This paper presents an efficient approach based on Hopfield network for solving nonlinear optimization problems, with polynomial objective function, polynomial equality constraints and polynomial inequality constraint...
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ISBN:
(纸本)9783540723943
This paper presents an efficient approach based on Hopfield network for solving nonlinear optimization problems, with polynomial objective function, polynomial equality constraints and polynomial inequality constraints. A modified Hopfield network is developed and its stability and convergence is analyzed in the paper. Then a mapping of nonlinear optimization problems is formulated using the modified Hopfield network. Simulation results are provided to demonstrate the performance of the proposed neural network.
The problem of determining the optimum design parameters of single pass and multipass cold strip rolling is considered. The problemis formulated and solved as a constrained nonlinear programming problem by considering...
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This paper presents a methodology for learning arbitrary discrete motions from a set of demonstrations. We model a motion as a nonlinear autonomous (i.e. time-invariant) dynamical system, and define the sufficient con...
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ISBN:
(纸本)9781424466757
This paper presents a methodology for learning arbitrary discrete motions from a set of demonstrations. We model a motion as a nonlinear autonomous (i.e. time-invariant) dynamical system, and define the sufficient conditions to make such a system globally asymptotically stable at the target. The convergence of all trajectories is ensured starting from any point in the operational space. We propose a learning method, called Stable Estimator of Dynamical Systems (SEDS), that estimates parameters of a Gaussian Mixture Model via an optimization problem under non-linear constraints. Being time-invariant and globally stable, the system is able to handle both temporal and spatial perturbations, while performing the motion as close to the demonstrations as possible. The method is evaluated through a set of robotic experiments.
A new general scheme for Inexact Restoration methods for nonlinear programming is introduced. After computing an inexactly restored point, the new iterate is determined in an approximate tangent affine subspace by mea...
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A new general scheme for Inexact Restoration methods for nonlinear programming is introduced. After computing an inexactly restored point, the new iterate is determined in an approximate tangent affine subspace by means of a simple line search on a penalty function. This differs from previous methods, in which the tangent phase needs both a line search based on the objective function (or its Lagrangian) and a confirmation based on a penalty function or a filter decision scheme. Besides its simplicity the new scheme enjoys some nice theoretical properties. In particular, a key condition for the inexact restoration step could be weakened. To some extent this also enables the application of the new scheme to mathematical programs with complementarity constraints.
We investigate the use of a nonlinear control allocation scheme for automotive vehicles. Such a scheme is useful in e.g. yaw or roll stabilization of the vehicle. The control allocation allows a modularization of the ...
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ISBN:
(纸本)0780390989
We investigate the use of a nonlinear control allocation scheme for automotive vehicles. Such a scheme is useful in e.g. yaw or roll stabilization of the vehicle. The control allocation allows a modularization of the control task, such that a higher level control system specifies a desired moment to work on the vehicle, while the control allocation distributes this moment among the individual wheels by commanding appropriate wheel slips. The control allocation problem is defined as a nonlinear., optimization problem, to which an explicit piecewise linear approximate solution function is computed off-line. Such a solution function can computationally efficiently be implemented in real time with at most a few hundred arithmetic operations per sample. Yaw stabilization of the vehicle yaw dynamics is used as an example of use of the control allocation. Simulations show that the controller stabilizes the vehicle in an extreme manoeuvre where the vehicle yaw dynamics otherwise becomes unstable.
An optimization neural network for constrained nonlinear programming is constructed based on the mechanism of economic systems. It is shown that the network can be used to search for global optimal solutions to a non-...
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An optimization neural network for constrained nonlinear programming is constructed based on the mechanism of economic systems. It is shown that the network can be used to search for global optimal solutions to a non-convex objective function and to overcome the drawback of the classical nonlinear programming neural networks that always obtain solutions outside the feasible region.
An algorithmic parameter tuning methodology for controller design of complex systems is needed. This methodology should offer designers a great degree of flexibility and give insight into the potentials of the control...
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ISBN:
(纸本)3540673539
An algorithmic parameter tuning methodology for controller design of complex systems is needed. This methodology should offer designers a great degree of flexibility and give insight into the potentials of the controller structure and the consequences of the design decisions that are made. Such a method is proposed here. For an exploratory phase a new pareto-ranked genetic algorithm is proposed to generate an evenly dispersed set of near optimal, global, solutions. By pair-wise preference statements on design alternatives a linear program is set up as a formal means for selecting the solution with best overall designer satisfaction. In a following interactive design phase using nonlinear programming techniques with a priori decisions on allowed quality levels, a best tuning compromise in competing requirements satisfaction is searched for while guaranteeing pareto-optimality. In particular, this two-phase tuning approach allows the designer to balance nominal control performance and multi-model control robustness.
An essential part of many iterative methods for linearly constrained nonlinear programming problems is a procedure for determining those inequality constraints which will be "active" (that is, satisfied as e...
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In this paper a new neural network is proposed to solve nonlinear convex programming problems. The proposed neural network is shown to be asymptotically stable in the sense of Lyapunov. Comparing with the existing neu...
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ISBN:
(纸本)9783642211041
In this paper a new neural network is proposed to solve nonlinear convex programming problems. The proposed neural network is shown to be asymptotically stable in the sense of Lyapunov. Comparing with the existing neural networks, the proposed neural network has fewer state variables and simpler architecture. Numerical examples show that the proposed network is feasible and efficient.
A new preamble structure and design method for orthogonal frequency division multiplexing（OFDM）systems is described,which results a two-symbol long training *** preamble contains four parts,the first part is the sam...
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A new preamble structure and design method for orthogonal frequency division multiplexing（OFDM）systems is described,which results a two-symbol long training *** preamble contains four parts,the first part is the same as the third,and the four parts are calculated by using nonlinear programming（NLP）model such that the moving correlation of the preamble results a steep rectangular-like pulse of certain width,whose step-down indicates the timing *** results in AWGN channel are given to evaluate the perf o rmance of the proposed preamble design.
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