Issue 2 • May 1998
Cited by: Papers (23)
The paper describes a framework for task sequence planning for a generalized robotic work cell. The AND/OR net provides a compact, distributed, domain-specific representation of geometric configurations of parts and devices in the work cell. The approach maintains a correspondence from geometric state information to task and motion plans and on-line discrete-event control that is not available in ... View full abstract»
Cited by: Papers (22) | Patents (9)
The data exploration task can be divided into three interrelated subtasks: 1) feature selection, 2) discovery, and 3) interpretation. This paper describes an unsupervised discovery method with biases geared toward partitioning objects into clusters that improve interpretability. The algorithm ITERATE employs: 1) a data ordering scheme and 2) an iterative redistribution operator to produce maximall... View full abstract»
Cited by: Papers (5)
Compartmental modeling is an approach to dynamic systems analysis that has proven useful in enhancing the understanding of biological and ecological systems. The compartmental approach emphasizes model conceptualization and is especially appropriate in applications in which quantitative behavioral data are difficult or expensive to obtain and in which qualitative understanding is the primary goal.... View full abstract»
Cited by: Papers (17)
Electrical drives are usually modeled using circuit theory, with currents or linking fluxes chosen as state variables for its electrical part and rotor speed or position chosen for its mechanical part. Often, its internal structure contains nonlinear relations which are difficult to model such as dead-time, hysteresis, and saturation effects. On the contrary, if the available model is accurate eno... View full abstract»
Cited by: Papers (2)
Many real-world decision-making problems fall into the general category of classification. Algorithms for constructing knowledge by inductive inference from example have been widely used for some decades. Although these learning algorithms frequently address the same problem of learning from preclassified examples and much previous work in inductive learning has focused on the algorithms' predicti... View full abstract»
Cited by: Papers (30)
An adaptive fusion algorithm is proposed for an environment where the observations and local decisions are dependent from one sensor to another. An optimal decision rule, based on the maximum posterior probability (MAP) detection criterion for such an environment, is derived and compared to the adaptive approach. In the algorithm, the log-likelihood ratio function can be expressed as a linear comb... View full abstract»
An experimental approach to robotic grasping using a connectionist architecture and generic grasping functionsPublication Year: 1998, Page(s):239 - 253
Cited by: Papers (17)
An experimental approach to robotic grasping is presented. This approach is based on developing a generic representation of grasping rules, which allows learning them from experiments between the object and the robot. A modular connectionist design arranged in subsumption layers is used to provide a mapping between sensory inputs and robot actions. Reinforcement feedback is used to select between ... View full abstract»
Cited by: Papers (11)
In their previous work, the authors have developed a method for selecting features based on the analysis of class regions approximated by hyperboxes. They select features analyzing class regions approximated by ellipsoids. First, for a given set of features, each class region is approximated by an ellipsoid with the center and the covariance matrix calculated by the data belonging to the class. Th... View full abstract»
Cited by: Papers (12)
A neural network based fuzzy set model is proposed to support organizational decision making under uncertainty. This model incorporates three theories and methodologies: classical decision-making theory under conflict, as suggested by Luce and Raiffa (1957), the fuzzy set theory of Zadeh (1965, 1984), and a modified version of the backpropagation (BP) neural network algorithm originated by Rumelha... View full abstract»
The relationship between quantizability and learning complexity in multilayer neural networks is examined. In a special neural network architecture that calculates node activations according to the certainty factor (CF) model of expert systems, the analysis based upon quantizability leads to lower and also better estimates for generalization dimensionality and sample complexity than those suggeste... View full abstract»
Cited by: Papers (15)
Modeling nonlinear systems by neural networks and fuzzy systems encounters problems such as the conflict between overfitting and good generalization and low reliability, which requires a great number of fuzzy rules or neural nodes and uses very complicated learning algorithms. A new adaptive fuzzy inference system, combined with a learning algorithm, is proposed to cope with these problems. First,... View full abstract»
Cited by: Papers (136)
The paper presents a learning procedure for optimizing the parameters in the evidence-theoretic k-nearest neighbor rule, a pattern classification method based on the Dempster-Shafer theory of belief functions. In this approach, each neighbor of a pattern to be classified is considered as an item of evidence supporting certain hypotheses concerning the class membership of that pattern. Based on thi... View full abstract»
Cited by: Papers (9) | Patents (3)
A document retrieval system mainly consists of three components: document representation, user queries, and document evaluation. Each component may involve some uncertainties. Fuzzy set theory is a natural approach to coping with the representation of documents, queries, and the relevance of documents to a given query. The authors propose a fuzzy document retrieval model on the World Wide Web (WWW... View full abstract»
Cited by: Papers (18) | Patents (1)
The paper summarizes recent results on both binary and M-ary distributed hypothesis testing problems with decision makers (DMs) organized in structured decision networks. The general problem of finding an optimal organizational structure and decision strategy for such networks is formulated as a functional optimization problem. A normative model to study the effect of interactions between task str... View full abstract»
Aims & Scope
This Transactions ceased production in 2012. The current retitled publication is IEEE Transactions on Human-Machine Systems.
Overview, tutorial and application papers concerning all areas of interest to the SMC Society: systems engineering, human factors and human machine systems, and cybernetics and computational intelligence.
Authors should submit human-machine systems papers to the IEEE Transactions on Human-Machine Systems.
Authors should submit systems engineering papers to the IEEE Transactions on Systems, Man and Cybernetics: Systems.
Authors should submit cybernetics papers to the IEEE Transactions on Cybernetics.
Authors should submit social system papers to the IEEE Transactions on Computational Social Systems.
Meet Our Editors
Dr. Vladimir Marik
(until 31 December 2012)