# IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)

## Filter Results

Displaying Results 1 - 25 of 26

Publication Year: 2011, Page(s):C1 - 1173
| PDF (148 KB)
• ### IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics publication information

Publication Year: 2011, Page(s): C2
| PDF (38 KB)
• ### Stability Margin of Linear Systems With Parameters Described by Fuzzy Numbers

Publication Year: 2011, Page(s):1174 - 1182
Cited by:  Papers (4)
| | PDF (438 KB) | HTML

This paper deals with the linear systems with uncertain parameters described by fuzzy numbers. The problem of determining the stability margin of those systems with linear affine dependence of the coefficients of a characteristic polynomial on system parameters is studied. Fuzzy numbers describing the system parameters are allowed to be characterized by arbitrary nonsymmetric membership functions.... View full abstract»

• ### Bayesian Inference With Adaptive Fuzzy Priors and Likelihoods

Publication Year: 2011, Page(s):1183 - 1197
Cited by:  Papers (4)
| | PDF (897 KB) | HTML

Fuzzy rule-based systems can approximate prior and likelihood probabilities in Bayesian inference and thereby approximate posterior probabilities. This fuzzy approximation technique allows users to apply a much wider and more flexible range of prior and likelihood probability density functions than found in most Bayesian inference schemes. The technique does not restrict the user to the few known ... View full abstract»

• ### Knowledge Discovery Employing Grid Scheme Least Squares Support Vector Machines Based on Orthogonal Design Bee Colony Algorithm

Publication Year: 2011, Page(s):1198 - 1212
Cited by:  Papers (18)
| | PDF (1227 KB) | HTML

This paper proposes a concept for machine learning that integrates a grid scheme (GS) into a least squares support vector machine (LSSVM) (called GS-LSSVM) with a mixed kernel in order to solve data classification problems. The purpose of GS-LSSVM is to execute feature selections, mixed kernel applications, and parameter optimization in a learning paradigm. The proposed learning paradigm includes ... View full abstract»

• ### Decentralized Indirect Methods for Learning Automata Games

Publication Year: 2011, Page(s):1213 - 1223
Cited by:  Papers (11)
| | PDF (649 KB) | HTML

We discuss the application of indirect learning methods in zero-sum and identical payoff learning automata games. We propose a novel decentralized version of the well-known pursuit learning algorithm. Such a decentralized algorithm has significant computational advantages over its centralized counterpart. The theoretical study of such a decentralized algorithm requires the analysis to be carried o... View full abstract»

• ### Behavior Coordination of Mobile Robotics Using Supervisory Control of Fuzzy Discrete Event Systems

Publication Year: 2011, Page(s):1224 - 1238
Cited by:  Papers (15)
| | PDF (1140 KB) | HTML

In order to incorporate the uncertainty and impreciseness present in real-world event-driven asynchronous systems, fuzzy discrete event systems (DESs) (FDESs) have been proposed as an extension to crisp DESs. In this paper, first, we propose an extension to the supervisory control theory of FDES by redefining fuzzy controllable and uncontrollable events. The proposed supervisor is capable of enabl... View full abstract»

• ### Measuring Neuromuscular Control Dynamics During Car Following With Continuous Haptic Feedback

Publication Year: 2011, Page(s):1239 - 1249
Cited by:  Papers (38)
| | PDF (796 KB) | HTML

In previous research, a driver support system that uses continuous haptic feedback on the gas pedal to inform drivers of the separation to the lead vehicle was developed. Although haptic feedback has been previously shown to be beneficial, the influence of the underlying biomechanical properties of the driver on the effectiveness of haptic feedback is largely unknown. The goal of this paper is to ... View full abstract»

• ### Regression Reformulations of LLE and LTSA With Locally Linear Transformation

Publication Year: 2011, Page(s):1250 - 1262
Cited by:  Papers (18)
| | PDF (1024 KB) | HTML

Locally linear embedding (LLE) and local tangent space alignment (LTSA) are two fundamental algorithms in manifold learning. Both LLE and LTSA employ linear methods to achieve their goals but with different motivations and formulations. LLE is developed by locally linear reconstructions in both high- and low-dimensional spaces, while LTSA is developed with the combinations of tangent space project... View full abstract»

• ### A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation

Publication Year: 2011, Page(s):1263 - 1274
Cited by:  Papers (72)
| | PDF (633 KB) | HTML

In this paper, a generalized multiple-kernel fuzzy C-means (FCM) (MKFCM) methodology is introduced as a framework for image-segmentation problems. In the framework, aside from the fact that the composite kernels are used in the kernel FCM (KFCM), a linear combination of multiple kernels is proposed and the updating rules for the linear coefficients of the composite kernel are derived as well. The ... View full abstract»

• ### A Unified Approach to the Stability of Generalized Static Neural Networks With Linear Fractional Uncertainties and Delays

Publication Year: 2011, Page(s):1275 - 1286
Cited by:  Papers (66)
| | PDF (347 KB) | HTML

In this paper, the robust global asymptotic stability (RGAS) of generalized static neural networks (SNNs) with linear fractional uncertainties and a constant or time-varying delay is concerned within a novel input-output framework. The activation functions in the model are assumed to satisfy a more general condition than the usually used Lipschitz-type ones. First, by four steps of technical trans... View full abstract»

• ### Target-Motion Prediction for Robotic Search and Rescue in Wilderness Environments

Publication Year: 2011, Page(s):1287 - 1298
Cited by:  Papers (10)
| | PDF (1099 KB) | HTML

This paper presents a novel modular methodology for predicting a lost person's (motion) behavior for autonomous coordinated multirobot wilderness search and rescue. The new concept of isoprobability curves is introduced and developed, which represents a unique mechanism for identifying the target's probable location at any given time within the search area while accounting for influences such as t... View full abstract»

• ### Cerebellarlike Corrective Model Inference Engine for Manipulation Tasks

Publication Year: 2011, Page(s):1299 - 1312
Cited by:  Papers (16)
| | PDF (1498 KB) | HTML

This paper presents how a simple cerebellumlike architecture can infer corrective models in the framework of a control task when manipulating objects that significantly affect the dynamics model of the system. The main motivation of this paper is to evaluate a simplified bio-mimetic approach in the framework of a manipulation task. More concretely, the paper focuses on how the model inference proc... View full abstract»

• ### Homogenous Polynomially Parameter-Dependent $H_{ infty}$ Filter Designs of Discrete-Time Fuzzy Systems

Publication Year: 2011, Page(s):1313 - 1322
Cited by:  Papers (27)
| | PDF (432 KB) | HTML

This paper proposes a novel H∞ filtering technique for a class of discrete-time fuzzy systems. First, a novel kind of fuzzy H∞ filter, which is homogenous polynomially parameter dependent on membership functions with an arbitrary degree, is developed to guarantee the asymptotic stability and a prescribed H∞ performance of t... View full abstract»

• ### A One-Layer Recurrent Neural Network for Constrained Nonsmooth Optimization

Publication Year: 2011, Page(s):1323 - 1333
Cited by:  Papers (35)
| | PDF (718 KB) | HTML

This paper presents a novel one-layer recurrent neural network modeled by means of a differential inclusion for solving nonsmooth optimization problems, in which the number of neurons in the proposed neural network is the same as the number of decision variables of optimization problems. Compared with existing neural networks for nonsmooth optimization problems, the global convexity condition on t... View full abstract»

• ### A New Particle Swarm Algorithm and Its Globally Convergent Modifications

Publication Year: 2011, Page(s):1334 - 1351
Cited by:  Papers (40)
| | PDF (1819 KB) | HTML

Particle swarm optimization (PSO) is a population-based optimization technique that can be applied to a wide range of problems. Here, we first investigate the behavior of particles in the PSO using a Monte Carlo method. The results reveal the essence of the trajectory of particles during iterations and the reasons why the PSO lacks a global search ability in the last stage of iterations. Then, we ... View full abstract»

• ### Recurring Two-Stage Evolutionary Programming: A Novel Approach for Numeric Optimization

Publication Year: 2011, Page(s):1352 - 1365
Cited by:  Papers (8)
| | PDF (688 KB) | HTML

In the application of evolutionary algorithms (EAs) to complex problem solving, it is essential to maintain proper balance between global exploration and local exploitation to achieve a good near-optimum solution to the problem. This paper presents a recurring two-stage evolutionary programming (RTEP) to balance the explorative and exploitative features of the conventional EAs. Unlike most previou... View full abstract»

• ### Evidence-Driven Image Interpretation by Combining Implicit and Explicit Knowledge in a Bayesian Network

Publication Year: 2011, Page(s):1366 - 1381
Cited by:  Papers (6)
| | PDF (1094 KB) | HTML

Computer vision techniques have made considerable progress in recognizing object categories by learning models that normally rely on a set of discriminative features. However, in contrast to human perception that makes extensive use of logic-based rules, these models fail to benefit from knowledge that is explicitly provided. In this paper, we propose a framework that can perform knowledge-assiste... View full abstract»

• ### A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data

Publication Year: 2011, Page(s):1382 - 1394
Cited by:  Papers (13)
| | PDF (344 KB) | HTML

Automatic learning of Bayesian networks from data is a challenging task, particularly when the data are scarce and the problem domain contains a high number of random variables. The introduction of expert knowledge is recognized as an excellent solution for reducing the inherent uncertainty of the models retrieved by automatic learning methods. Previous approaches to this problem based on Bayesian... View full abstract»

• ### Analysis of the Noise Reduction Property of Type-2 Fuzzy Logic Systems Using a Novel Type-2 Membership Function

Publication Year: 2011, Page(s):1395 - 1406
Cited by:  Papers (40)
| | PDF (1049 KB) | HTML

In this paper, the noise reduction property of type-2 fuzzy logic (FL) systems (FLSs) (T2FLSs) that use a novel type-2 fuzzy membership function is studied. The proposed type-2 membership function has certain values on both ends of the support and the kernel and some uncertain values for the other values of the support. The parameter tuning rules of a T2FLS that uses such a membership function are... View full abstract»

• ### Incremental State Aggregation for Value Function Estimation in Reinforcement Learning

Publication Year: 2011, Page(s):1407 - 1416
Cited by:  Papers (5)
| | PDF (844 KB) | HTML

In reinforcement learning, large state and action spaces make the estimation of value functions impractical, so a value function is often represented as a linear combination of basis functions whose linear coefficients constitute parameters to be estimated. However, preparing basis functions requires a certain amount of prior knowledge and is, in general, a difficult task. To overcome this difficu... View full abstract»

• ### Accurate Landmarking of Three-Dimensional Facial Data in the Presence of Facial Expressions and Occlusions Using a Three-Dimensional Statistical Facial Feature Model

Publication Year: 2011, Page(s):1417 - 1428
Cited by:  Papers (21)
| | PDF (899 KB) | HTML

Three-dimensional face landmarking aims at automatically localizing facial landmarks and has a wide range of applications (e.g., face recognition, face tracking, and facial expression analysis). Existing methods assume neutral facial expressions and unoccluded faces. In this paper, we propose a general learning-based framework for reliable landmark localization on 3-D facial data under challenging... View full abstract»

• ### Gait-Based Gender Classification Using Mixed Conditional Random Field

Publication Year: 2011, Page(s):1429 - 1439
Cited by:  Papers (13)
| | PDF (1234 KB) | HTML

This paper proposes a supervised modeling approach for gait-based gender classification. Different from traditional temporal modeling methods, male and female gait traits are competitively learned by the addition of gender labels. Shape appearance and temporal dynamics of both genders are integrated into a sequential model called mixed conditional random field (CRF) (MCRF), which provides an open ... View full abstract»

• ### Reply to Comments on "State-Feedback Control of Fuzzy Discrete-Event Systems"

Publication Year: 2011, Page(s): 1440
| | PDF (52 KB) | HTML

This paper is a reply to comments on "State-feedback control of fuzzy discrete-event systems" by Yongzhi Cao et l., (2011) for pointing out an error in our paper "State-feedback control of fuzzy discrete event systems" (June 2010). The error is caused by the situation in which a state-feedback control may generate a set of states while not allowing some transitions in the set of states generated. ... View full abstract»

• ### IEEE Systems, Man, and Cybernetics Society Information

Publication Year: 2011, Page(s): C3
| PDF (28 KB)

## Aims & Scope

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics focuses on cybernetics, including communication and control across humans, machines and organizations at the structural or neural level

This Transactions ceased production in 2012. The current retitled publication is IEEE Transactions on Cybernetics.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Dr. Eugene Santos, Jr.
Thayer School of Engineering
Dartmouth College