Optimization problems in which a quadratic objective function is optimized subject to linear constraints on the parameters are known as quadratic programming problems (QPs). This focus article reviews algorithms for c...
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Optimization problems in which a quadratic objective function is optimized subject to linear constraints on the parameters are known as quadratic programming problems (QPs). This focus article reviews algorithms for convex QPs (in which the objective is a convex function) and provides pointers to various online resources about QPs. (C) 2015 Wiley Periodicals, Inc.
The conventional zeroing neural network (ZNN) model faces significant challenges in handling time-varying noise, with its convergence speed being highly sensitive to initial conditions. In this paper, we propose a new...
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The conventional zeroing neural network (ZNN) model faces significant challenges in handling time-varying noise, with its convergence speed being highly sensitive to initial conditions. In this paper, we propose a new parameter-changing integral ZNN model with nonlinear activation (NAPCIZNN) to effectively tackle time-varying quadratic programming problems with inequality constraints (IC-TVQP). By integrating a nonlinear activation function and dynamic parameter adjustment, the proposed NAPCIZNN model exhibits superior convergence speed and robust noise tolerance. We rigorously derive the theoretical upper bound for convergence time under noisy environments, providing a strong foundation for the model's reliability. Comprehensive numerical simulations demonstrate that NAPCIZNN significantly outperforms traditional ZNN variants-including the original ZNN, nonlinear activated ZNN, integral ZNN, and piecewise variable parameter ZNN-in solving time-varying quadratic programming problems. Moreover, the practical application of the NAPCIZNN model in controlling the PUMA560 robotic manipulator showcases its robustness and precision in real-world scenarios. Empirical evidence from these applications validates the model's exceptional capability in executing complex butterfly trajectory tracking controls with high accuracy and reliability.
This paper proposes a novel one-layer neural network to solve quadratic programming problems in real time by using a control parameter and transforming the optimality conditions into a system of projection equations. ...
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This paper proposes a novel one-layer neural network to solve quadratic programming problems in real time by using a control parameter and transforming the optimality conditions into a system of projection equations. The proposed network includes two existing dual networks as its special cases, and an existing model can be derived from it. In particular, another new model for linear and quadratic programming problems can be obtained from the proposed network. Meanwhile, a new Lyapunov function is constructed to ensure that the proposed network is Lyapunov stable and can converge to an optimal solution of the concerned problem under mild conditions. In contrast with the existing models for quadratic programming, the proposed network requires the least neurons while maintaining weaker stability conditions. The effectiveness and characteristics of the proposed model are demonstrated by the limited simulation results.
This paper deals with a class of quadratic programming problems having intuitionistic fuzzy parameters and bounded constraints. Such problems are designed to handle uncertain parameters in quadratic programming and pr...
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This paper deals with a class of quadratic programming problems having intuitionistic fuzzy parameters and bounded constraints. Such problems are designed to handle uncertain parameters in quadratic programming and provide a better representation of many real-life situations. This study presents the utilization of (alpha,u)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\alpha ,u)$$\end{document} and (beta,v)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\beta ,v)$$\end{document} cuts and a new solution methodology is suggested to obtain the lower and upper bounds of the objective function in the problem. Using the bounds obtained, we construct the membership and non-membership functions of the optimal values graphically. By expressing the optimal value through membership and non-membership functions instead of a crisp value, this method offers a more detailed and nuanced view of the data, which can lead to better-informed decision-making. Moreover, it has been found that the proposed method yields more efficient solutions, requiring less computational work. We illustrate the solution procedure of the proposed technique by applying it to a real-life problem in textile industry.
Data-driven methods for constructing HIs are now well-established for incipient fault detection and prognostic analysis. In order for physics-explainable diagnositic and prognostic information to be easily embeded int...
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Data-driven methods for constructing HIs are now well-established for incipient fault detection and prognostic analysis. In order for physics-explainable diagnositic and prognostic information to be easily embeded into the HI construction process, existing optimization-based weight learning approaches are limited by considering only linear fusion functions. Integrating the physical information related to bearing degradation into the optimization based HI construction process in a nonlinear form, has yet to be fully explored. To solve this problem, this study first proposes a novel nonlinear optimization-based weight learning model for constructing a HI. By introducing kernel methods, the model maximizes the correlation between the latest measured time series and previously measured time series while simultaneously minimizing the fitting error, effectively capturing the nonlinear relations between spectral components. We also explore how the bearing degradation-related physics information can be fully revealed from the built HIs and how this information can be used to explain the superior performance of the HIs in terms of diagnosis and prognosis. Moreover, the constructed HIs are utilized for remaining useful life (RUL) prediction based on a stochastic degradation modelling approach. The ability of the proposed nonlinear weight learning model to detect incipient faults and model degradation is verified through two bearing run-to-failure case studies.
This article considers the two-stage approach to solving a partially observable Markov decision process (POMDP): the identification stage and the (optimal) control stage. We present an inexact sequential quadratic pro...
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This article considers the two-stage approach to solving a partially observable Markov decision process (POMDP): the identification stage and the (optimal) control stage. We present an inexact sequential quadratic programming framework for recurrent neural network learning (iSQPRL) for solving the identification stage of the POMDP, in which the true system is approximated by a recurrent neural network (RNN) with dynamically consistent overshooting (DCRNN). We formulate the learning problem as a constrained optimization problem and study the quadratic programming (QP) subproblem with a convergence analysis under a restarted Krylov-subspace iterative scheme that implicitly exploits the structure of the associated Karush-Kuhn-Tucker (KKT) subsystem. In the control stage, where a feedforward neural network (FNN) controller is designed on top of the RNN model, we adapt a generalized Gauss-Newton (GGN) algorithm that exploits useful approximations to the curvature terms of the training data and selects its mini-batch step size using a known property of some regularization function. Simulation results are provided to demonstrate the effectiveness of our approach.
Existing computational models for addressing time-dependent quadratic programming (TDQP) problems encounter some challenges, such as generating lagging errors, lack of noise immunity, and inability to be distributed. ...
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The concept of upper variance under multiple probabilities is defined through a corresponding minimax optimization *** study proposes a simple algorithm to solve this optimization problem ***,we provide a probabilisti...
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The concept of upper variance under multiple probabilities is defined through a corresponding minimax optimization *** study proposes a simple algorithm to solve this optimization problem ***,we provide a probabilistic representation for a class of quadratic programming problems,demonstrating the practical application of our approach.
A high-accuracy hand-eye calibration is vital in robotic inspection (RI). The inaccurate robotic kinematic matrix and sensors extrinsic matrix decrease the accuracy of the hand-eye calibration matrix. Previous works f...
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The primary challenge is to design feedback controls that enable robots to autonomously reach predetermined destinations while avoiding collisions with obstacles and other robots. Various control algorithms, such as t...
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The primary challenge is to design feedback controls that enable robots to autonomously reach predetermined destinations while avoiding collisions with obstacles and other robots. Various control algorithms, such as the control barrier function-based quadratic programming (CBF-QP) controller, address collision avoidance problems. Control barrier functions (CBFs) ensure forward invariance, which is critical for guaranteeing safety in robotic collision avoidance within agricultural fields. The goal of this study is to enhance the safety and mitigation of potential collisions in smart agriculture systems. The entire system was simulated in the MATLAB/Simulink environment, and the results demonstrated a 93% improvement in steady-state error over rapidly exploring random tree (RRT). These findings indicate that the proposed controller is highly effective for collision avoidance in smart agricultural systems.
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