Dynamic Fuzzy Research Articles

Fuzzy mixed assembly line sequencing and scheduling optimization model using multiobjective dynamic fuzzy GA.

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model.

EPA-0542 - New tools for new psychiatry: the change offered by fuzzy logic algorithms

The management of non-linear and grey clinical data, so frequent in psychiatry, needs the development of new statistical tools, able to give value to subjectivity and complexity of the observed changes. A comparison between soft computing models, psychodynamic theory and clinical observations in the experience of psychiatric rehabilitation. To test the use of some dynamic fuzzy algorithms (like clustering, ellipsoidal rules, least square method) in order to improve data analysis in the field of clinical research in psychiatry. The role of slow, evolutionary, multifactorial, minute movements and their potential significance become readable, especially referred to the subjective observations of the clinical operators. In this work, that it's necessary to consider only a first step, seems that Dynamic Fuzzy Functions may have a future role in the development of new assessments of clinical data in psychiatry.

状態の調整および状態と行動の組の削除機能を持つ動的ファジィQ-learning

状態の調整および状態と行動の組の削除機能を持つ動的ファジィQ-learning

Dynamic Fuzzy Logic Control of Genetic Algorithm Probabilities

Genetic algorithms are commonly used to solve combinatorial optimization problems. The implementation evolves using genetic operators (crossover, mutation, selection, etc.). Anyway, genetic algorit ...

Dynamic Fuzzy Clustering and Its Application in Motion Segmentation

Dynamic textures are common in natural scenes and have recently received great attention in video content analysis. A dynamic fuzzy clustering to automatically segment time-varying characteristics and phenomena is presented in this paper. First, compared with the existing models that assume a common prior distribution, which independently generates the labels, the prior distribution in our model is different for each observation and depends on the labels. In addition, in order to properly account for the neighboring observations during the learning step, we introduce the explicit assumptions of the hidden Markov random field model into the dynamic fuzzy clustering. Second, in order to model the observed dynamic texture data, only grayscale information is taken into consideration of the existing models. We use different visual properties by proposing a new distribution in this paper. Finally, to estimate the model parameters, the gradient method is adopted to minimize the fuzzy objective function with the Kullback-Leibler divergence information. Numerical experiments are presented, where the proposed model is tested on various simulated and real dynamic textures.

Closed Loop Dynamic Fuzzy Neural Network for Real-Time Lifetime Forecasting

Real-time lifetime forecasting has extensive application in the fields of machine system manufacturing and integration, which is a good way to promote the dependability and operation stability. In this paper, a closed loop adaptive forecasting model with feedback channel of state monitoring information is built up for the real-time lifetime forecasting. The difference of working state between prediction and monitoring information is used to evaluate the prediction performance. The dynamic fuzzy neural network introduced into the prediction model, in which the fuzzy rule, membrane function and structure parameters can be adjusted according to the evaluate results. A service lifetime testing experiment of gear case is utilized to validate the prediction model. The proposed model achieved reasonable precision with an error of less than 1 hour between the failure time of experimental results and the forecasting remaining lifetime. The adaptive prediction method can deal with the real-time lifetime forecasting for multiple factors and nonlinear system without specific parameters structure.

Safety Evaluation of Mine Pressure Based on Dynamic Fuzzy Theory

Safety Evaluation of Mine Pressure Based on Dynamic Fuzzy Theory

Dynamic Fuzzy Reliability Analysis of Mechanical Components with Small Samples of Load

Dynamic fuzzy reliability models of mechanical components are proposed in this paper in the case of small samples of load. The proposed models are established based on the distribution of load obtained from a load history, which reflects the relationship among the occurrence frequency of loads with different magnitude in a given time period, rather than the distribution at each load application. To deal with the uncertainty of the distribution of load from a load history due to the randomness of the magnitude of load at each load application, the statistical parameters involved in the distribution of load are modeled as fuzzy variables. Explosive bolts are chosen as representative examples to illustrate the proposed models. The results show that reliability increases with the increase in the level of cut set. Besides, the dispersion of strength has great influences on the reliability and failure rate of mechanical components.

A Dynamic Fuzzy Group Decision Making Method for Supplier Selection

A Dynamic Fuzzy Group Decision Making Method for Supplier Selection

Dynamic supervised classification method for online monitoring in non-stationary environments

The monitoring of a system functioning is achieved using a classifier which determines at each instant the class of a new incoming pattern. In non-stationary environments, the classifier must be able to adjust its parameters according to changes in the environment conditions. This requires a continuous learning while new patterns are available. Incremental learning is an efficient continuous learning technique for updating the classifier parameters without starting from scratch every time a new pattern is available. However in non-stationary environments, data characteristics may drift over time. This leads to deteriorate dramatically the performance of incremental learning algorithms over time. This is due to the use of data which is no more consistent with the characteristics of new incoming data. Thus, a mechanism to use only the recent and representative patterns to update the classifier parameters without a “catastrophic forgetting” is necessary. In this paper, we propose a dynamic pattern recognition method, named Dynamic Fuzzy Pattern Matching, to be used for the online monitoring of non-stationary processes functioning. This method is based on the use of an incremental algorithm allowing to follow the accumulated gradual changes of classes characteristics after the classification of each new pattern. When the accumulated gradual changes reach a suitable predefined threshold, the classifier parameters are adapted online using the recent and useful patterns.

Dynamical Fuzzy Decision of Freight Transportation Schemes under Variable Conditions

Dynamical Fuzzy Decision of Freight Transportation Schemes under Variable Conditions

DYNAMIC FUZZY LOGIC ELEVATOR GROUP CONTROL SYSTEM FOR ENERGY OPTIMIZATION

High-rise buildings with a considerable number of elevators represent a major logistic problem concerning saving space and time due to economic reasons. For this reason, complex Elevator Group Control Systems are developed in order to manage the elevators properly. Furthermore, the subject of energy is acquiring more and more industrial relevance every day as far as sustainable development is concerned. In this paper, the first entirely dynamic Fuzzy Logic Elevator Group Control System to dispatch landing calls so as to minimize energy consumption, especially during interfloor traffic, is proposed. The fuzzy logic design described here constitutes not only an innovative solution that outperforms usual dispatchers but also an easy, cheap, feasible and reliable solution, which is possible to be implemented in real industry controllers.

State Observer Based Dynamic Fuzzy Logic System for a Class of SISO Nonlinear Systems

This paper presents an observer based dynamic fuzzy logic system (DFLS) scheme for a class of unknown single-input single-output (SISO) nonlinear dynamic systems with external disturbances. The proposed approach does not need the availability of the state variables. Within this scheme, the DFLS is employed to identify the unknown nonlinear dynamic system. The control law and parameter adaptation laws of the DFLS are derived based on Lyapunov synthesis approach. The control law is robustfied in H8 sense to attenuate external disturbance, model uncertainties, and fuzzy approximation errors. It is shown that under appropriate assumptions, it guarantees the boundedness of all the signals in the closed-loop system and the asymptotic convergence to zero of tracking errors. The proposed method is applied to an inverted pendulum system to verify the effectiveness of the proposed algorithms.

Dynamic Fuzzy Neural Network Based Learning Algorithms for Ocular Artefact Reduction in EEG Recordings

Frequent occurrence of ocular artefacts leads to serious problems in reading and analysing the electroencephalogram (EEG) signal. These artefacts have high amplitude and overlapping frequency band with the physiological signal or real brain signal. Hence, it is difficult to reduce this type of artefacts by traditional filtering methods. In this paper, a novel ocular artefact removal method using artificial neural networks is described. In the proposed method, the number of radial basis function (RBF) neurons and input output space clustering are adaptively determined. Furthermore, the structure of the system and the parameters of the corresponding RBF units are trained automatically and relatively fast adaptation is attained. By the recursive least square error estimator techniques, the proposed system is suitable for real EEG applications. The advantages of the proposed method are demonstrated on EEG recordings by comparing with systems based on ICA. Our results demonstrate that this new system is preferable to other methods for ocular artefact reduction, achieving a better trade-off between removing artefacts and preserving inherent brain activities.

A Novel Legal Expert Based on Dynamic Fuzzy System in Manipulating Property Division upon Divorce

Legal expert system is an important research topic both in law research and computer science. This paper proposes a novel model for property division, which adopts latest marital law of PRC and dynamic fuzzy theory. Firstly, all up-to-dated clauses involved with property distribution in divorce were quantified to obtain related set elements. Then the dynamic fuzzy theory was applied to construct a membership function to calculate the weight of set elements. Ultimately， the appropriate degrees are calculated by optimum model. By testing, property division legal expert dynamic fuzzy system could consider questions scientifically, comprehensively and effectively.

A Dynamic Fuzzy Cluster Algorithm for Time Series

This paper presents an efficient algorithm, called dynamic fuzzy cluster (DFC), for dynamically clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.

Dynamic Fuzzy Logic Parameter Tuning for ACO and Its Application in the Fuzzy Logic Control of an Autonomous Mobile Robot

Ant Colony Optimization (ACO) is a population-based constructive meta-heuristic that exploits a form of past performance memory inspired by the foraging behaviour of real ants. The behaviour of the ACO algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired algorithms. The present paper explores a new approach to diversity control in ACO. The central idea is to avoid or slow down full convergence through the dynamic variation of certain parameters. The performance of different variants of the ACO algorithm was observed to choose one as the basis for the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence was created. Encouraging results have been obtained on its application to the design of fuzzy controllers. In particular, the optimization of membership functions for a unicycle mobile robot trajectory control is presented with the proposed method.

Uncertain Dynamic Model and Fuzzy Control Strategies of Closed-loop Supply Chain Based on Remanufacturing

Uncertain Dynamic Model and Fuzzy Control Strategies of Closed-loop Supply Chain Based on Remanufacturing

Study of Complex Equipment Fault Diagnosis System Based on Dynamic Fuzzy Judging

Study of Complex Equipment Fault Diagnosis System Based on Dynamic Fuzzy Judging

Research on Robustness of Color Device Characteristic Methods Based on Artificial Intelligence

The nature of device color characteristic methods is the mutual conversion of device-dependent color space and device-independent color space. This paper does the comparative study on the robustness of some color space conversion methods which are based on fuzzy control, dynamic subspace divided BP neural network identification method, and fuzzy and neural identification method, by defining the robustness of color space conversion model and evaluation method. The result shows that the device color characteristic methods which are based on fuzzy and neural identification method can make the feature of BP neural network combine with fuzzy control to greatly improve the robustness of model.