The Experts below are selected from a list of 11919 Experts worldwide ranked by ideXlab platform

Jacek M Zurada - One of the best experts on this subject based on the ideXlab platform.

  • a fuzzy relational rule network modeling of electromyographical activity of trunk muscles in manual lifting based on trunk angels moments pelvic tilt and rotation angles
    International Journal of Industrial Ergonomics, 2006
    Co-Authors: Waldemar Karwowski, Adam E Gaweda, Jacek M Zurada, William S Marras, Kermit G Davis, David Rodrick
    Abstract:

    Abstract The main objective of the study was to model the electromyographic (EMG) responses for 10 trunk muscles in manual-lifting tasks using the fuzzy relational rule network (FRRN). The FRRN utilized trunk-related variables, including sagittal and lateral trunk moments, pelvic tilt and pelvic rotation angles, and sagittal, lateral, and twist trunk angles as model Inputs. The EMG data for model training and testing were randomly selected from a set collected for 20 college students. The data represented a total of 24 combinations of weight lifted (15, 30, 50 lbs), asymmetry (0°, 60°), and the origin and destination of lift (floor-waist, floor-102 cm, knee-waist, knee-102 cm), with two replications of each condition. The primary data-driven fuzzy model with relational Input Partition was trained using the laboratory EMG data for 10 subjects, and was then tested based on the EMG data for another 10 subjects. The model allowed for estimating EMG responses for the 10 trunk muscles with the average value of mean absolute error (MAE) of 9.9% (SD=1.44%). This study demonstrates that application of fuzzy modeling techniques allows for estimating time domain EMG responses of trunk muscles due to manual lifting under limited task conditions. Relevance to industry Estimation of EMG responses using the proposed fuzzy-based system opens new opportunities for biomechanical modeling of manual-lifting tasks aimed at prevention of low back disorders at the workplace.

  • data driven design of fuzzy system with relational Input Partition
    IEEE International Conference on Fuzzy Systems, 2001
    Co-Authors: Adam E Gaweda, Jacek M Zurada
    Abstract:

    An approach to data-driven linguistic modeling is presented. The methodology is based on a fuzzy system with relational Input Partition that allows for transparent modeling of linear dependencies between the Inputs. An identification algorithm for this type of fuzzy system is proposed. It automatically finds the strongest dependencies from numerical data. An application example illustrates the usefulness of the proposed approach.

Chia-feng Juang - One of the best experts on this subject based on the ideXlab platform.

  • Temporal problems solved by dynamic fuzzy network based on genetic algorithm with variable-length chromosomes
    Fuzzy Sets and Systems, 2004
    Co-Authors: Chia-feng Juang
    Abstract:

    Abstract In this paper, a dynamic fuzzy network and its design based on genetic algorithm with variable-length chromosomes is proposed. First, the dynamic fuzzy network constituted from a series of dynamic fuzzy if–then rules is proposed. One characteristic of this network is its ability to deal with temporal problems. Then, the proposed genetic algorithm with variable-length chromosomes is adopted into the design process as a means of allowing the application of the network in situations where the actual desired output is unavailable. In the proposed genetic algorithm, the length of each chromosome varies with the number of rules coded in it. Using this algorithm, no pre-assignment of the number of rules in the dynamic fuzzy network is required, since it can always help to find the most suitable number of rules. All free parameters in the network, including the spatial Input Partition, consequent parameters and feedback connection weights, are tuned concurrently. To further promote the design performance, genetic algorithm with variable-length chromosomes and relative-based mutated reproduction operation is proposed. In this algorithm, the elite individuals are directly reproduced to the next generation only when their averaged similarity value is smaller than a similarity threshold; otherwise, the elites are mutated to the next generation. To show the efficiency of this dynamic fuzzy network designed by genetic algorithm with variable-length chromosomes and relative-based mutated reproduction operation, two temporal problems are simulated. The simulated results and comparisons with recurrent neural and fuzzy networks verify the efficacy and efficiency of the proposed approach.

  • a recurrent self organizing neural fuzzy inference network
    IEEE Transactions on Neural Networks, 1999
    Co-Authors: Chia-feng Juang, Chinteng Lin
    Abstract:

    A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes initially in the RSONFIN. They are created online via concurrent structure identification and parameter identification. The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of fuzzy rules generated, a flexible Input Partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made. Efficiency of the RSONFIN is verified from these results.

Adam E Gaweda - One of the best experts on this subject based on the ideXlab platform.

  • a fuzzy relational rule network modeling of electromyographical activity of trunk muscles in manual lifting based on trunk angels moments pelvic tilt and rotation angles
    International Journal of Industrial Ergonomics, 2006
    Co-Authors: Waldemar Karwowski, Adam E Gaweda, Jacek M Zurada, William S Marras, Kermit G Davis, David Rodrick
    Abstract:

    Abstract The main objective of the study was to model the electromyographic (EMG) responses for 10 trunk muscles in manual-lifting tasks using the fuzzy relational rule network (FRRN). The FRRN utilized trunk-related variables, including sagittal and lateral trunk moments, pelvic tilt and pelvic rotation angles, and sagittal, lateral, and twist trunk angles as model Inputs. The EMG data for model training and testing were randomly selected from a set collected for 20 college students. The data represented a total of 24 combinations of weight lifted (15, 30, 50 lbs), asymmetry (0°, 60°), and the origin and destination of lift (floor-waist, floor-102 cm, knee-waist, knee-102 cm), with two replications of each condition. The primary data-driven fuzzy model with relational Input Partition was trained using the laboratory EMG data for 10 subjects, and was then tested based on the EMG data for another 10 subjects. The model allowed for estimating EMG responses for the 10 trunk muscles with the average value of mean absolute error (MAE) of 9.9% (SD=1.44%). This study demonstrates that application of fuzzy modeling techniques allows for estimating time domain EMG responses of trunk muscles due to manual lifting under limited task conditions. Relevance to industry Estimation of EMG responses using the proposed fuzzy-based system opens new opportunities for biomechanical modeling of manual-lifting tasks aimed at prevention of low back disorders at the workplace.

  • data driven design of fuzzy system with relational Input Partition
    IEEE International Conference on Fuzzy Systems, 2001
    Co-Authors: Adam E Gaweda, Jacek M Zurada
    Abstract:

    An approach to data-driven linguistic modeling is presented. The methodology is based on a fuzzy system with relational Input Partition that allows for transparent modeling of linear dependencies between the Inputs. An identification algorithm for this type of fuzzy system is proposed. It automatically finds the strongest dependencies from numerical data. An application example illustrates the usefulness of the proposed approach.

Chinteng Lin - One of the best experts on this subject based on the ideXlab platform.

  • a recurrent self organizing neural fuzzy inference network
    IEEE Transactions on Neural Networks, 1999
    Co-Authors: Chia-feng Juang, Chinteng Lin
    Abstract:

    A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes initially in the RSONFIN. They are created online via concurrent structure identification and parameter identification. The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of fuzzy rules generated, a flexible Input Partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made. Efficiency of the RSONFIN is verified from these results.

David Rodrick - One of the best experts on this subject based on the ideXlab platform.

  • a fuzzy relational rule network modeling of electromyographical activity of trunk muscles in manual lifting based on trunk angels moments pelvic tilt and rotation angles
    International Journal of Industrial Ergonomics, 2006
    Co-Authors: Waldemar Karwowski, Adam E Gaweda, Jacek M Zurada, William S Marras, Kermit G Davis, David Rodrick
    Abstract:

    Abstract The main objective of the study was to model the electromyographic (EMG) responses for 10 trunk muscles in manual-lifting tasks using the fuzzy relational rule network (FRRN). The FRRN utilized trunk-related variables, including sagittal and lateral trunk moments, pelvic tilt and pelvic rotation angles, and sagittal, lateral, and twist trunk angles as model Inputs. The EMG data for model training and testing were randomly selected from a set collected for 20 college students. The data represented a total of 24 combinations of weight lifted (15, 30, 50 lbs), asymmetry (0°, 60°), and the origin and destination of lift (floor-waist, floor-102 cm, knee-waist, knee-102 cm), with two replications of each condition. The primary data-driven fuzzy model with relational Input Partition was trained using the laboratory EMG data for 10 subjects, and was then tested based on the EMG data for another 10 subjects. The model allowed for estimating EMG responses for the 10 trunk muscles with the average value of mean absolute error (MAE) of 9.9% (SD=1.44%). This study demonstrates that application of fuzzy modeling techniques allows for estimating time domain EMG responses of trunk muscles due to manual lifting under limited task conditions. Relevance to industry Estimation of EMG responses using the proposed fuzzy-based system opens new opportunities for biomechanical modeling of manual-lifting tasks aimed at prevention of low back disorders at the workplace.