检索结果-内蒙古大学图书馆

Enhancing the accuracy and precision of forecasting the productivity of a factory: a fuzzified feedforward neural network approach

学校读者我要写书评

暂无评论

COMPLEX & INTELLIGENT SYSTEMS 2021年第5期7卷 2317-2327页

作者： Chen, Toly Lin, Yu-Cheng Natl Chiao Tung Univ Dept Ind Engn & Management 1001 Univ Rd Hsinchu Taiwan Overseas Chinese Univ Dept Comp Aided Ind Design 100 Chiao Kwang Rd Taichung 407 Taiwan

Most existing methods for forecasting the productivity of a factory cannot estimate the range of productivity reliably, especially when future conditions are distinct from those in the past. To address this issue, a fuzzified feedforward neural network (FFNN) approach is proposed in this study. The FFNN approach improves the forecasting precision after generating accurate fuzzy productivity forecasts. In addition, the acceptable range of a fuzzy productivity forecast is specified, based on which the sum of the memberships of actual values is maximized. In this way, the range of productivity can be precisely estimated. After applying the FFNN approach to a real case, the experimental results revealed the superiority of the FFNN approach by improving the forecasting precision, in terms of the hit rate, by 25%. Such an improvement also contributed to a better forecasting accuracy. The superiority of the FFNN approach is in the context that the accuracy of forecasting productivity is optimized only after the range of productivity has been precisely estimated. In contrast, most state-of-the-art methods focus on optimizing the forecasting accuracy, but may be ineffective without information about the range of productivity when future conditions are distinct from the past.

关键词： Feedforward neural network Fuzzify nonlinear programming Productivity

Robust Communication Connectivity for Multi-Robot Path Coordination using Mixed Integer nonlinear programming: Formulation and Feasibility Analysis

学校读者我要写书评

暂无评论

Robust Communication Connectivity for Multi-Robot Path Coord...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Abichandani, Pramod Benson, Hande Y. Kam, Moshe Drexel Univ Coll Engn Philadelphia PA 19104 USA Drexel Univ Dept Decis Sci Philadelphia PA 19104 USA Drexel Univ Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (纸本)9781467356411;9781467356435

Mixed Integer nonlinear programming (MINLP) techniques are increasingly used to address challenging problems in robotics, especially Multi-Vehicle Motion Planning (MVMP). A particular challenge in using this framework is encoding stochastic phenomena such as communication connectivity in the form of MINLP constraints. The main contribution of this paper is an analytical formulation of communication connectivity constraints using stochastic physical layer communication models. These constraints account for the log-normal channel shadowing in noisy communication environments and specify inter-vehicle connectivity in terms of the outage probability of communication. A method is developed to provably accord robustness to communication failure by specifying an upper bound on the outage probability in terms of the inter-vehicle communication range. Finally, we demonstrate the utility of this formulation in the context of a realistic decentralized Multi-Vehicle Path Coordination (MVPC) scenario in which multiple robotic vehicles travel along predetermined fixed paths and are required to maintain communication connectivity during their transit. Conditions that affect the feasibility of the MVPC problem are formaliz1ed. Examples that assist in visualizing these conditions are provided.

关键词： decentralised control integer programming mobile robots multi-robot systems nonlinear programming path planning probability robust control stochastic processes

Robust Lindbladian Tomography for Cyclic Quantum Gates

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Sugiyama, Takanori Quantum Laboratory Fujitsu Limited. Nakahara-ku Kanagawa Kawasaki211-8588 Japan RIKEN RQC-FUJITSU Collaboration Center RIKEN. Wako Saitama351-0198 Japan Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo153-8904 Japan

Precise characterization of noisy quantum operations plays an important role for realizing further accurate operations. Quantum tomography is a popular class of characterization methods, and several advanced methods in the class use error amplification circuit (EAC), a repetition of a sequence of quantum gates, for increasing their estimation precision. Here, we develop new theoretical tools for analyzing effects of an EAC on Lindbladian error of cyclic gates in the EAC for arbitrary finite-dimensional system, which takes non-commutativity between different gates or between ideal and error parts of a gate, periodic properties of ideal gates, and repetition of gate sequence into consideration within a linear approximation. We also propose a tomographic protocol for the Lindbladian errors of cyclic gates based on the linear approximation, named Robust Lindbladian Tomography (RLT). The numerical optimization at data-processing of the proposed method reduces from nonlinear to linear (positive semi-definite) programming. Therefore, compared to the original optimization problem, the reduced one is solvable more efficiently and stably, although its numerical cost grows exponentially with respect to the number of qubits, which is the same as other tomographic methods. Copyright © 2025, The Authors. All rights reserved.

关键词： nonlinear programming

A Globally Convergent Method for Computing B-stationary Points of Mathematical Programs with Equilibrium Constraints

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Nurkanovic, Armin Leyffer, Sven Systems Control and Optimization Laboratory Department of Microsystems Engineering University of Freiburg Germany Mathematics and Computer Science Division Argonne National Laboratory United States

This paper introduces a method that globally converges to Bstationary points of mathematical programs with equilibrium constraints (MPECs) in a finite number of iterations. B-stationarity is necessary for optimality and means that no feasible first-order direction improves the objective. Given a feasible point of an MPEC, B-stationarity can be certified by solving a linear program with equilibrium constraints (LPEC) constructed at this point. The proposed method solves a sequence of LPECs, which either certify B-stationarity or provide an active-set estimate for the complementarity constraints, and nonlinear programs (NLPs) - referred to as branch NLPs (BNLPs) - obtained by fixing the active set in the MPEC. A BNLP is more regular than the MPEC, easier to solve, and with the correct active set, its solution coincides with the solution of the MPEC. The method has two phases: the first phase identifies a feasible BNLP or certifies local infeasibility, and the second phase solves a finite sequence of BNLPs until a B-stationary point of the MPECis found. The paper provides a detailed convergence analysis and discusses implementation details. In addition, extensive numerical experiments and an open-source software implementation are provided. The experiments demonstrate that the proposed method is more robust and faster than relaxation-based methods, while also providing a certificate of B-stationarity without requiring the usual too-restrictive *** Codes 90C30, 90C33, 49M37, 65K10, 90C11 © 2025, CC BY.

关键词： nonlinear programming

A Gap Penalty Reformulation for Mathematical programming with Complementarity Constraints: Convergence Analysis

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Lin, Kangyu Ohtsuka, Toshiyuki Graduate School of Informatics Kyoto University Kyoto Japan

Our recent study [1] proposed a new penalty method to solve the mathematical programming with complementarity constraints (MPCC). This method reformulates the MPCC as a parameterized nonlinear programming (NLP) called gap penalty reformulation and solves a sequence of gap penalty reformulations with an increasing penalty parameter. This letter studies the convergence behavior of the new penalty method. We prove that it converges to a strongly stationary point of MPCC, provided that: (1) The MPCC linear independence constraint qualification holds;(2) The upper-level strict complementarity condition holds;(3) The gap penalty reformulation satisfies the second-order necessary conditions in terms of the second-order directional derivative. Since the strong stationarity is used to identify the local minimum of MPCC, our convergence analysis indicates that the new penalty method is capable of finding an MPCC solution. © 2025, CC BY.

关键词： nonlinear programming

Knowledge Sharing-enabled Semantic Rate Maximization for Multi-cell Task-oriented Hybrid Semantic-Bit Communication Networks

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Chen, Hong Fang, Fang Wang, Xianbin The Department of Electrical and Computer Engineering Canada The Department of Computer Science Western University LondonONN6A 3K7 Canada

In task-oriented semantic communications, the transmitters are designed to deliver task-related semantic information rather than every signal bit to receivers, which alleviates the spectrum pressure by reducing network traffic loads. Effective semantic communications depend on the perfect alignment of shared knowledge between transmitters and receivers, however, the alignment of knowledge cannot always be guaranteed in practice. To tackle this challenge, we propose a novel knowledge sharing-enabled task-oriented hybrid semantic and bit communications mechanism, where a mobile device (MD) can proactively share and upload the task-related mismatched knowledge to associated small base station (SBS). The traditional bit communications can be adopted as an aid to transmit the rest data related to unshared mismatched knowledge to guarantee the effective execution of target tasks. Considering the heterogeneous transceivers in multi-cell networks, target task demands, and channel conditions, an optimization problem is formulated to maximize the generalized effective semantic transmission rate of all MDs by jointly optimizing knowledge sharing, semantic extraction ratio, and SBS association, while satisfying the semantic accuracy requirements and delay tolerances of MD target tasks. The formulated mixed integer nonlinear programming problem is decomposed into multiple subproblems equivalently. An optimum algorithm is proposed and another efficient algorithm is further developed using hierarchical class partitioning and monotonic optimization. Simulation results demonstrate the validity and superior performance of proposed solutions. Copyright © 2025, The Authors. All rights reserved.

关键词： nonlinear programming

Modified Hybrid Multiagent Swarm Optimization Algorithms for Mixed-Binary nonlinear programming

学校读者我要写书评

暂无评论

Modified Hybrid Multiagent Swarm Optimization Algorithms for...

46th Annual Hawaii International Conference on System Sciences (HICSS)

作者： Zhang, Haopeng Hui, Qing Texas Tech Univ Dept Mech Engn Lubbock TX 79409 USA

ISBN: (纸本)9780769548920;9781467359337

Motivated by many social phenomena such as bird flocking and fish schooling, in this paper three types of constrained hybrid multiagent swarm optimization (HMSO) algorithms are presented to address the constrained optimization problem by incorporating the fly-back mechanism into the update formula for the particle's position. The original HMSO algorithm is proposed for solving unconstrained, continuous optimization problems by using a simple logic switching structure to achieve superior performance. However, this method cannot be directly used to solve discrete optimization problems like the binary programming. Since the application of the mixed-binary nonlinear programming (MBNLP) problem is widespread in many system engineering problems, it is necessary to develop an HMSO based optimization algorithm to address the mixed-binary optimization so that one can achieve the better performance for MBNLP with a simple algorithm structure. In this context, first a binary version of the constrained type of the HMSO algorithm is provided by introducing communication topologies for the particles to exchange their position information, which is well studied under multiagent coordination problems in control theory. By taking advantage of the fly-back mechanism dealing with constraints in optimization, a new architecture for HMSO is introduced to form a constrained HMSO algorithm for constrained optimization. Finally, we combine the proposed binary HMSO and constrained HMSO to create a modified HMSO algorithm to address the MBNLP problem. Several benchmark functions are used for the evaluation of the binary HMSO, constrained HMSO, and modified HMSO algorithm and compared with the standard particle swarm optimization algorithm.

关键词： nonlinear programming topology

学校读者我要写书评

暂无评论

A

Journal of Computational and nonlinear Dynamics 2023年第8期18卷 081001页

作者： Bartali, Lorenzo Gabiccini, Marco Guiggiani, Massimo Dipartimento di Ingegneria Civile e Industriale Università di Pisa Pisa PI 56122 Italy

This paper presents an automatic procedure to enhance the accuracy of the numerical solution of an optimal control problem (OCP) discretized via direct collocation at Gauss–Legendre points. First, a numerical solution is obtained by solving a nonlinear program (NLP). Then, the method evaluates its accuracy and adaptively changes both the degree of the approximating polynomial within each mesh interval and the number of mesh intervals until a prescribed accuracy is met. The number of mesh intervals is increased for all state vector components alike, in a classical fashion. Instead, improving on state-of-the-art procedures, the degrees of the polynomials approximating the different components of the state vector are allowed to assume, in each finite element, distinct values. This explains thepnhdefinition, wherenis the state dimension. With respect to the approaches found in the literature, where the degree is always raised to the highest order for all the state components, our methods allow a sensible reduction of the overall number of variables of the resulting NLP, with a corresponding reduction of the computational burden. Numerical tests on three OCP problems highlight that, under the same maximum allowable error, by independently selecting the degree of the polynomial for each state, our method effectively picks lower degrees for some of the states, thus reducing the overall number of variables in the NLP. Accordingly, various advantages are brought about, the most remarkable being: (i) an increased computational efficiency for the final enhanced mesh with solution accuracy still within the prescribed tolerance, (ii) a reduced risk of being trapped by local minima due to the reduced NLP size, and (iii) a gain of the robustness of the convergence process due to the better-behaved solution landscapes.

关键词： Errors Polynomials nonlinear programming Algorithms Optimal control

Optimal Generator Policy for Hybrid Fuel UAV under Airspace Noise Restrictions

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2023年第3期56卷 25-30页

作者： Drew D. Scott Isaac E. Weintraub Satyanarayana G. Manyam David W. Casbeer Manish Kumar Department of Mechanical and Materials Engineering University of Cincinnati Cincinnati OH 45221 Control Science Center Air Force Research Laboratory Wright-Patterson AFB OH 45433 Infoscitex Corp. a DCS Company Dayton OH 45431

Here we study an optimal control problem involving energy management of a hybrid-fuel Unmanned Aerial Vehicle (UAV). The planning problem for a hybrid-fuel platform involves determining the path while managing the energy resources, which includes a policy for power modality switching whenever applicable. The hybrid-fuel platform considered here involves a generator and battery pack combined in a series fashion as energy sources on-board a UAV. Also included in the problem are the noise restrictions, which place constraints on generator operation depending on the airspace location. These emulate possible restrictions on UAV noise that occur in military surveillance missions or in urban path planning, where the collective noise of many UAVs, some with combustion engines, may be restricted in certain areas or times of the day. We present a hybrid methodology which starts from an initial path and generator pattern obtained from a mixed integer linear program (MILP) solution. The generator pattern from the discrete solution is then refined in an optimal control framework with an objective to minimize fuel usage, while considering the nonlinear battery and generator dynamics and noise-restriction constraints. Optimal control problem is solved with a nonlinear program solver, IPOPT. Numerical results are presented and analyzed with varying path lengths and scenarios. This work aims to serve as an initial study of this hybrid-fuel UAV problem within an optimal control framework, which can be extended to refinement of both the generator pattern and the trajectory in tandem, while considering vehicle and power dynamics that are often ignored in discrete path planning solutions.

关键词： UAV hybrid-fuel optimal control path planning optimization hybrid methods nonlinear programming