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Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on

Date 1-6 June 2008

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Displaying Results 1 - 25 of 376
  • Improvements in the motion accuracy of Linear Switched Reluctance Motors

    Page(s): 1 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (457 KB) |  | HTML iconHTML  

    During the last decade, the linear switched reluctance motor (LSRM) has become popular due to its structural simplicity, robustness and high power density. However, its significant torque ripple creates difficulty on precision motion control. This paper aims to develop a robust control system to improve the motion accuracy of LSRMs. The LSRM prototype is firstly investigated to study its force and current relationship. With the help of software, LSRM motion tests are simulated before real experiment. The significant improvement on position control strongly proves the success of the proposal. After that, the experimental result applying on the real prototype closely matches the simulation result. In order to enhance the LSRM robustness and the position tracking responses, another fuzzy logic controller is newly designed and implemented to supervise the traditional proportional-differential (PD) control parameters. Combining the inner control loop on current force relationship and the outer control loop on PD parameter supervision, the LSRM system in this project is very robust and capable to provide a high precision motion performance. View full abstract»

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  • A novel approach to find the satisfaction pattern of customers in hotel management

    Page(s): 11 - 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (96 KB) |  | HTML iconHTML  

    Nowadays, many studies of the discovery of needs and feelings of the hotel customers are not only around before-booking period, but also do not consider the privacy of customers completely. While the best period of studies of this knowledge are after the booking took place, there are two major problems for its unpopular: one is personal privacy, the other is not having a scientific and valuable approach. In this paper, we propose a novel approach to deal with the above existing problems. We employ intuitionistic fuzzy set, alpha-cuts, and Apriori algorithm to discovery the knowledge of needs and feelings of customers under an anonymous way. The approach is expatiated under different alpha by an example. And The yielded pattern and association rules have taken to the cooperative hotel more effects than before. So the approach is provable and valuable. View full abstract»

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  • Optimization of the normalization of fuzzy relational databases by using alternative methods of calculation for the Fuzzy Functional Dependency

    Page(s): 15 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (219 KB) |  | HTML iconHTML  

    Although, in comparison to standard databases, a tremendous benefit is often derived by using fuzzy databases, their distribution is very low. A reason for the relatively poor acceptance of fuzzy relational databases is to be seen in the difficulty to carry out an adequate normalization. The various procedures discussed in the literature possess miscellaneous weaknesses. In this work a normalization method is regarded whose most significant deficit lies in the fuzzy functional dependency (FFD) because thereby comprehensible results are not produced. Therefore, it is registered which alternatives for the determination of the degree of FFD exist. Furthermore, it is examined with which of these methods the just addressed disadvantage can be eliminated. For this purpose, the presented methods are applied to several examples in order to identify their characteristics. View full abstract»

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  • Discussion of approximation error bounds to the class of fuzzy system

    Page(s): 21 - 26
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (128 KB) |  | HTML iconHTML  

    The standard fuzzy systems are established with partition of normal quadratic polynomial membership functions and normal trigonometric membership functions. Based on the systems established and the standard fuzzy systems with partition of normal triangle functions, approximation error bounds problems are discussed by interpolation theory. Universal approximation error bounds of these fuzzy systems from SISO to MISO are given and their relations are founded. The error remainder term and auxiliary function are employed for the first time in proving process. Moreover, advantage and shortcoming of the three fuzzy systems are compared and correlative conclusions are obtained. Finally, computing examples are given and the validity of the conclusions is confirmed. View full abstract»

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  • Fuzzy scheduling for single batch-processing machine with non-identical job sizes

    Page(s): 27 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (109 KB)  

    In this paper, we introduce the fuzzy model of the makespan on a single batch-processing machine with non-identical job sizes and propose an improved DNA evolutionary algorithm (IDEA) solution approach. The model is based on fuzzy batch processing time and fuzzy intervals between batches. DEA is improved by integrating the crossover operator to overcome the immature convergence caused by the determinate selection of vertical operator in DEA. To decode the permutations of jobs searched by IDEA, the heuristic first fit decreasing (FFD) is applied to produce batches. In the experiment, the results of the fuzzy makespan demonstrate the proposed algorithm outperforms GA and SA on all instances. View full abstract»

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  • Simulating associations and interactions among multiple pieces of brand image using Fuzzy Bidirectional Associative Memory

    Page(s): 31 - 36
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (890 KB) |  | HTML iconHTML  

    The paper discusses an idea of representing brand image on a computer and simulating associations and interactions among multiple pieces of brand image. Brand image is represented using a fuzzy set based on the theory of brand personality, which is a theory to represent brand image indirectly by a set of human characteristics associated with a brand. An convenient feature of the representation is generality that image of any kind of brands could be defined on the same universal set. The interactions among multiple pieces of image are simulated using the framework of conceptual fuzzy set which is realized as combination of two fuzzy bidirectional associative memories. View full abstract»

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  • Fuzzy preference relation based multi-criteria decision making approach for WiMAX license award

    Page(s): 37 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (457 KB) |  | HTML iconHTML  

    This paper develops a multi-criteria evaluation approach based on the preference relation to help the National Communication Commission (NCC) in Taiwan award a WiMAX license under fuzzy environment, where the vagueness and subjectivity are handled with linguistic variables parameterized by triangular fuzzy numbers. This study applies the fuzzy multi-criteria decision making (MCDM) method to determine the importance weights of evaluation criteria and synthesize the ratings of possible alternatives. Aggregated the evaluatorspsila attitude toward possible alternatives; then the non-dominated degree is employed to obtain a crisp overall performance value for each contender to make a final decision. This approach is demonstrated with a real case study involving seven evaluation criteria, eight mobile companies assessed by four evaluators from academia and telecommunication arena. View full abstract»

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  • Grey modelling the groundwater level dynamic in the lower reaches of Tarim River affected by water delivery from upper reaches: A demonstration from Yingsu section

    Page(s): 43 - 48
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (162 KB) |  | HTML iconHTML  

    Using the grey system theory and the monitored data from the monitoring section of Yingsu, this paper models the groundwater level dynamic in the lower reaches of Tarim River affected by water delivery from upper reaches. The main conclusions are: (1) discharging volume, running days for water delivery and daily discharging volume, which related with water delivery from the upper reaches of Tarim River, are three main factors that markedly control and affect the groundwater level. (2) The sensitivity of groundwater level changing respond to itself becomes more and more lower versus the distance apart from river center, and the affection from discharging volume and running days for water delivery to the change rate of groundwater level becomes more and more significant with increase of the distance apart from river center. Water delivery not only markedly controls and raises the groundwater level near river, but also affects the groundwater level as far as the range in the distance of 1050 m apart from river center. View full abstract»

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  • Fuzzy logic based assignable causes ranking system for control chart abnormity diagnosis

    Page(s): 49 - 53
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (149 KB) |  | HTML iconHTML  

    When using control chart patterns as signals to identify the cause for faster and easier process diagnosis, tradition method is hard to handle with the uncertainties, ambiguities and vagueness associated with the problem. Based on fuzzy logic, this paper develops a fuzzy inference system (FIS), composed by six sub modules. Each determines the intensity of corresponding causes based on degree of presence of each pattern. All the evidence supporting each cause from the unnatural patterns are aggregated using fuzzy connective operators and causes are prioritized according to the final aggregating results. The search can be done from the cause having highest priority when process goes out of control. View full abstract»

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  • Searching minimal attribute reduction sets based on combination of the binary discernibility matrix and graph theory

    Page(s): 54 - 57
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (117 KB) |  | HTML iconHTML  

    Attribute reduction plays an important role in rough set theory. It is an important application in data mining. In this paper, we focus on discussing the relation between set covering and attribute reduction in rough set theory. Based on the equivalence between minimal set covering and minimal attribute reduction sets, attribute reduction graph (ARG) is constructed. A novel algorithm to find the minimal attribute reduction sets, which is based on combination of binary discernibility matrix and graph theory is proposed in this paper. This algorithm demonstrates its efficiency and feasibility by an example. View full abstract»

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  • On the monotonicity of functional type SIRMs connected fuzzy reasoning method and T-S reasoning method

    Page(s): 58 - 63
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (143 KB) |  | HTML iconHTML  

    Yubazaki et al. have proposed ldquosingle input rule modules connected type fuzzy reasoning methodrdquo (SIRMs method, for short) whose final output is obtained by summarizing the product of the importance degrees and the inference results from single input fuzzy rule module. Moreover, Seki et al. have proposed ldquofunctional type single input rule modules connected fuzzy reasoning methodrdquo (functional type SIRMs method, for short) whose consequent parts are generalized to functions from real numbers. It is expect that inference results of functional type SIRMs method have monotonicity if the antecedent parts and consequent parts of fuzzy rules in the functional type SIRMs rule modules have monotonicity. However, this paper points out that even if fuzzy rules in functional type SIRMs rule modules have monotonicity, the inference results do not necessarily have monotonicity. Moreover, it clarifies the conditions for the monotonicity of inference results by functional type SIRMs method, Takagi-Sugeno reasoning method (T-S reasoning method, for short), and simplified fuzzy reasoning method. View full abstract»

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  • Analytical structures and stability analysis of three-dimensional fuzzy controllers

    Page(s): 64 - 69
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (336 KB) |  | HTML iconHTML  

    We have revealed the analytical structures and stability analysis of the three-dimensional fuzzy controllers involving trapezoidal input fuzzy sets, singleton output fuzzy sets, Zadeh fuzzy AND triangular norm, Zadeh fuzzy OR triangular co-norm, Mamdani inference method and centroid defuzzification algorithm. This class of fuzzy controllers is a combination of a nonlinear PID controller with dynamic proportional gain, dynamic integral gain and dynamic derivative gain plus a piecewise constant term. Based on the mathematical structures, the bounded-input bounded-output (BIBO) stability conditions for fuzzy control systems have been obtained by the well-known small gain theorem. A computer simulation is provided to illustrate that the new fuzzy controller is effective and superior to the conventional PID controller. View full abstract»

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  • Analysis of structure and stability for the simplest two-dimensional fuzzy controller using generalized trapezoid-shaped input fuzzy sets

    Page(s): 70 - 75
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (360 KB) |  | HTML iconHTML  

    By summarizing the common characteristics of popular triangular and trapezoidal fuzzy sets, the more extensive generalized trapezoid-shaped (GTS) fuzzy set has been proposed. We have contributed to the analytical structures and stability analysis of the simplest two-dimensional fuzzy controllers using GTS input fuzzy sets. This class of fuzzy controllers is a combination of a piecewise linear PI controller plus a piecewise constant term. Based on the mathematical structures, the bounded-input bounded-output (BIBO) stability conditions for fuzzy control systems have been obtained by the well-known Small Gain Theorem. Two computer simulations are provided to demonstrate that the new fuzzy controller is effective and superior to the conventional PID controller. View full abstract»

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  • A hybrid system by integrating case based reasoning and fuzzy decision tree for financial time series data

    Page(s): 76 - 82
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (319 KB) |  | HTML iconHTML  

    Stock price predictions suffer from two well known difficulties, i.e., complicated and non-stationary variations within the large historic data. This paper establishes a novel financial time series-forecasting model by a case based fuzzy decision tree induction for stock price movement predictions in Taiwan Stock Exchange Corporation (TSEC). This forecasting model integrates a case based reasoning technique, a fuzzy decision tree (FDT) and genetic algorithms (GA) to construct a decision-making system based on historical data and technical indexes. The model is major based on the idea that the historic price data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately react to the current tendency of the stock price movement from these smaller case based fuzzy decision tree inductions. Hit rate is applied as a performance measure and the effectiveness of our proposed CBFDT model is demonstrated by experimentally compared with other approaches on various stocks from TSEC. The average hit rate of CBFDT model is 91% the highest among others. View full abstract»

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  • Adaptive fuzzy logic controller for vehicle active suspensions with interval type-2 fuzzy membership functions

    Page(s): 83 - 89
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (270 KB) |  | HTML iconHTML  

    Elicited from the least means squares optimal algorithm (LMS), an adaptive fuzzy logic controller (AFC) based on interval type-2 fuzzy sets is proposed for vehicle non-linear active suspension systems. The interval membership functions (IMF2s) are utilized in the AFC design to deal with not only non-linearity and uncertainty caused from irregular road inputs and immeasurable disturbance, but also the potential uncertainty of expertpsilas knowledge and experience. The adaptive strategy is designed to self-tune the active force between the lower bounds and upper bounds of interval fuzzy outputs. A case study based on a quarter active suspension model has demonstrated that the proposed type-2 fuzzy controller significantly outperforms conventional fuzzy controllers of an active suspension and a passive suspension. View full abstract»

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  • Adaptive fuzzy-neural-network control of robot manipulator using T-S Fuzzy model design

    Page(s): 90 - 97
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (880 KB) |  | HTML iconHTML  

    This study focuses on the development of an adaptive fuzzy-neural-network control (AFNNC) scheme for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, an AFNNC system is investigated without the requirement of prior system information. In this model-free control scheme, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with on-line learning ability is constructed for representing the system dynamics of an n-link robot manipulator. Then, a four-layer fuzzy-neural-network (FNN) is utilized for estimating nonlinear dynamic functions in this fuzzy model. Moreover, the AFNNC law and adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the network convergence as well as stable control performance. Numerical simulations of a two-link robot manipulator actuated by DC servomotors are given to verify the effectiveness and robustness of the proposed AFNNC methodology. In addition, the superiority of the proposed control scheme is indicated in comparison with proportional-differential control (PDC), Takagi-Sugeno-Kang (TSK) type fuzzy-neural-network control (T-FNNC), robust-neural-fuzzy-network control (RNFNC), and fuzzy-model-based control (FMBC) systems. View full abstract»

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  • A fuzzy clustering approach on the classification of non uniform cosmetic defects

    Page(s): 98 - 103
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (443 KB) |  | HTML iconHTML  

    In this paper a fuzzy clustering approach for the classification of cosmetic defects is presented. The paper investigates the solution of this classification problem with the Gustafson-Kessel (GK), and Geth-Geva (GG) with Abonyi-Szeifert (AS) fuzzy algorithms. The clustering process is achieved on multidimensional feature vectors that represent the cosmetic defects. The performance of the GK algorithm may be considered similar to a human inspector which is between 85% and 90% approximately. However, the fuzzy clustering technique has the advantage to be very consistent, contrary to a human inspector that can change her/his mind due to subjective influences. The paper also presents the comparison between the fuzzy approach and the artificial neural network approach. The problem faced in this work also helped to compare the performance of FC algorithms with ANN in real world applications. View full abstract»

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  • Ontological self-organizing maps for cluster visualization and functional summarization of gene products using Gene Ontology similarity measures

    Page(s): 104 - 109
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (244 KB) |  | HTML iconHTML  

    This paper presents an ontological self-organizing map (OSOM), which is used to produce visualization and functional summarization information about gene products using gene ontology (GO) similarity measures. The OSOM is an extension of the self-organizing map as initially developed by Kohonen, which trains on data composed of sets of terms. Term-based similarity measures are used as a distance metric as well as in the update of the OSOM training procedure. We present an OSOM-based visualization method that shows the cluster tendency of the gene products. Also demonstrated is an OSOM-based functional summarization which produces the most representative term(s) (MRT) from the GO for each OSOM prototype and, subsequently, each gene product cluster. We validated the results of our method by applying the OSOM to a well-studied set of gene products. View full abstract»

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  • On the division operator for probabilistic and possibilistic relational databases

    Page(s): 110 - 116
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (124 KB) |  | HTML iconHTML  

    This paper is situated in the area of imprecise (probabilistic and possibilistic) databases. Any imprecise database has a canonical interpretation as a set of more or less possible regular databases, also called worlds. In order to manipulate such databases in a safe and efficient way, a constrained framework has been previously proposed, where a restricted number of querying operations are permitted (selection, union, projection and foreign-key join which can handle attributes taking imprecise values). The key for efficiency resides in the fact that these operators do not require to make computations explicitly over all the more or less possible worlds. The division operation is dealt with in this paper and the impact of the uncertainty model on the processing technique is particularly studied. View full abstract»

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  • Fuzzy finite automata and fuzzy monadic second-order logic

    Page(s): 117 - 121
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (136 KB) |  | HTML iconHTML  

    We introduce fuzzy monadic second-order (LMSO-) logic and prove that the behaviours of fuzzy finite automata with membership values in an MV-algebra are precisely the fuzzy languages definable with sentences of our LMSO logic. This generalizes Buchipsilas and Elgotpsilas fundamental theorems to fuzzy logic setting. We also consider fuzzy first-order logic and show that star-free fuzzy languages and aperiodic fuzzy languages introduced here coincide with the fuzzy first-order definable ones. View full abstract»

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  • FCM-type switching regression with alternating least squares method

    Page(s): 122 - 127
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (142 KB) |  | HTML iconHTML  

    Fuzzy c-regression models (FCRM) performs switching regression based on a Fuzzy c-means (FCM)-like iterative optimization procedure, in which regression errors are also used for clustering criteria. In data mining applications, we often deal with databases consisting of mixed measurement levels. The alternating least squares method is a technique for mixed measurement situations, in which nominal variables (categorical observations) are quantified so that they suit the current model, and has been applied to FCM-type fuzzy clustering in order to characterize each cluster considering mutual relation among categories. This paper proposes two new algorithms for handling mixed measurement situations in FCM-type switching regression based on the alternating least squares method. The iterative algorithms include additional optimal scaling steps for calculating numerical scores of categorical variables. View full abstract»

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  • FCM in novel application of science and technology progress monitor system

    Page(s): 128 - 133
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (524 KB)  

    This paper focuses on the issues about the complex relations in large-scale FCM, and then proposes a promising method for weight global optimization with local inference to analyze and predict indexes in Anhui sci-tech progress monitor system. Firstly, a new concept, unbalanced degree, is introduced for standard evaluation in FCM model to modify the weight assessment factors and result in the satisfied convergence rate. Secondly, relations between unbalanced degree and convergence error are also presented for further analysis with training error and guarantee on perfect condition in model. Thirdly, local inference in FCM is discussed to enhance prediction accuracy rate. Finally, experimental result reveals successful application of FCM in large-scale complex sci-tech systems. View full abstract»

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  • LMI relaxations for nonlinear fuzzy control systems via homogeneous polynomials

    Page(s): 134 - 140
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (167 KB) |  | HTML iconHTML  

    Based on recent results on homogeneous polynomially parameter-dependent (HPPD) solutions to parameter-dependent LMIs (PD-LMIs) that arise from robust stability of linear parameter varying (LPV) systems, we investigate the relaxed conditions characterized by parameter-dependent LMIs (PD-LMIs) in terms of firing strength belonging to the unit simplex, exploiting the algebraic property of Polyapsilas theorem to construct a family of finite-dimensional LMI relaxations. The main contribution of this paper is that sets of relaxed LMIs are parameterized in term of the polynomial degree d. As d increases, progressively less conservative LMI conditions are generated, being easier satisfied due to more freedom provided by new variables involved. An example to illustrate the relaxation is provided. View full abstract»

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  • Delay-dependent robust H filtering design for uncertain discrete-time T-S fuzzy systems with interval time-varying delay

    Page(s): 141 - 147
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (157 KB) |  | HTML iconHTML  

    This paper investigates the problem of delay-dependent robust Hinfin filtering design for a class of uncertain discrete-time state-delayed T-S fuzzy systems. The state delay is assumed to be time-varying and of an interval-like type, which means that both the lower and upper bounds of the time-varying delay are available. The parameter uncertainties are assumed to have a structured linear fractional form. Based on a novel delay and fuzzy-basis-dependent Lyapunov-Krasovskii functional combined with Finslerpsilas Lemma, a new sufficient condition for robust Hinfin performance analysis is firstly derived and then the filter synthesis is developed. It is shown that by using a new linearization technique incorporating a bounding inequality, a unified framework can be developed such that both the full-order and reduced-order filters can be obtained by solving a set of linear matrix inequalities, which are numerically efficient with commercially available software. Finally, a numerical example is provided to illustrate the advantages and less conservatism of the proposed approach. View full abstract»

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  • Developing a type-2 FLC through embedded type-1 FLCs

    Page(s): 148 - 155
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB) |  | HTML iconHTML  

    Type-1 fuzzy logic controllers (FLCs) have been widely employed in many control applications as they give a good performance and it is relatively easy to extract the type-1 FLC parameters from experts. However, type-1 FLCs cannot fully handle the encountered uncertainties in changing unstructured environments as they use crisp type-1 fuzzy sets. Consequently, in order for type-1 FLCs to provide a satisfactory performance in face of high levels of uncertainties, some common practices are followed including continuously tuning the type-1 FLC or providing a set of type-1 FLCs where each FLC handles specific operation conditions. Alternatively, type-2 FLCs can handle uncertainties to give a better control performance. However, it is relatively challenging to extract from experts the footprint of uncertainty (FOU) information and consequently the type-2 fuzzy sets for type-2 FLCs. In this paper, we will present a novel method for generating the input and output type-2 fuzzy sets so that their FOUs can capture the faced uncertainties. The proposed method will generate a type-2 FLC that will try to embed the type-1 FLCs corresponding to the various operation conditions faced so far besides embedding a large number of other embedded type-1 FLCs. This will allow the type-2 FLC to handle the uncertainties trough a big number of embedded type-1 FLCs to produce a smooth and robust control performance. We will show through real world experiments how the developed type-2 FLC will handle the uncertainties and give a smooth control response that outperforms the individual and aggregated type-1 FLCs. View full abstract»

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