addressed for the motion stage needs only measurement signals in an actual motion system rather than a detailed model of the stage. The proposed algorithm is demonstrated by experimental results. It can be applied further in other multi-DOF motion systems. [ABSTRACT FROM AUTHOR]