This paper develops a novel method for solving a type of nonlinear programming model with all fuzzy coefficients (AFCNP). For a decision maker specified credibility level, by presenting the equivalent deterministic fo...
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ISBN:
(纸本)3540283250
This paper develops a novel method for solving a type of nonlinear programming model with all fuzzy coefficients (AFCNP). For a decision maker specified credibility level, by presenting the equivalent deterministic forms of fuzzy inequality constraints and fuzzy objective, the fuzzy model is converted into a crisp constrained nonlinear programming model with parameter (CPNP). An improved genetic algorithm is presented to solve the CPNP and obtain the crisp optimal solution of AFCNP for specified credibility level.
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 opt...
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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.
Non-rigid image registration is a key technique in medical image analysis. In conventional non-rigid registration, the whole image is deformed in a non-rigid fashion. However, in some clinical applications, the regist...
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ISBN:
(纸本)9780819485045
Non-rigid image registration is a key technique in medical image analysis. In conventional non-rigid registration, the whole image is deformed in a non-rigid fashion. However, in some clinical applications, the registration process is required to maintain rigidity in some parts of the image (e.g. bones) while other parts of the image (e.g. soft tissues) can deform in a non-rigid fashion. In this paper, we employ nonlinear programming techniques to solve the registration problem efficiently while ensuring feasibility of the solution with respect to rigidity constraints. Our approach differs from others from an optimization perspective: Unlike the frequently used regularization formulation that incorporates soft constraints into energy function, we impose the local rigidity requirements as hard constraints. The constrained optimization problem is solved by nonlinear programming. The nonlinear programming formulations allow us to exploit the constraints in order to reduce the dimensionality of the optimization problem. In addition, we use dense registration framework to control the deformation at every voxel explicitly. Therefore, unconstrained voxels are not affected by the method. Experimental results from synthetic and MR images of the knee show that our method converges to the optimal solution faster and satisfies the rigidity constraints of the transformation during registration process. The result is a more realistic estimation of rigid and non-rigid deformations.
In this paper, we investigate the problem of power optimization in CMOS circuits using gate sizing and voltage selection for a given clock period specification. Several solutions have been proposed for power optimizat...
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ISBN:
(纸本)076952365X
In this paper, we investigate the problem of power optimization in CMOS circuits using gate sizing and voltage selection for a given clock period specification. Several solutions have been proposed for power optimization during gate sizing and voltage selection. Since the problem formulation is nonlinear in nature, nonlinear programming (NLP) based solutions will yield better accuracy, however, convergence is difficult for large circuits. On the other hand, heuristic solutions will result in faster but less accurate solutions. In this work, we propose a new algorithm for gate sizing and voltage selection based on NLP for power optimization. The algorithm uses gate level heuristics for delay assignment which disassociates the delays of all the paths to the individual gate level, and each gate is then separately optimized for power with its delay constraint. Since the optimization is done at the individual gate level, NLP converges quickly while maintaining accuracy. Experimental results are presented for ISCAS benchmarks which clearly illustrate the efficacy of the proposed solution.
This paper formulates the problem of packing a given set of different-sized circles into a smallest possible square box as a nonlinear programming problem and establishes the first order optimality conditions. The aug...
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ISBN:
(纸本)1424403316
This paper formulates the problem of packing a given set of different-sized circles into a smallest possible square box as a nonlinear programming problem and establishes the first order optimality conditions. The augmented lagrangian method is applied to solve this problem and the computational experiments show its effectiveness.
In this paper, an "Adaptive Receding Horizon Controller (ARHC)" is exemplified in the suboptimal control of a Furuta pendulum. A dynamic model of strongly overestimated inertia and friction parameters is use...
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ISBN:
(纸本)9781665444996
In this paper, an "Adaptive Receding Horizon Controller (ARHC)" is exemplified in the suboptimal control of a Furuta pendulum. A dynamic model of strongly overestimated inertia and friction parameters is used in an RHC controller to track the nominal trajectory under cost terms penalizing the control forces. The so obtained "optimized" trajectory is tracked by an adaptive controller that uses a realistic approximate dynamic model of the controlled system. Since the approximate and the actual model contain considerably smaller inertia and friction parameters than that used for optimization the cautiously optimized trajectory can be precisely tracked by the actual system without suffering from heavy force burdens. The adaptivity is guaranteed by a "Fixed Point Iteration"-based approach that in this manner easily can be combined with the mathematical framework of optimal controllers. Instead of using Lagrange multipliers, the optimization happens through explicitly applying the dynamic model in forward Euler integration. The operation of the method is exemplified via numerical simulations.
Three optimization problems concerning the maximization of the signal-to-interference ratio for a doubly spread target via signal design are expressed in terms of equivalent nonlinear programming problems defined on a...
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A dynamic method to solve the general nonlinear programming (NLP) problem, inspired by the Lyapunov continuous-time dynamics stability theory in the control field, is proposed. The optimal solution is regarded as the ...
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ISBN:
(纸本)9798350387780;9798350387797
A dynamic method to solve the general nonlinear programming (NLP) problem, inspired by the Lyapunov continuous-time dynamics stability theory in the control field, is proposed. The optimal solution is regarded as the stable equilibrium point of a finite-dimensional dynamic system and solved in an asymptotic manner. Under the premise that the Karush-Kuhn-Tucker (KKT) optimality condition exists, the Dynamic Optimization Equation (DOE), which has the same dimension to that of the optimization parameter vector, is established within the feasible region and its solution will converge to the optimal solution of the NLP globally. The expressions of the Lagrange multipliers and the KKT multipliers during the entire optimization process are also derived. Using the matrix pseudo-inverse, the DOE is valid even without the linearly independent regularity requirement on the nonlinear constraints. Via the control based method, it is shown that the NLP may be transformed to the Initial-value Problem (IVP) to be solved efficiently, with mature ordinary differential equation integration methods.
Linear systems of equations, with uncertainty on the parameters, play a major role in various problems in economics and finance. In this paper parametric fuzzy linear systems of the general form A1 x + b1 = A2x + b2, ...
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To reduce the effects of modeling imprecisions, in the traditional "Receding Horizon Control" (RHC) that works with finite horizon lengths, in the consecutive horizon-length cycles, the actually measured sta...
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ISBN:
(纸本)9781538646366
To reduce the effects of modeling imprecisions, in the traditional "Receding Horizon Control" (RHC) that works with finite horizon lengths, in the consecutive horizon-length cycles, the actually measured state variable is used as the starting point in the next cycle. In this design, within a horizon-length cycle, a cost function is minimized under a constraint that mathematically represents the dynamic properties of the system under control. In the "nonlinear programming" (NP) approach the state variables as well as the control signals are considered over a discrete time-resolution grid, and the solution is computed by the use of Lagrange's "Reduced Gradient" (RG) method. It provides the "estimated optimal control signals" and the "estimated optimal state variables" over this grid. The controller exerts the estimated control signals but the state variables develop according to the exact dynamics of the system. In this paper an alternative approach is suggested in which, instead of exerting the estimated control signals, the estimated optimized trajectory is adaptively tracked within the given horizon. Simulation investigations are presented for a simple "Linear Time-Invariant" (LTI) model with strongly non-linear cost and terminal cost functions. It is found that the transients of the adaptive controller that appear at the boundaries of the finite-length horizons reduce the available improvement in the tracking precision. In contrast to the traditional RHC, in which decreasing horizon length improves the tracking precision, in our case some increase in the horizon length improves the precision by giving the controller more time to compensate the effects of these transients.
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