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833
Neurofuzzy modeling and control
 IEEE Proceedings
, 1995
"... Abstract  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 framew ..."
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Cited by 231 (1 self)
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Abstract  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. KeywordsFuzzy logic, neural networks, fuzzy modeling, neurofuzzy modeling, neurofuzzy control, ANFIS. I.
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 ..."
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Cited by 168 (4 self)
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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.
Automatic sign language analysis: A survey and the future beyond lexical meaning
 In IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2005
"... Abstract—Research in automatic analysis of sign language has largely focused on recognizing the lexical (or citation) form of sign gestures as they appear in continuous signing, and developing algorithms that scale well to large vocabularies. However, successful recognition of lexical signs is not s ..."
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Cited by 116 (1 self)
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Abstract—Research in automatic analysis of sign language has largely focused on recognizing the lexical (or citation) form of sign gestures as they appear in continuous signing, and developing algorithms that scale well to large vocabularies. However, successful recognition of lexical signs is not sufficient for a full understanding of sign language communication. Nonmanual signals and grammatical processes which result in systematic variations in sign appearance are integral aspects of this communication but have received comparatively little attention in the literature. In this survey, we examine data acquisition, feature extraction and classification methods employed for the analysis of sign language gestures. These are discussed with respect to issues such as modeling transitions between signs in continuous signing, modeling inflectional processes, signer independence, and adaptation. We further examine works that attempt to analyze nonmanual signals and discuss issues related to integrating these with (hand) sign gestures.We also discuss the overall progress toward a true test of sign recognition systems—dealing with natural signing by native signers. We suggest some future directions for this research and also point to contributions it can make to other fields of research. Webbased supplemental materials (appendicies) which contain several illustrative examples and videos of signing can be found at www.computer.org/publications/dlib. Index Terms—Sign language recognition, hand tracking, hand gesture recognition, gesture analysis, head tracking, head gesture recognition, face tracking, facial expression recognition, review. 1
SelfLearning Fuzzy Controllers Based on Temporal Back Propagation
, 1992
"... This paper presents a generalized control strategy that enhances fuzzy controllers with selflearning capability for achieving prescribed control objectives in a nearoptimal manner. This methodology, termed temporal back propagation, is modelinsensitive in the sense that it can deal with plants tha ..."
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Cited by 95 (3 self)
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This paper presents a generalized control strategy that enhances fuzzy controllers with selflearning capability for achieving prescribed control objectives in a nearoptimal manner. This methodology, termed temporal back propagation, is modelinsensitive in the sense that it can deal with plants that can be represented in a piecewise differentiable format, such as difference equations, neural networks, GMDH, fuzzy models, etc. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy ifthen rules obtained from human experts, or automatically derive the fuzzy ifthen rules if human experts are not available. The inverted pendulum system is employed as a testbed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller. 1 Introduction Fuzzy controllers (FC's) have recently found various applications in industry as well as in household appliances. For com...
An online selfconstructing neural fuzzy inference network and its applications
 IEEE. Trans. Fuzzy. Sys
, 1998
"... Abstract—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 SO ..."
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Cited by 92 (21 self)
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Abstract—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. Index Terms—Equalizer, noisy speech recognition, projectionbased correlation measure, similarity measure, TSK fuzzy rule.
Designing fuzzy inference systems from data: an interpretabilityoriented review
 IEEE Trans. Fuzzy Systems
"... Abstract—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 infer ..."
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Cited by 83 (14 self)
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Abstract—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. Index Terms—Fuzzy inference systems, fuzzy partitioning, interpretability, rule induction, system optimization. I.
NeuroFuzzy Systems for Function Approximation
 Fuzzy Sets and Systems
, 1999
"... We propose a neurofuzzy architecture for function approximation based on supervised learning. The learning algorithm is able to determine the structure and the parameters of a fuzzy system. The approach is an extension to our already published NEFCON and NEFCLASS models which are used for control ..."
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Cited by 51 (1 self)
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We propose a neurofuzzy architecture for function approximation based on supervised learning. The learning algorithm is able to determine the structure and the parameters of a fuzzy system. The approach is an extension to our already published NEFCON and NEFCLASS models which are used for control or classification purposes. The proposed extended model, which we call NEFPROX, is more general and can be used for any application based on function approximation. Keywords: neurofuzzy system, function approximation, structure learning, parameter learning 1 Introduction Certain fuzzy systems are universal function approximators [1, 4]. In order to identify a suitable fuzzy system for a given problem, membership functions (parameters) and a rule base (structure) must be specified. This can be done by prior knowledge, by learning, or by a combination of both. If a learning algorithm is applied that uses local information and causes local modifications in a fuzzy system, this approach is us...
Genetic Tuning of Fuzzy Rule Deep Structures for Linguistic Modeling
 IEEE Trans. on Fuzzy Systems
, 2001
"... Tuning fuzzy rulebased systems for Linguistic Modeling is an interesting and widely developed task. It involves adjusting some of the components composing the knowledge base without completely redening it. To do that, as the fuzzy rule symbolic representations (known as fuzzy rule surface structure ..."
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Cited by 49 (13 self)
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Tuning fuzzy rulebased systems for Linguistic Modeling is an interesting and widely developed task. It involves adjusting some of the components composing the knowledge base without completely redening it. To do that, as the fuzzy rule symbolic representations (known as fuzzy rule surface structures) as the meaning of the involved membership functions (which together with the surface structures are known as fuzzy rule deep structures) may be modied. This contribution introduces a genetic tuning process for jointly tting these two components, i.e., whole deep structures. To adjust the symbolic representations, we propose to use linguistic hedges to perform slight modications keeping a good interpretability. To change the membership function meanings, two dierent ways considering basic or extended expressions are proposed. As the accomplished experimental study shows, the good performance of our proposal mainly lies in the consideration of this tuning approach performed at two dierent levels of signicance. Keywords Fuzzy linguistic modeling, tuning, surface and deep structures, linguistic hedges. I.
Improving the interpretability of TSK fuzzy models by combining global and local learning
 IEEE Trans. Fuzzy Syst
, 1998
"... Abstract — The fuzzy inference system proposed by Takagi, Sugeno, and Kang, known as the TSK model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear systems. Unlike conventional modeling where a single model is used to describe the global behavior of a system, TSK ..."
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Cited by 47 (1 self)
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Abstract — The fuzzy inference system proposed by Takagi, Sugeno, and Kang, known as the TSK model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear systems. Unlike conventional modeling where a single model is used to describe the global behavior of a system, TSK modeling is essentially a multimodel approach in which simple submodels (typically linear models) are combined to describe the global behavior of the system. Most existing learning algorithms for identifying the TSK model are based on minimizing the square of the residual between the overall outputs of the real system and the identified model. Although these algorithms can generate a TSK model with good global performance (i.e., the model is capable of approximating the given system with arbitrary accuracy, provided that sufficient rules are used and sufficient training data are available), they cannot guarantee the resulting model to have a good local performance. Often, the submodels in the TSK model may exhibit an erratic local behavior, which is difficult to interpret. Since one of the important motivations of using the TSK model (also other fuzzy models) is to gain insights into the model, it is important to investigate the interpretability issue of the TSK model. In this paper, we propose a new learning algorithm that integrates global learning and local learning in a single algorithmic framework. This algorithm uses the idea of local weighed regression and local approximation in nonparametric statistics, but remains the component of global fitting in the existing learning algorithms. The algorithm is capable of adjusting its parameters based on the user’s preference, generating models with good tradeoff in terms of global fitting and local interpretation. We illustrate the performance of the proposed algorithm using a motorcycle crash modeling example. Index Terms—Fuzzy modeling, fuzzy systems, learning algorithms, TSK model. I.
Genetic reinforcement learning through symbiotic evolution for fuzzy controller design
 IEEE Trans. Syst., Man, Cybern., Part B
"... Abstract—An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuz ..."
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Cited by 38 (5 self)
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Abstract—An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this SymbioticEvolutionbased Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GAbased fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSKtype fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSKtype fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cartpole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GAbased fuzzy systems. Index Terms—Genetic reinforcement, fitness value, fuzzy partition, symbiotic evolution, TSKtype fuzzy rules. I.