Results 1  10
of
1,602
ANFIS: Adaptivenetworkbased fuzzy Inference Systems”,
 IEEE Transactions on Systems, Man and Cybernetics. Part B: Cybernetics,
, 1993
"... ..."
Neurofuzzy modeling and control
 IEEE PROCEEDINGS
, 1995
"... Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of ad ..."
Abstract

Cited by 240 (1 self)
 Add to MetaCart
(Show Context)
Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (AdaptiveNetworkbased Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neurofuzzy approaches are also addressed.
Nonlinear BlackBox Modeling in System Identification: a Unified Overview
 Automatica
, 1995
"... A nonlinear black box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area with structures based on neural networks, radial basis networks, wavelet networks, hinging hyperplanes, ..."
Abstract

Cited by 224 (16 self)
 Add to MetaCart
(Show Context)
A nonlinear black box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area with structures based on neural networks, radial basis networks, wavelet networks, hinging hyperplanes, as well as wavelet transform based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system identification application of these techniques. It is pointed out that the nonlinear structures can be seen as a concatenation of a mapping from observed data to a regression vector and a nonlinear mapping from the regressor space to the output space. These mappings are discussed separately. The latter mapping is usually formed as a basis function e...
Functional Equivalence between Radial Basis Function Networks and Fuzzy Inference Systems
, 1993
"... This short article shows that under some minor restrictions, the functional behavior of radial basis function networks and fuzzy inference systems are actually equivalent. This functional equivalence implies that advances in each literature, such as new learning rules or analysis on representational ..."
Abstract

Cited by 170 (4 self)
 Add to MetaCart
This short article shows that under some minor restrictions, the functional behavior of radial basis function networks and fuzzy inference systems are actually equivalent. This functional equivalence implies that advances in each literature, such as new learning rules or analysis on representational power, etc., can be applied to both models directly. It is of interest to observe that twomodels stemming from different origins turn out to be functional equivalent.
Selecting fuzzy ifthen rules for classification problems using genetic algorithms
 IEEE TRANS. FUZZY SYST
, 1995
"... This paper proposes a geneticalgorithmbased method for selecting a small number of significant fuzzy ifthen rules to construct a compact fuzzy classification system with high classification power. The rule selection problem is formulated as a combinatorial optimization problem with two objectives ..."
Abstract

Cited by 134 (21 self)
 Add to MetaCart
(Show Context)
This paper proposes a geneticalgorithmbased method for selecting a small number of significant fuzzy ifthen rules to construct a compact fuzzy classification system with high classification power. The rule selection problem is formulated as a combinatorial optimization problem with two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy ifthen rules. Genetic algorithms are applied to this problem. A set of fuzzy ifthen rules is coded into a string and treated as an individual in genetic algorithms. The fitness of each individual is specified by the two objectives in the combinatorial optimization problem. The performance of the proposed method for training data and test data is examined by computer simulations on the iris data of Fisher.
DENFIS: Dynamic Evolving NeuralFuzzy Inference System and Its Application for TimeSeries Prediction
, 2001
"... This paper introduces a new type of fuzzy inference systems, denoted as DENFIS (dynamic evolving neuralfuzzy inference system), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), ..."
Abstract

Cited by 117 (19 self)
 Add to MetaCart
(Show Context)
This paper introduces a new type of fuzzy inference systems, denoted as DENFIS (dynamic evolving neuralfuzzy inference system), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning and accommodate new input data, including new features, new classes, etc. through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment the output of DENFIS is calculated through a fuzzy inference system based on mmost activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: (1) dynamic creation of a firstorder TakagiSugeno type fuzzy rule set for a DENFIS online model; (2) creation of a firstorder TakagiSugeno type fuzzy rule set, or an expanded highorder one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before, or during its learning process. Fuzzy rules can also be extracted during the learning process or after it. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well known, existing models.
Integrating design stages of fuzzy systems using genetic algorithms
, 1993
"... Abstract — This paper proposes an automaticfuzzy system design method that uses a Genetic Algorithm and integrates three design stages; our method determines membership functions, the number of fuzzy rules, and the ruleconsequent parameters at the same time. Because these design stages may not be in ..."
Abstract

Cited by 110 (1 self)
 Add to MetaCart
(Show Context)
Abstract — This paper proposes an automaticfuzzy system design method that uses a Genetic Algorithm and integrates three design stages; our method determines membership functions, the number of fuzzy rules, and the ruleconsequent parameters at the same time. Because these design stages may not be independent, it is important to consider them simultaneously to obtain optimal fuzzy systems. The method includes a genetic algorithm and a penalty strategy that favors systems with fewer rules. The proposed method is applied to the classic inverted pendulum control problem and has been shown to be practical through a comparison with another method. 1 1
Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems
 IEEE TRANS. SYSTEMS, MAN CYBERNETICS—PART B: CYBERNET
, 1999
"... We examine the performance of a fuzzy geneticsbased machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if–then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be vi ..."
Abstract

Cited by 95 (11 self)
 Add to MetaCart
(Show Context)
We examine the performance of a fuzzy geneticsbased machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if–then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be viewed as a classifier system. In this paper, we first describe fuzzy if–then rules and fuzzy reasoning for pattern classification problems. Then we explain a geneticsbased machine learning method that automatically generates fuzzy if–then rules for pattern classification problems from numerical data. Because our method uses linguistic values with fixed membership functions as antecedent fuzzy sets, a linguistic interpretation of each fuzzy if–then rule is easily obtained. The fixed membership functions also lead to a simple implementation of our method as a computer program. The simplicity of implementation and the linguistic interpretation of the generated fuzzy if–then rules are the main characteristic features of our method. The performance of our method is evaluated by computer simulations on some wellknown test problems. While our method involves no tuning mechanism of membership functions, it works very well in comparison with other classification methods such as nonfuzzy machine learning techniques and neural networks.
An online selfconstructing neural fuzzy inference network and its applications
 IEEE. TRANS. FUZZY. SYS
, 1998
"... A selfconstructing neural fuzzy inference network (SONFIN) with online learning ability is proposed in this paper. The SONFIN is inherently a modified Takagi–Sugeno–Kang (TSK)type fuzzy rulebased model possessing neural network’s learning ability. There are no rules initially in the SONFIN. The ..."
Abstract

Cited by 92 (22 self)
 Add to MetaCart
(Show Context)
A selfconstructing neural fuzzy inference network (SONFIN) with online learning ability is proposed in this paper. The SONFIN is inherently a modified Takagi–Sugeno–Kang (TSK)type fuzzy rulebased model possessing neural network’s learning ability. There are no rules initially in the SONFIN. They are created and adapted as online learning proceeds via simultaneous structure and parameter identification. In the structure identification of the precondition part, the input space is partitioned in a flexible way according to a aligned clusteringbased algorithm. As to the structure identification of the consequent part, only a singleton value selected by a clustering method is assigned to each rule initially. Afterwards, some additional significant terms (input variables) selected via a projectionbased correlation measure for each rule will be added to the consequent part (forming a linear equation of input variables) incrementally as learning proceeds. The combined precondition and consequent structure identification scheme can set up an economic and dynamically growing network, a main feature of the SONFIN. In the parameter identification, the consequent parameters are tuned optimally by either least mean squares (LMS) or recursive least squares (RLS) algorithms and the precondition parameters are tuned by backpropagation algorithm. Both the structure and parameter identification are done simultaneously to form a fast learning scheme, which is another feature of the SONFIN. Furthermore, to enhance the knowledge representation ability of the SONFIN, a linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved. Proper linear transformations are also learned dynamically in the parameter identification phase of the SONFIN. To demonstrate the capability of the proposed SONFIN, simulations in different areas including control, communication, and signal processing are done. Effectiveness of the SONFIN is verified from these simulations.
Designing fuzzy inference systems from data: an interpretabilityoriented review
 IEEE TRANS. FUZZY SYSTEMS
, 2001
"... Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from ..."
Abstract

Cited by 88 (16 self)
 Add to MetaCart
(Show Context)
Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for humancomputer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. This paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined.