Unscented fuzzy-controlled current statistic model and adaptive filtering for tracking maneuvering targets

A novel adaptive algorithm for tracking maneuvering targets is proposed. The algorithm is implemented with fuzzy-controlled current statistic model adaptive filtering and unscented transformation. A fuzzy system allows the filter to tune the magnitude of maximum accelerations to adapt to different target maneuvers, and unscented transformation can effectively handle nonlinear system. A bearing-only tracking scenario simulation results show the proposed algorithm has a robust advantage over a wide range of maneuvers and overcomes the shortcoming of the traditional current statistic model and adaptive filtering algorithm.     

求解阵列天线自适应滤波问题的一种调比随机逼近算法

提出了求解阵列天线自适应滤波问题的一种调比随机逼近算法.每一步迭代中,算法选取调比的带噪负梯度方向作为新的迭代方向.相比已有的其他随机逼近算法,这个算法不需要调整稳定性常数,在一定程度上解决了稳定性常数选取难的问题.数值仿真实验表明,算法优于已有的滤波算法,且比经典Robbins-Monro （RM）算法具有更好的稳定性.     

An adaptive simulated annealing algorithm

In this paper, inspired by the idea of Metropolis algorithm, a new sample adaptive simulated annealing algorithm is constructed on finite state space. This new algorithm can be considered as a substitute of the annealing of iterative stochastic schemes. The convergence of the algorithm is shown.     

Online affine model identification of nonlinear processes using a new adaptive neuro-fuzzy approach

This paper presents a new online identification algorithm to drive an adaptive affine dynamic model for nonlinear and time-varying processes. The new algorithm is devised on the basis of an adaptive neuro-fuzzy modeling approach. Two adaptive neuro-fuzzy models are sequentially identified on the basis of the most recent input-output process data to realize an online affine-type model. A series of simulation test studies has been conducted to demonstrate the efficient capabilities of the proposed algorithm to automatically identify an online affine-type model for two highly nonlinear and time-varying continuous stirred tank reactor (CSTR) benchmark problems having inherent non-affine dynamic model representations. Adequacy assessments of the identified models have been explored using different evaluation measures, including comparison with an adaptive neuro-fuzzy inference system (ANFIS) as the pioneering and the most popular adaptive neuro-fuzzy system with powerful modeling features.     

An adaptive algorithm for discrete-time differential games

This paper presents an algorithm for a player to improve his performance by adapting optimally over his non-optimally playing opponent in discrete-time differential games. The algorithm first estimates the opponent's actual strategies and then constructs an adaptive strategy for the player. The adaptive strategy is periodically updated according to the opponent's behavior using the neighboring optimal closed-loop solution technique. An example is given which demonstrates the superiority of this algorithm over the conventional one which assumes that the opponent plays optimally.     

Four-triangles adaptive algorithms for RTIN terrain meshes

We present a refinement and coarsening algorithm for the adaptive representation of Right-Triangulated Irregular Network (RTIN) meshes. The refinement algorithm is very simple and proceeds uniformly or locally in triangle meshes. The coarsening algorithm decreases mesh complexity by reducing unnecessary data points in the mesh after a given error criterion is applied. We describe the most important features of the algorithms and give a brief numerical study on the propagation associated with the adaptive scheme used for the refinement algorithm. We also present a comparison with a commercial tool for mesh simplification, Rational Reducer, showing that our coarsening algorithm offers better results in terms of accuracy of the generated meshes.     

Limiting optimal adaptive filtering with unknown disturbance covariance

The accuracy of estimating the variance of the Kalman-Bucy filter depends essentially on disturbance covariance matrices and measurement noise. The main difficulty in filter design is the lack of necessary statistical information about the useful signal and the disturbance. Filters whose parameters are tuned during active estimation are classified with adaptive filters. The problem of adaptive filtering under parametric uncertainty conditions is studied. A method for designing limiting optimal Kalman-Bucy filters in the case of unknown disturbance covariance is presented. An adaptive algorithm for estimating disturbance covariance matrices based on stochastic approximation is described. Convergence conditions for this algorithm are investigated. The operation of a limiting adaptive filter is exemplified.     

An optimally convergent adaptive mixed finite element method

We prove convergence and optimal complexity of an adaptive mixed finite element algorithm, based on the lowest-order Raviart–Thomas finite element space. In each step of the algorithm, the local refinement is either performed using simple edge residuals or a data oscillation term, depending on an adaptive marking strategy. The inexact solution of the discrete system is controlled by an adaptive stopping criterion related to the estimator.     

Expected search duration for finite backtracking adaptive search

Backtracking adaptive search is an optimisation algorithm which generalises pure adaptive search and hesitant adaptive search. This paper considers the number of iterations for which the algorithm runs, on a problem with finitely many range levels, in order to reach a sufficiently extreme objective function level. A difference equation for the expectation of this quantity is derived and solved. Several examples of backtracking adaptive search on finite problems are presented, including special cases that have received attention in earlier papers.     

Hesitant adaptive search for global optimisation

Pure adaptive search is a stochastic algorithm which has been analysed in distinct ways for finite and continuous global optimisation. In this paper, motivated by the behaviour of practical algorithms such as simulated annealing, we extend these ideas. We present a unified theory which yields both the finite and continuous results for pure adaptive search. At the same time, we allow our extended algorithm to hesitate before improvement continues. Results are obtained for the expected number of iterations to convergence for such an algorithm. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.Corresponding author.     

Neural network-based state fusion and adaptive tracking for maneuvering targets

An adaptive algorithm for tracking maneuvering targets is proposed. This algorithm is implemented with two filters and a multilayer feedforward neural network using state fusion, together with the current statistic model and adaptive filtering. The neural network fuses automatically all the state information of the two filters and tunes adaptively the system variance for one of the two filters to adapt to different target maneuvers when the two filters track the same maneuvering target in parallel. Simulation results show that the adaptive algorithm tracks very well maneuvering targets over a wide range of maneuvers with high precision, in both one and three-dimensional cases.     

An adaptive estimation of MAVE

Minimum average variance estimation (MAVE, Xia et al. (2002) [29]) is an effective dimension reduction method. It requires no strong probabilistic assumptions on the predictors, and can consistently estimate the central mean subspace. It is applicable to a wide range of models, including time series. However, the least squares criterion used in MAVE will lose its efficiency when the error is not normally distributed. In this article, we propose an adaptive MAVE which can be adaptive to different error distributions. We show that the proposed estimate has the same convergence rate as the original MAVE. An EM algorithm is proposed to implement the new adaptive MAVE. Using both simulation studies and a real data analysis, we demonstrate the superior finite sample performance of the proposed approach over the existing least squares based MAVE when the error distribution is non-normal and the comparable performance when the error is normal.     

New adaptive mixed finite element method (AMFEM)

The need to develop reliable and efficient adaptive algorithms using mixed finite element methods arises from various applications in fluid dynamics and computational continuum mechanics. In order to save degrees of freedom, not all but just some selected set of finite element domains are refined and hence the fundamental question of convergence requires a new mathematical argument as well as the question of optimality. We will present a new adaptive algorithm for mixed finite element methods to solve the model Poisson problem, for which optimal convergence can be proved. The a posteriori error control of mixed finite element methods dates back to Alonso (1996) Error estimators for a mixed method. and Carstensen (1997) A posteriori error estimate for the mixed finite element method. The error reduction and convergence for adaptive mixed finite element methods has already been proven by Carstensen and Hoppe (2006) Error Reduction and Convergence for an Adaptive Mixed Finite Element Method, Convergence analysis of an adaptive nonconforming finite element methods. Recently, Chen, Holst and Xu (2008) Convergence and Optimality of Adaptive Mixed Finite Element Methods. presented convergence and optimality for adaptive mixed finite element methods following arguments of Rob Stevenson for the conforming finite element method. Their algorithm reduces oscillations, before applying and a standard adaptive algorithm based on usual error estimation. The proposed algorithm does this in a natural way, by switching between the reduction of either the estimated error or oscillations. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

An effective adaptive trust region algorithm for nonsmooth minimization

In this paper, an adaptive trust region algorithm that uses Moreau–Yosida regularization is proposed for solving nonsmooth unconstrained optimization problems. The proposed algorithm combines a modified secant equation with the BFGS update formula and an adaptive trust region radius, and the new trust region radius utilizes not only the function information but also the gradient information. The global convergence and the local superlinear convergence of the proposed algorithm are proven under suitable conditions. Finally, the preliminary results from comparing the proposed algorithm with some existing algorithms using numerical experiments reveal that the proposed algorithm is quite promising for solving nonsmooth unconstrained optimization problems.     

A model of parameter adaptive law with time varying function for robot control

In this article, a model of adaptive control law for controlling robot manipulators using the Lyapunov based theory of guaranteed the stability of uncertain a system is derived. The novelty of obtained result is that the adaptive control algorithm is developed using a parameter estimation rule depending on manipulator kinematic, dynamic parameters and tracking error. This study is supported by a computer simulation and tracking performance has been improved.     

An adaptive memory algorithm for the k-coloring problem

Let G=(V,E) be a graph with vertex set V and edge set E. The k-coloring problem is to assign a color (a number chosen in {1,…,k}) to each vertex of G so that no edge has both endpoints with the same color. The adaptive memory algorithm is a hybrid evolutionary heuristic that uses a central memory. At each iteration, the information contained in the central memory is used for producing an offspring solution which is then possibly improved using a local search algorithm. The so obtained solution is finally used to update the central memory. We describe in this paper an adaptive memory algorithm for the k-coloring problem. Computational experiments give evidence that this new algorithm is competitive with, and simpler and more flexible than, the best known graph coloring algorithms.     

An adaptive tabu-simulated annealing for concave cost transportation problems

The transportation problem (TP) is one of the most popular network problems because of its theoretical and practical importance. If the transportation cost linearly depends on the transported amount of the product, then TP is solvable in polynomial time with linear programming methods. However, in the real world, the transportation costs are generally nonlinear, frequently concave where the unit cost for transporting products decreases as the amount of products increases. Since concave cost transportation problems (ccTPs) are NP-hard, solving large-scale problems is time consuming. In this study, we propose a hybrid algorithm based on the concepts borrowed from tabu search (TS) and simulated annealing (SA) to solve the ccTP. This algorithm, called ATSA (adaptive tabu-simulated annealing), is an SA approach supplemented with a tabu list and adaptive cooling strategy. The effectiveness of ATSA has been investigated in two stages using a set of TPs with different sizes. The first stage includes performance analysis of ATSA using SA, ASA (adaptive simulated anealing) and TS, which are basic forms of ATSA. In the second stage, ATSA has been compared with the heuristic approaches given in the literature for ccTP. Statistical analysis shows that ATSA exhibits better performance than its basic forms and heuristic approaches.     

An adaptive homotopy approach for non-selfadjoint eigenvalue problems

This paper presents adaptive algorithms for eigenvalue problems associated with non-selfadjoint partial differential operators. The basis for the developed algorithms is a homotopy method which departs from a well-understood selfadjoint problem. Apart from the adaptive grid refinement, the progress of the homotopy as well as the solution of the iterative method are adapted to balance the contributions of the different error sources. The first algorithm balances the homotopy, discretization and approximation errors with respect to a fixed stepsize τ in the homotopy. The second algorithm combines the adaptive stepsize control for the homotopy with an adaptation in space that ensures an error below a fixed tolerance ε. The outcome of the analysis leads to the third algorithm which allows the complete adaptivity in space, homotopy stepsize as well as the iterative algebraic eigenvalue solver. All three algorithms are compared in numerical examples.     

An adaptive decision maker for constrained evolutionary optimization

An adaptive decision maker (ADM) is proposed for constrained evolutionary optimization. This decision maker, which is designed in the form of an adaptive penalty function, is used to decide which solution candidate prevails in the Pareto optimal set and to choose the individuals to be replaced. By integrating the ADM with a model of a population-based algorithm-generator, a novel generic constrained optimization evolutionary algorithm is derived. The performance of the new method is evaluated by 13 well-known benchmark test functions. It is shown that the ADM has powerful ability to balance the objective function and the constraint violations, and the results obtained are very competitive to other state-of-the-art techniques referred to in this paper in terms of the quality of the resulting solutions.     

Parametric study of nonlinear adaptive control algorithm with magneto-rheological suspension systems

This paper describes the details of the simulation analysis of a nonlinear model-based adaptive suspension control system [Song X, Ahmadian M, Southward SC, Miller LR. An adaptive semiactive control algorithm for magneto-rheological suspension systems. ASME J Vibr Acoust, in press; Song X. Design of adaptive vibration control systems with application of magneto-rheological dampers. Dissertation, Virginia Tech, December, 1999]. The numerical aspect of the simulation study of a seat suspension with application of magneto-rheological dampers will be presented. Magneto-rheological (MR) dampers have strong nonlinearities such as bi-linearity, hysteresis, and saturation related to magnetism, which can be represented by appropriate mathematic functions, respectively. Thus the model-based adaptive algorithm becomes complicated because of involvement of MR damper models. One objective of this study is to investigate the effect of MR damper model simplifications on the adaptive suspension performance. Furthermore, simulation is also applied to do parametric study of adaptive algorithm parameters such as filtering and step size. The numerical results compare the proposed adaptive controller with passive dampers to validate not only its effectiveness but also obtain some guidance information for its experimental implementation.