Design Of Fuzzy System Research Articles

Flow regime identification in a novel circulating-turbulent fluidized bed

Abstract Whether flow dynamics in a novel circulating-turbulent fluidized bed (C-TFB) belongs to a new flow regime or just a transition state is controversial. In order to resolve this confusion, transient fluctuation signal processing and wavelet analysis have been used to identify the flow regimes in five specially designed flow systems, which have been proven to be effective tools to extract detailed information of flow features in different flow regimes. Furthermore, the fuzzy system identification method, which was used to process wavelet energy data of various scales obtained by wavelet analysis, was employed to make a quantitative distinction among the flow regimes. The results showed that the flow regime in the C-TFB is a novel flow regime, which is different from traditional flow regimes such as bubbling, turbulent, fast fluidization (FF) and dense suspension up-flow (DSU) regimes. The new regime has some unique characteristics including intensive gas–solids interaction, high gas–solids contact efficiency, two peaks of probability density distribution (PDD) curves in the center of the fluidized bed, broader spectrum of solids holdup for PDD curves, a decreasing extent of gas–solids separation extent and unique radial detail wavelet energy percentage profiles. Quantitative analysis by the fuzzy system identification method further confirmed that the flow behavior in the C-TFB can be described as a novel flow regime.

The Application of MATLAB for Fuzzy Control System Simulation

The design of fuzzy control system is one of the important problems of the application of fuzzy control system. And the system simulation is an important step in the design of and the necessary guarantee. This paper briefly introduces the MATLAB language, the fuzzy logic toolbox, and SILMULINK toolbox. Then the examples, this method overcome the traditional in c language simulation of complex and inconvenient shortcoming. It is easy to fuzzy control system simulation, direct and fast. Finally also introduced several methods of create SILMULINK user s function module.

Flow Graph Approach for Studying Fuzzy Inference Systems

This paper proposes an approach to analysis of fuzzy inference systems. To this end, a flow graph representation of a decision table with linguistic values is used. Every layer in a fuzzy flow graph corresponds to a fuzzy attribute (input or output). Each node in a layer represents a particular linguistic value of an attribute. Certainty factor, coverage factor and strength of rules can be computed for a given set of examples (universe) and serve as helpful measures for gaining more insight into the operation of a fuzzy inference system. Moreover, application of flow graphs can be useful for selecting membership functions and determining other parameters in the design of fuzzy inference systems.

Stability analysis and decentralized H∞ control for time-delay fuzzy interconnected systems via fuzzy Lyapunov-Krasovskii functional

This paper is concerned with the stability analysis and H∞ fuzzy controller design of continuous-time fuzzy interconnected systems with time-varying delays. The fuzzy interconnected system consists of N interconnected time-delay Takagi-SugenoT-S fuzzy subsystems and the delay-dependent stability analysis is based on the fuzzy weighting-dependent Lyapunov-Krasovskii functionals. It is shown that the delay-dependent stability of the time-delay fuzzy interconnected systems can be established if a fuzzy weighting-dependent Lyapunov-Krasovskii functional can be constructed, and moreover, the functional can be obtained by solving a set of linear matrix inequalities LMIs that are numerically feasible. The decentralized H∞ controllers are also designed by solving a set of LMIs based on these fuzzy weighting-dependent Lyapunov-Krasovskii functionals. It is demonstrated via numerical examples that the stability and controller synthesis results based on fuzzy weighting-dependent Lyapunov-Krasovskii functionals are less conservative than those based on the common quadratic Lyapunov-Krasovskii functionals.

Fuzzy rules extraction based on output-interval clustering and support vector regression for forecasting

This paper presents a fuzzy rules extraction algorithm based on output-interval clustering and support vector regression. The approach is unlike most existing clustering algorithms for structure identification of fuzzy systems, where the focus is on combined input–output clustering. The output-interval clustering algorithm divides the output space into several partitions and each output partition is considered to be an interval; then, input data are projected into sub-clusters that are based on the input distribution constrained by the output intervals. Fuzzy rules are extracted from sub-clusters within each output interval. In order to have a more compact final system structure and better accuracy, local functions associated with each of the sub-clusters based on support vector regression are constructed. The fuzzy rule-based modeling scheme gradually adapts its structure and rules antecedent and consequent parameters from data. Its main purpose is continuous learning, and adaptation to unknown environments. To illustrate the effectiveness of the approach, the paper considers a 2-D nonlinear function approximation, chaotic time series prediction and an operation learning application of steel mechanical property forecasting.

Optimal fuzzy load frequency controller with simultaneous auto-tuned membership functions and fuzzy control rules

In this paper, an auto-tuned fuzzy load frequency controller (FLFC)-based articial bee colony (ABC) algorithm is developed to quench the deviations in the frequency and tie-line power due to load disturbances in an interconnected power system. Optimal tuning of membership functions (MFs) and fuzzy control rules is very important to improve the design performance and achieve a satisfactory level of robustness for a particular operation. In this work, to reduce the fuzzy system design eort and take large parametric uncertainties into account, a new systematic and simultaneous tuning method is developed for designing MFs and fuzzy rules. For this, the designing problem is restructured as an optimization problem and the ABC algorithm is employed to solve it. This newly developed method provides some advantages such as a exible controller with a simple structure and easy algorithm. For the purpose of the proposed method's evaluation, the designed controller is applied to a 2-area power system with considerations regarding governor saturation and the results are compared to the one obtained by a classic proportional-integral controller. Simulation results show better operation and improved system parameters, such as the settling time and step response rise time, using the proposed approach, in the presence of system parameter variations.

Hierarchical Structured Sparse Representation for T–S Fuzzy Systems Identification

“The curse of dimensionality” has become a significant bottleneck for fuzzy system identification and approximation. In this paper, we cast the Takagi-Sugeno (T-S) fuzzy system identification into a hierarchical sparse representation problem, where our goal is to establish a T-S fuzzy system with a minimal number of fuzzy rules, which simultaneously have a minimal number of nonzero consequent parameters. The proposed method, which is called hierarchical sparse fuzzy inference systems ( H-sparseFIS), explicitly takes into account the block-structured information that exists in the T-S fuzzy model and works in an intuitive way: First, initial fuzzy rule antecedent part is extracted automatically by an iterative vector quantization clustering method; then, with block-structured sparse representation, the main important fuzzy rules are selected, and the redundant ones are eliminated for better model accuracy and generalization performance; moreover, we simplify the selected fuzzy rules consequent with sparse regularization such that more consequent parameters can approximate to zero. This algorithm is very efficient and shows good performance in well-known benchmark datasets and real-world problems.

FINGRAMS: Visual Representations of Fuzzy Rule-Based Inference for Expert Analysis of Comprehensibility

Since Zadeh's proposal and Mamdani's seminal ideas, interpretability is acknowledged as one of the most appreciated and valuable characteristics of fuzzy system identification methodologies. It represents the ability of fuzzy systems to formalize the behavior of a real system in a human understandable way, by means of a set of linguistic variables and rules with a high semantic expressivity close to natural language. Interpretability analysis involves two main points of view: readability of the knowledge base description (regarding complexity of fuzzy partitions and rules) and comprehensibility of the fuzzy system (regarding implicit and explicit semantics embedded in fuzzy partitions and rules, as well as the fuzzy reasoning method). Readability has been thoroughly treated by many authors who have proposed several criteria and metrics. Unfortunately, comprehensibility has usually been neglected because it involves some cognitive aspects related to human reasoning, which are very hard to formalize and to deal with. This paper proposes the creation of a new paradigm for fuzzy system comprehensibility analysis based on fuzzy systems' inference maps, so-called fuzzy inference-grams (fingrams), by analogy with scientograms used for visualizing the structure of science. Fingrams show graphically the interaction between rules at the inference level in terms of co-fired rules, i.e., rules fired at the same time by a given input. The analysis of fingrams offers many possibilities: measuring the comprehensibility of fuzzy systems, detecting redundancies and/or inconsistencies among fuzzy rules, identifying the most significant rules, etc. Some of these capabilities are explored in this study for the case of fuzzy models and classifiers.

Designing fuzzy rule base using hybrid elite genetic algorithm and tabu search: Application for control and modeling

In this paper, Hybrid Elite Genetic Algorithm and Tabu Search HEGATS is proposed to automatically generate Fuzzy rule base for fuzzy inference systems. The algorithm is used to simultaneously optimize the premise and consequent parameters of the fuzzy rules for the appropriate design of fuzzy system for Takagi-Sugeno zero-order. After finale selection of the new generation calculated by genetic algorithm, elitist solution is saved. In this step, tabu search is introduced to find the better neighboring of the elitist solution which will be introduced in the new generation. This hybridization of global and local optimization algorithms minimizes the number of iterations and computation time while maintaining accuracy and minimum response time. To demonstrate the effectiveness of the proposed algorithm, several numerical examples given in the literature for control and modeling systems are examined. Results prove the effectiveness of the proposed algorithm.

A novel T–S fuzzy systems identification with block structured sparse representation

In this paper, we propose a fuzzy partition based T–S fuzzy systems identification method with block structured sparse representation. Firstly, a novel fuzzy partition method is developed to learn fuzzy rule dictionaries by taking advantage of the geometrical structure of input variables and the functional relationship between input and output variables. Then, we explicitly focus on the block structured information existing in T–S fuzzy models and cast the systems identification problem into an optimization problem with structured sparse representation. In such a way, accurate description of T–S fuzzy model is established with far fewer numbers of fuzzy rules by selecting the important fuzzy rules and eliminating the redundant ones in the process of block structured sparse regression. Several numerical experiments on well-known benchmark data sets are carried out to illustrate the effectiveness of the proposed method.

Research on Adaptive Fuzzy PID Dust Collection System

Aimed at time lag, time variant, serious inertia, nonlinearity problems and the difficulty of PID controller parameters adjusting, the combination of PID control and fuzzy control was put forward, this paper introduces a kind of design of adaptive fuzzy PID dust collection system of constant pressure with regarding the pressure of the deviation and the rate of change of deviation as the input variables in the ventilation pipe. The three parameters of PID controller can be adjusted by establishing the fuzzy control rules and practicing the fuzzy generalize. The experiment simulation and the practical test show that the response of the system with adaptive fuzzy PID control is much faster than the tradition PID control but the overshoot is lower and quickly achieve the steady-state.

Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity

This paper proposes the design of fuzzy control systems with a reduced parametric sensitivity making use of Gravitational Search Algorithms (GSAs). The parametric variations of the processes lead to sensitivity models. Objective functions expressed as integral quadratic performance indices, which depend on the control error and squared output sensitivity functions are suggested. GSAs are employed to minimize the objective functions in the appropriately defined optimization problems. This paper also suggests a GSA with improved search accuracy. The new GSA is characterized by the modification of the denominator in the expression of the force acting on an agent from the other agent; the denominator depends not only on the Euclidian distance between the two agents but also on the position of the latter: A design method for Takagi–Sugeno proportional-integral fuzzy controllers (PI-FCs) is proposed. The PI-FCs are dedicated to a class of processes characterized by second-order linear or linearized models with an integral component. Two discrete-time sensitivity models of the fuzzy control systems are derived. An example dealing with the angular position control of direct current (DC) servo system laboratory equipment validates the new controller design. A set of real-time experimental results illustrates the fuzzy control system performance.

Evaluation of Fuzzy Inference System in Image Processing

paper reports the evaluation of Fuzzy logic implemented on image, in MATLAB environment, used to implement different functions. In this paper, the in-built functions of MATLAB are implemented on image and the resultant images are displayed using different functions. Fuzzy inference system has been designed for four inputs, one output that tells whether the pixel under consideration is low, medium or high pixel. Rule base comprises of eight rules, which classify the target pixel. The Type-1 Fuzzy Logic System Toolbox (T1FLS) is an environment for type-1 fuzzy logic inference system development. Tools that cover the different phases of the fuzzy system design process, from the initial description phase, to the final implementation phase, build the Toolbox. The Toolbox's best qualities are the capacity to develop complex systems and the flexibility that permits the user to extend the availability of functions for working with the use of type-1 fuzzy operators, fuzzy variables, type-1 membership functions, de-fuzzification methods and the evaluation of Type-1 Fuzzy Inference Systems.

PLC-based Temperature Fuzzy Control System Design Research

The paper introduced a PLC-based temperature fuzzy control system design method and elaborated the basic theory of PLC and relevant PLC engineer design through electric heating temperature control. The paper will design a fuzzy controller based on the Siemens programmable control, targeting at the features of temperature control. It adopts Profibus-DP field bus to establish a field control layer and then construct a supervision and management layer based on industrial Ethernet. Finally, it realizes fuzzy control of temperature, regarding furnace temperature as the control object.

Switching-Type $H_{\infty }$ Filter Design for T–S Fuzzy Systems With Unknown or Partially Unknown Membership Functions

This paper is concerned with the H∞ filter design problem for Takagi-Sugeno (T-S) fuzzy systems with unknown or partially unknown membership functions. If the membership functions are allowed to be unknown or partially unknown, then a fuzzy system may describe a wide class of nonlinear systems. However, in this case, the filter design of fuzzy systems, which is based on parallel distributed compensator strategy, is infeasible. To tackle this difficulty, a switching mechanism, which depends on the lower and upper bounds of the unknown membership functions, is introduced to construct the H∞ filter with varying gains. Some examples given in the simulation section show that the proposed method can achieve a better disturbance attenuation performance than a simple fixed-gain filter design approach.

A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions

Over the past few decades, fuzzy systems have been widely used in several application fields, thanks to their ability to model complex systems. The design of fuzzy systems has been successfully performed by applying evolutionary and, in particular, genetic algorithms, and recently, this approach has been extended by using multiobjective evolutionary algorithms, which can consider multiple conflicting objectives, instead of a single one. The hybridization between multiobjective evolutionary algorithms and fuzzy systems is currently known as multiobjective evolutionary fuzzy systems. This paper presents an overview of multiobjective evolutionary fuzzy systems, describing the main contributions on this field and providing a two-level taxonomy of the existing proposals, in order to outline a well-established framework that could help researchers who work on significant further developments. Finally, some considerations of recent trends and potential research directions are presented.

A new indirect approach to the type-2 fuzzy systems modeling and design

This paper proposes a new method for designing Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) considering two issues: first, quality of clustering the output space and secondly, approximating the output of the IT2 FLS based on a new output processing method. Based on these two issues, we present a new cluster validity index capable of being used for type-1 Fuzzy C-Means (FCMs), Interval Type-2 FCM (IT2 FCM), and Possibilistic C-Means (PCMs) clustering algorithms. This validity index is highly efficient in determining clusters with the least similarity between them and the highest similarity between data vectors in each cluster. Then, a new definition for uncertainty bounds is presented in order to eliminate the type-reduction process in IT2 FLSs and to increase accuracy of the existing uncertainty bounds in the literature. Finally, effectiveness of the proposed approaches compared to several well-known existing methods has been investigated. Computational results have verified accuracy and effectiveness of the proposed method.

Output feedback control design with guaranteed cost for fuzzy bilinear time-delay systems

This paper is concerned with observer-based guaranteed cost control design of fuzzy bilinear systems with time-varying delay. First, we consider the state feedback control design by assuming a special form of a fuzzy controller. A delay-dependent condition, which is less conservative than delay-independent one, for guaranteed cost control design of fuzzy bilinear systems with time-varying delays is given in terms of linear matrix inequality (LMI). Next, we consider a fuzzy observer design for the same class of systems, and propose an observer design method based on another delay-dependent condition. Then, we show that state feedback controller and observer make a stabilizing observer-based output feedback controller with guaranteed cost. Finally, we give a numerical example to illustrate our design procedures and to show the effectiveness of our approach.

Identification of Fuzzy Inference Systems by Means of a Multiobjective Opposition-Based Space Search Algorithm

We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA). The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.

Design of BLDCM Fuzzy Control System for Electric Vehicle Based on DSP

In this paper, the design of BLDCM fuzzy control system for electric vehicle which bases on TMS320F2812 Digital Signal Processor (DSP) is given. The design methods of hardware and software of the control system are described in details. Using of brushless DC motor current and speed double closed loop control, meanwhile, the fuzzy self-tuning PI algorithm is applied in the control system, and achieve the intelligent control of brushless DC motor control system. In this paper, the PWM control of the overlapping commutation method to suppress torque ripple. Experimental results show that this system has such characteristics as sound controlling effects, obvious restrain of torque pulsation and reliable operation.