Special Membership Functions Research Articles

An evolving T–S fuzzy model identification approach based on a special membership function and its application on pump-turbine governing system

Hyper-plane-shaped clustering (HPSC) has been proved to be more effective in Takagi–Sugeno (T–S) fuzzy model identification compared with hyper-sphere-shaped clustering (HSSC). However, there is no special membership function matching HPSC in fuzzy modeling, and the commonly used bell-shaped Gaussian function is more suitable for HSSC. In this paper, a novel T–S fuzzy model identification method is adopted, in which a new fuzzy membership function designed for HSPC is designed. In this approach, a fuzzy c-regression model based clustering method is used to partition the fuzzy space firstly; and then a new HPSC fuzzy membership function is designed to identify the antecedent membership function (MF) parameters; finally the gravitational search algorithm is applied to optimize the MF parameters further. Experimental results on several benchmark problems show that modeling accuracies have been promoted significantly. The proposed approach has been applied in fuzzy modeling of pump-turbine governing system (PTGS). The comparative experimental results reveal that the proposed approach could achieve high accuracy and would be an effective modeling tool for complicated nonlinear system in engineering applications.

Revisit of Moving Average Technique for Smoothing GNSS Based Timing Data

A new moving average technique with a unique way of assigning weight has been proposed to reduce the influence of the bad/stray data points or large short term fluctuations automatically. A simple iterative technique is introduced wherein the weights of elements are dynamically optimised. This method has been compared with another proposed method which finds the average of a set of data by applying Fuzzy logic through generation of a special membership function. These two methods have been applied on some arbitrarily generated test data for establishing their validation. Finally this technique has been used to smooth the GNSS based timing data. The capabilities of smoothing data utilizing these techniques have been analyzed.

A fuzzy set plasticity model for cyclic loading of granular soils

A constitutive model for describing the stress–strain behavior of granular soils subjected to cyclic loading is presented. The model is formulated using fuzzy set plasticity theory within the classical incremental plasticity theory framework. A special membership function is introduced to provide an analytical and simple geometrical interpretation to formulate hardening, hysteresis feature, material memory, and kinematic mechanisms without resorting to complicated kinematic hardening formulations. The model can accurately describe cyclic loading, dilatancy, material theory and critical state soil mechanics features effects. Two series of cyclic drained triaxial tests data are considered. The characteristic features of behavior in granular soils subjected to cyclic loading are captured.

Fuzzy Gupta Scheduling for Flow Shops with More than two Machines

In past work the authors demonstrated how fuzzy concepts can easily be used in the Johnson algorithm for managing uncertain scheduling on two-machine flow shops. This article extends these concepts to fuzzy flow shops with more than two machines. A new fuzzy heuristic flow-shop scheduling algorithm (the fuzzy Gupta algorithm) is then designed, as optimal solutions seem unnecessary for uncertain environments. Also, the conventional Gupta algorithm is shown as a special case of the fuzzy Gupta algorithm with special membership functions being assigned.

Fuzzy Gupta Scheduling for Flow Shops with more than Two Machines

In past work the authors demonstrated how fuzzy concepts can easily be used in the Johnson algorithm for managing uncertain scheduling on two-machine flow shops. This article extends these concepts to fuzzy flow shops with more than two machines. A new fuzzy heuristic flow-shop scheduling algorithm (the fuzzy Gupta algorithm) is then designed, as optimal solutions seem unnecessary for uncertain environments. Also, the conventional Gupta algorithm is shown as a special case of the fuzzy Gupta algorithm with special membership functions being assigned.

A fuzzy rule-based approach to spatio-temporal hand gesture recognition

Gesture based applications widely range from replacing the traditional mouse as a position device to virtual reality and communication with the deaf. The article presents a fuzzy rule based approach to spatio-temporal hand gesture recognition. This approach employs a powerful method based on hyperrectangutar composite neural networks (HRCNNs) for selecting templates. Templates for each hand shape are represented in the form of crisp IF-THEN rules that are extracted from the values of synaptic weights of the corresponding trained HRCNNs. Each crisp IF-THEN rule is then fuzzified by employing a special membership function in order to represent the degree to which a pattern is similar to the corresponding antecedent part. When an unknown gesture is to be classified, each sample of the unknown gesture is tested by each fuzzy rule. The accumulated similarity associated with all samples of the input is computed for each hand gesture in the vocabulary, and the unknown gesture is classified as the gesture yielding the highest accumulative similarity. Based on the method we can implement a small-sized dynamic hand gesture recognition system. Two databases which consisted of 90 spatio-temporal hand gestures are utilized for verifying its performance. An encouraging experimental result confirms the effectiveness of the proposed method.

Identification of enhanced fuzzy models with special membership functions and fuzzy rule bases

A special fuzzy modeling method for developing multi-variable fuzzy models on the basis of measured input and output data is presented. Forming the crucial point in fuzzy modeling, the fuzzy model identification procedure is carried out by applying a special clustering method, the fuzzy c-elliptotypes method, to provide the parameters of the fuzzy model. To enhance the efficiency of the fuzzy model, the rather simple membership functions defined at first for the input fuzzy sets are replaced by a special class of functions. Additionally, the conventional rule base expressing the main links between the inputs and the outputs of the fuzzy model is replaced by a fuzzy rule base with fuzzy assignments, leading to further improvements of the fuzzy model.

LPT scheduling for fuzzy tasks

Scheduling mainly concerns allocating resources to tasks over time, under necessary constraints. In the past, the processing time for each task was usually assigned or estimated as a fixed value. In many real-world applications, however, the processing time for each job may vary dynamically with the situation. In this paper, fuzzy concepts are utilized in the LPT algorithm for managing uncertain scheduling. Given a set of tasks, each with its membership function for the processing time, the fuzzy LPT algorithm can get a scheduling result with a membership function for the final completion time. Also, the conventional LPT scheduling algorithm is shown as a special case of the fuzzy LPT scheduling algorithm with special membership functions being assigned. The fuzzy LPT scheduling algorithm is then a feasible solution for both deterministic and uncertain scheduling.

Enhanced Fuzzy Modeling Using Special Membership Functions and Fuzzy Rule Bases

Abstract A special fuzzy modeling method for developing multi-variable fuzzy models on the basis of measured input and output data is presented. Representing the crucial point in fuzzy modeling, the fuzzy model identification procedure is carried out by applying a special clustering method, the fuzzy c-elliptotypes method, providing the parameters of the fuzzy model. To enhance the efficiency of the fuzzy model, the rather simple membership functions defined at first for the input fuzzy sets are replaced by a special class of functions. Additionally, the conventional rule base expressing the main links between the inputs and the outputs of the fuzzy model is replaced by a fuzzy rule base with fuzzy assignments to enable further improvements of the fuzzy model.