By Topic

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Issue 1 • Date Feb 2001

Filter Results

Displaying Results 1 - 16 of 16
  • Robust adaptive fuzzy-neural control of nonlinear dynamical systems using generalized projection update law and variable structure controller

    Page(s): 140 - 147
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (300 KB)  

    In this paper, a robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance, and modeling errors. A generalized projection update law, which generalizes the projection algorithm modification and the switching-σ adaptive law, is used to tune the adjustable parameters for preventing parameter drift and confining states of the system to the specified regions. Moreover, a variable structure control method is incorporated into the control law so that the derived controller is robust with respect to unmodeled dynamics, disturbances, and modeling errors. To demonstrate the effectiveness of the proposed method, several examples are illustrated in this paper View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A dual neural network for kinematic control of redundant robot manipulators

    Page(s): 147 - 154
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (264 KB)  

    The inverse kinematics problem in robotics can be formulated as a time-varying quadratic optimization problem. A new recurrent neural network, called the dual network, is presented in this paper. The proposed neural network is composed of a single layer of neurons, and the number of neurons is equal to the dimensionality of the workspace. The proposed dual network is proven to be globally exponentially stable. The proposed dual network is also shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Determining equivalent values for possibilistic variables

    Page(s): 19 - 31
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (328 KB)  

    We discuss the nature of possibilistic uncertainty. Two approaches to the determination of the equivalent value for a possibilistically uncertain variable are introduced. These approaches are then generalized to yield a parameterized class of functions for obtaining the equivalent value of a possibilistic variable. A novel aspect of this class is that it is parameterized by a weak ordering. A discussion to try to understand the semantics of this parameterizing ordering is included. A method is suggested for obtaining the weak ordering directly from a scalar value. We briefly look at the issue of variance in possibilistic uncertainty View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A recurrent neural fuzzy network for word boundary detection in variable noise-level environments

    Page(s): 84 - 97
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (412 KB)  

    This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed. In fact, the background noise level may vary during the procedure of recording. This is the major reason that most robust word boundary detection algorithms cannot work well in the condition of variable background noise level. In order to solve this problem, we first propose a refined time-frequency (RTF) parameter for extracting both the time and frequency features of noisy speech signals. The RTF parameter extends the (time-frequency) TF parameter proposed by Junqua et al. from single band to multiband spectrum analysis, where the frequency bands help to make the distinction between speech signal and noise clear. The RTF parameter can extract useful frequency information. Based on this RTF parameter, we further propose a new word boundary detection algorithm by using a recurrent self-organizing neural fuzzy inference network (RSONFIN). Since RSONPIN can process the temporal relations, the proposed RTF-based RSONFIN algorithm can find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level. As compared to normal neural networks, the RSONFIN can always find itself an economic network size with high-learning speed. Due to the self-learning ability of RSONFIN, this RTF-based RSONFIN algorithm avoids the need for empirically determining ambiguous decision rules in normal word boundary detection algorithms. Experimental results show that this new algorithm achieves higher recognition rate than the TF-based algorithm which has been shown to outperform several commonly used word boundary detection algorithms by about 12% in variable background noise level condition, It also reduces the recognition error rate due to endpoint detection to about 23%, compared to an average of 47% obtained by the TF-based algorithm in the same condition View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Model predictive control using fuzzy decision functions

    Page(s): 54 - 65
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (232 KB)  

    Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the control problem is combined by using a decision function from the theory of fuzzy sets. This paper investigates the use of fuzzy decision making (FDM) in model predictive control (MPG), and compares the results to those obtained from conventional MPG. Attention is also paid to the choice of aggregation operators for fuzzy decision making in control. Experiments on a nonminimum phase, unstable linear system, and on an air-conditioning system with nonlinear dynamics are studied. It is shown that the performance of the model predictive controller can be improved by the use of fuzzy criteria in a fuzzy decision making framework View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Membership function modification of fuzzy logic controllers with histogram equalization

    Page(s): 125 - 132
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (296 KB)  

    In most fuzzy logic controllers (FLCs), initial membership functions (MFs) are normally laid evenly all across the universes of discourse (UD) that represent fuzzy control inputs. However, for evenly distributed MFs, there exists a potential problem that may adversely affect the control performance; that is, if the actual inputs are not equally distributed, but instead concentrate within a certain interval that is only part of the entire input area, this will result in two negative effects. On one hand, the MFs staying in the dense-input area will not be sufficient to react precisely to the inputs, because these inputs are too close to each other compared to the MFs in this area. The same fuzzy control output could be triggered for several different inputs. On the other hand, some of the MFs assigned for the sparse-input area are “wasted”. In this paper we argue that, if we arrange the placement of these MFs according to a statistical study of feedback errors in a closed-loop system, we can expect a better control performance. To this end, we introduce a new mechanism to modify the evenly distributed MFs with the help of a technique termed histogram equalization. The histogram of the errors is actually the spatial distribution of real-time errors of the control system. To illustrate the proposed MF modification approach, a computer simulation of a simple system that has a known mathematical model is first analyzed, leading to our understanding of how this histogram-based modification mechanism functions. We then apply this method to an experimental laser tracking system to demonstrate that in real-world applications, a better control performance can he obtained by using this proposed technique View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Document retrieval using fuzzy-valued concept networks

    Page(s): 111 - 118
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (228 KB)  

    This paper presents a new method for document retrieval using fuzzy-valued concept networks, where the relevant degrees between the concepts in a fuzzy-valued concept network are represented by arbitrary shapes of fuzzy numbers. There are two kinds of relevant relationships between any two concepts in a fuzzy-valued concept network, i.e., fuzzy positive association and fuzzy negative association. The relevant matrices and the relationship matrices are used to model the fuzzy-valued concept network. The elements in a relevant matrix represent the relevant degrees between concepts. The elements in a relationship matrix represent the relevant relationships between concepts. Furthermore, ne also allow users' queries to be represented by arbitrary shapes of fuzzy numbers and to use fuzzy positive association relationship and fuzzy negative association relationship for formulating their queries for increasing the flexibility of fuzzy information retrieval systems. We also present an information retrieval method in the Internet environment based on the network-type fuzzy-valued concept network architecture View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Kinematic control of redundant robots and the motion optimizability measure

    Page(s): 155 - 160
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (188 KB)  

    This paper treats the kinematic control of manipulators with redundant degrees of freedom. We derive an analytical solution for the inverse kinematics that provides a means for accommodating joint velocity constraints in real time. We define the motion optimizability measure and use it to develop an efficient method for the optimization of joint trajectories subject to multiple criteria. An implementation of the method for a 7-dof experimental redundant robot is present View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms

    Page(s): 32 - 53
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (384 KB)  

    In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design, View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Abstraction and specialization of information granules

    Page(s): 106 - 111
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (184 KB)  

    In his paper, we introduce a model of generalization and specialization of information granules. The information granules themselves are modeled as fuzzy sets or fuzzy relations. The generalization is realized by or-ing fuzzy sets while the specialization is completed through logic and operation. These two logic operators are realized using triangular norms (that is t- and a-norms). We elaborate on two (top-down and bottom-up) strategies of constructing information granules that arise as results of generalization and specialization. Various triangular norms are experimented with and some conclusions based on numeric studies are derived View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A recurrent neural network for minimum infinity-norm kinematic control of redundant manipulators with an improved problem formulation and reduced architecture complexity

    Page(s): 98 - 105
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (196 KB)  

    This paper presents an improved neural computation where scheme for kinematic control of redundant manipulators based on infinity-norm joint velocity minimization. Compared with a previous neural network approach to minimum infinity-non kinematic control, the present approach is less complex in terms of cost of architecture. The recurrent neural network explicitly minimizes the maximum component of the joint velocity vector while tracking a desired end-effector trajectory. The end-effector velocity vector for a given task is fed into the neural network from its input and the minimum infinity-norm joint velocity vector is generated at its output instantaneously. Analytical results are given to substantiate the asymptotic stability of the recurrent neural network. The simulation results of a four-degree-of-freedom planar robot arm and a seven-degree-of-freedom industrial robot are presented to show the proposed neural network can effectively compute the minimum infinity-norm solution to redundant manipulators View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Synthesis of fuzzy model-based designs to synchronization and secure communications for chaotic systems

    Page(s): 66 - 83
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (388 KB)  

    This paper presents synthesis approaches for synchronization and secure communications of chaotic systems by using fuzzy model-based design methods. Many well-known continuous and discrete chaotic systems can be exactly represented by T-S fuzzy models with only one premise variable. According to the applications on synchronization and signal modulation, the general fuzzy models may have either i) common bias terms; or ii) the same premise variable and driving signal. Then we propose two types of driving signals, namely, fuzzy driving signal and crisp driving signal, to deal with the asymptotical synchronization and secure communication problems for cases i) and ii), respectively. Based on these driving signals, the solutions are found by solving LMI problems. It is worthy to note that many well-known chaotic systems, such as Duffing system, Chua's circuit. Rassler's system, Lorenz system, Henon map, and Lozi map can achieve their applications on asymptotical synchronization and recovering messages in secure communication by using either the fuzzy driving signal or the crisp driving signal. Finally, several numerical simulations are shown to verify the results View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Stability analysis and synthesis for an affine fuzzy system via LMI and ILMI: discrete case

    Page(s): 132 - 140
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (264 KB)  

    This paper develops a stability analysis and controller synthesis methodology for a discrete affine fuzzy system based on the convex optimization techniques. In analysis, the stability condition under which the affine fuzzy system is quadratically stable is derived. Then, the condition Is recast in the formulation of Linear Matrix Inequalities (LMI) and numerically addressed. The emphasis of this paper, however, is on the synthesis of fuzzy controller based on the derived stability condition. In synthesis, the stabilizability condition turns out to be in the formulation of nonconvex matrix inequalities and is solved numerically in an iterative manner. Discrete iterative LMI (ILMI) approach is proposed to obtain the feasible solution for the synthesis of the affine fuzzy system. Finally, the applicability of the suggested methodology is demonstrated via some examples and computer simulations View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Knowledge discovery in time series databases

    Page(s): 160 - 169
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (204 KB)  

    Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. In this correspondence, we introduce a general methodology for knowledge discovery in TSDB. The process of knowledge discovery in TSDR includes cleaning and filtering of time series data, identifying the most important predicting attributes, and extracting a set of association rules that can be used to predict the time series behavior in the future. Our method is based on signal processing techniques and the information-theoretic fuzzy approach to knowledge discovery. The computational theory of perception (CTP) is used to reduce the set of extracted rules by fuzzification and aggregation. We demonstrate our approach on two types of time series: stock-market data and weather data View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Improved one-shot learning for feedforward associative memories with application to composite pattern association

    Page(s): 119 - 125
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (268 KB)  

    The local identical index (LII) associative memory (AM) proposed by the authors in a previous paper is a one-shot feedforward structure designed to exhibit no spurious attractors. In this paper we relax the latter design constraint in exchange for enlarged basins of attraction and we develop a family of modified LII AM networks that exhibit improved performance, particularly in memorizing highly correlated patterns. The new algorithm meets the requirement of no spurious attractors only in a local sense. Finally, we show that the modified LII family of networks can accommodate composite patterns of any size by storing (memorizing) only the basic (prime) prototype patterns. The latter property translates to low learning complexity and a simple network structure with significant memory savings. Simulation studies and comparisons illustrate and support the the optical developments View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming

    Page(s): 1 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (420 KB)  

    In this paper, the advantages of a fuzzy representation in problem solving and search is investigated using the framework of Cultural algorithms (CAs). Since all natural languages contain a fuzzy component, the natural question is “Does this fuzzy representation facilitate the problem-solving process, within these systems”. In order to investigate this question we use the CA framework of Reynolds (1996), CAs are a computational model of cultural evolution derived from and used to express basic anthropological models of culture and its development. A mathematical model of a full fuzzy CA is developed there. In it, the problem solving knowledge is represented using a fuzzy framework. Several theoretical results concerning its properties are presented. The model is then applied to the solution of a set of 12 difficult, benchmark problems in nonlinear real-valued function optimization. The performance of the full fuzzy model is compared with 8 other fuzzy and crisp architectures. The results suggest that a fuzzy approach can produce a statistically significant improvement in search efficiency over nonfuzzy versions for the entire set of functions, the then investigate the class of performance functions for which the full fuzzy system exhibits the greatest improvements over nonfuzzy systems. In general, these are functions which require some preliminary investigation in order to embark on an effective search View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

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 Transaction 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