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Systems, Man and Cybernetics, IEEE Transactions on

Issue 11 • Date Nov 1995

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Displaying Results 1 - 9 of 9
  • Convergence of teams and hierarchies of learning automata in connectionist systems

    Page(s): 1459 - 1469
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    Learning algorithms for feedforward connectionist systems in a reinforcement learning environment are developed and analyzed in this paper. The connectionist system is made of units of groups of learning automata. The learning algorithm used is the LR-I and the asymptotic behavior of this algorithm is approximated by an ordinary differential equation (ODE) for low values of the learning parameter. This is done using weak convergence techniques. The reinforcement learning model is used to pose the goal of the system as a constrained optimization problem. It is shown that the ODE, and hence the algorithm exhibits local convergence properties, converging to local solutions of the related optimization problem. The three layer pattern recognition network is used as an example to show that the system does behave as predicted and reasonable rates of convergence are obtained. Simulations also show that the algorithm is robust to noise View full abstract»

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  • Image restoration using recursive estimators

    Page(s): 1470 - 1482
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    In this paper, edge preserving recursive estimators are proposed For restoring images corrupted by noise. Edge detection using a 5×5 Graeco-Latin squares (GLS) mask is carried out as the first step for preserving edges. The GLS mask preprocessor determines the orientation of edges in horizontal, vertical, 45° diagonal, or 135° diagonal directions. The actual removal of noise is done in the second step. If the noise is Gaussian, the center pixel in the 5×5 mask is estimated using a multiple linear regression model fitted to the noisy image on the same side of the edge. The parameters of the regression model are estimated using the least squares estimator. The least squares estimator is made recursive using the Robbins-Monro stochastic approximation (RMSA) algorithm. The RMSA guarantees convergence of the estimate in the mean square sense and with probability one. If the Gaussian noise is contaminated by a small percentage of heavy tailed (impulsive) noise (salt and pepper noise), the recursive least square estimator is robustized using a symmetrical version of Wilcoxon signed rank statistic. The GLS mask for edge detection uses an F-ratio test which is robust for small deviations from normality assumption of the noise. The mathematical properties and various forms of convergence of the robustized algorithm are shown in the appendix. The efficacy of the proposed restoration procedures are demonstrated on two types of images (“girl” and “house”) View full abstract»

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  • Automated fault detection and accommodation: a learning systems approach

    Page(s): 1447 - 1458
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    The detection, diagnosis, and accommodation of system failures or degradations are becoming increasingly more important in modern engineering problems. A system failure often causes changes in critical system parameters, or even, changes in the nonlinear dynamics of the system. This paper presents a general framework for constructing automated fault diagnosis and accommodation architectures using on-line approximators and adaptation/learning schemes. In this framework, neural network models constitute an important class of on-line approximators. Changes in the system dynamics are monitored by an on-line approximation model, which is used not only for detecting but also for accommodating failures. A systematic procedure for constructing nonlinear estimation algorithms is developed, and a stable learning scheme is derived using Lyapunov theory. Simulation studies are used to illustrate the results and to gain intuition into the selection of design parameters View full abstract»

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  • A framework for on-line learning of plant models and control policies for restructurable control

    Page(s): 1502 - 1512
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    In this paper a learning framework to deal with restructurable control of a single-output dynamic plant is proposed. The central concept used to represent the restructurable behavior of the plant, and subsequently for the design of the framework, is the behavioral graph. The nodes of this graph correspond to possible local behaviors of the system while its edges model the switching scheme of the plant among its local behaviors. In the definition of this concept, general dynamical system theory is used. The framework is able to learn the dynamics (models) of a reconfigurable system, select appropriate models, and ultimately control the plant according to given specifications. The framework design borrows concepts and techniques from the active fields of adaptive and learning control. The underlying ideas and the software prototype implementing the framework design are tested through a series of simulated experiments. The simulations demonstrate the feasibility of the approach for controlling plants with unexpectedly and structurally changing behaviors in moderately noisy environments. They also identify a number of constraints that have to be satisfied for successful operation of the framework. This paper also discusses further validation of the approach, real-time application issues, and potential enhancements of the framework's functionality View full abstract»

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  • The improved compact QP method for resolving manipulator redundancy

    Page(s): 1521 - 1530
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    The compact QP method is an effective and efficient algorithm for resolving the manipulator redundancy under inequality constraints. In this paper, a more computationally efficient scheme which will improve the efficiency of the compact QP method-the improved compact BP method-is developed. With the technique of work space decomposition, the redundant inverse kinematics problem can be decomposed into two subproblems. Thus, the size of the redundancy problem can be reduced. For an n degree-of-freedom spatial redundant manipulator, instead of a 6×n matrix, only a 3×(n-3) matrix is needed to be manipulated by Gaussian elimination with partial pivoting for selecting the free variables. The simulation results on the CESAR manipulator indicate that the speedup of the compact QP method as compared with the original QP method is about 3.3. Furthermore, the speedup of the improved compact QP method is about 5.6. Therefore, it is believed that the improved compact QP method is one of the most efficient and effective optimization algorithm for resolving the manipulator redundancy under inequality constraints View full abstract»

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  • Design of process parameters using robust design techniques and multiple criteria optimization

    Page(s): 1437 - 1446
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    This paper presents a methodology for the design of products/processes that makes use of the concepts of robust design and the techniques of multiple criteria optimization for simultaneously optimizing many quality characteristics. First, a systematic approach to the selection of an efficient matrix experiment for a design problem is presented. Appropriate performance measures are obtained so that their joint optimization results in the minimum variation of product characteristics. The use of transformations is highlighted as a useful technique to statistically validate the design process. A discrete multiple criteria optimization algorithm that incorporates the methods of dominated approximations and reference points is developed to obtain nondominated solutions for the design problem. The methodology is illustrated using a case study gleaned from the literature View full abstract»

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  • Explaining control strategies in second generation expert systems

    Page(s): 1483 - 1490
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    Explaining control strategies is an important aspect in second generation expert system explanation. With explicit representation of control, it is possible to construct explanations at different levels of abstraction so as to satisfy different needs. This paper presents the authors, work on explaining control strategies within the context of the system tool, CARMEN. CARMEN's methodology for modeling control knowledge (MMCK) uses problem-solving entities (i.e., tasks, meta-knowledge sources (MKSs), basic-knowledge sources (BKSs) and engines) to integrate different kinds of knowledge and reasoning. These entities reflect the ways experts organize their problem-solving activities. Explanations about control strategies can be clearly given according to the roles these entities play in problem solving View full abstract»

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  • Multiobjective heuristic search in AND/OR graphs

    Page(s): 1513 - 1521
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    Develops and analyzes a heuristic search algorithm that determines the nondominated set of solution graphs for a multiobjective AND/OR graph. This algorithm, MOAO*, is a multiobjective generalization of AO*. MOAO* uses sets of vector-valued heuristic estimates to give guidance to the search. The authors show that MOAO* satisfies termination, completeness, and admissibility conditions, generalizing results associated with AO*. Further, the authors prove that if the heuristic sets satisfy a monotonicity condition, then MOAO* possesses an efficiency property reminiscent of a well-known result associated with A* View full abstract»

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  • Simple direction-dependent rhythmic movements and partial somesthesis of a marionette

    Page(s): 1491 - 1501
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    The simple rhythmic movements of a multi-link sagittal marionette with many muscle-like actuators are considered in this paper. The marionette is standing on the ground, and contact with surrounding objects is not permitted. Every actuator has two inputs: a firing rate, analogous to the collective action of the alpha motoneurons of a muscle, and a threshold signal, analogous to the effective action of the gamma motoneurons that excite the sensory organ of the natural muscle-the spindle. The system possesses intrinsic position and velocity feedback due to the structure of its actuators, and extrinsic feedback with transmission delays between the actuators and the control system. The extrinsic feedback is nonlinear and is fashioned after the spindle response in natural systems. Force and length sensors convey information from which the angular position of the marionette is estimated by simultaneous solution of a redundant set of equations. Thus, the marionette is endowed with partial somesthesis: awareness of the whereabouts of its limbs. A control strategy for simple rhythmic movements is developed. This is a preliminary effort to develop an analytical structure for a multiactuator system. The long range findings may shed some light on the elaborate control structure of the central nervous system in natural systems View full abstract»

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