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27
Modified GathGeva Fuzzy Clustering for Identification of TakagiSugeno Fuzzy Models
 IEEE Transactions on Systems, Man, and Cybernetics
, 2001
"... The construction of interpretable TakagiSugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the GathGeva algorithm. To preserve the part ..."
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Cited by 33 (6 self)
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The construction of interpretable TakagiSugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the GathGeva algorithm. To preserve the partitioning of the antecedent space, linearly transformed input variables can be used in the model. This may, however, complicate the interpretation of the rules. To form an easily interpretable model that does not use the transformed input variables, a new clustering algorithm is proposed, based on the Expectation Maximization (EM) identification of Gaussian mixture models. This new technique is applied to two wellknown benchmark problems: the MPG (miles per gallon) prediction and a simulated secondorder nonlinear process. The obtained results are compared with results from the literature.
Variable Selection Using NeuralNetwork Models
, 2000
"... In this paper we propose an approach to variable selection that uses a neuralnetwork model as the tool to determine which variables are to be discarded. The method performs a backward selection by successively removing input nodes in a network trained with the complete set of variables as inputs. I ..."
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Cited by 14 (0 self)
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In this paper we propose an approach to variable selection that uses a neuralnetwork model as the tool to determine which variables are to be discarded. The method performs a backward selection by successively removing input nodes in a network trained with the complete set of variables as inputs. Input nodes are removed, along with their connections, and remaining weights are adjusted in such a way that the overall inputoutput behavior learnt by the network is kept approximately unchanged. A simple criterion to select input nodes to be removed is developed. The proposed method is tested on a famous example of system identification. Experimental results show that the removal of input nodes from the neural network model improves its generalization ability. In addition, the method compares favorably with respect to other feature reduction methods.
NeuroFuzzy Control of Structures using Acceleration
 Feedback,” Smart Materials and Structures
, 2001
"... This paper describes a new approach for reduction of environmentally induced vibration in constructed facilities by way of a neurofuzzy technique. The new control technique is presented and tested in a numerical study that involves two types of building models. Energy of each building is dissipated ..."
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Cited by 5 (0 self)
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This paper describes a new approach for reduction of environmentally induced vibration in constructed facilities by way of a neurofuzzy technique. The new control technique is presented and tested in a numerical study that involves two types of building models. Energy of each building is dissipated through magnetorheological (MR) dampers whose damping properties are continuously updated by a fuzzy controller. This semiactive control scheme relies on development of a correlation between accelerations of the building (controller input) and voltage applied to the MR damper (controller output). This correlation forms the basis for development of an intelligent neurofuzzy control strategy. To establish a context for assessing effectiveness of the semiactive control scheme, responses to earthquake excitation are compared with passive strategies that have similar authority for control. According to numerical simulation, MR dampers are less effective control mechanisms than passive dampers with respect to a single degree of freedom (DOF) building model. On the other hand, MR dampers are predicted to be superior when used with multiple DOF structures for reduction of lateral acceleration.
Hybrid Intelligent Systems Design  A Review of a Decade of Research
, 2000
"... The emerging need for Hybrid Intelligent Systems (HIS) is currently motivating important research and development work. The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniqu ..."
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The emerging need for Hybrid Intelligent Systems (HIS) is currently motivating important research and development work. The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs. Soft Computing (SC) introduced by Lotfi Zadeh [1] is an innovative approach to construct computationally intelligent hybrid systems consisting of Artificial Neural Network (ANN), Fuzzy Logic (FL), approximate reasoning and derivative free optimization methods such as Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS). Most of these approaches, however, follow an ad hoc design methodology, further justified by success in certain application domains. Due to the lack of a common framework it remains often difficult to compare the various hybrid systems conceptually and evaluate their performance comparatively. It has been over a decade since HIS were first applied to solve complicated problems. In this paper, we first aim at classifying stateoftheart intelligent systems, which have evolved over the past decade in the HIS community. Some theoretical concepts of ANN, FL and Global Optimization Algorithms (GOA) namely GA, SA and TS are also presented. We further attempt to summarize the work that has been done and present the current standing of our vision on HIS and future research directions.
Article Minimal Camera Networks for 3D Image Based Modeling of Cultural Heritage Objects
, 2014
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Neuro – Fuzzy Modeling for Dynamic System Identification
 IEEE Fuzzy Systems Symposium
, 1996
"... This paper presents the continued work of a previously proposed ANFIS (Adaptive NeuroFuzzy b z z y Inference System) architecture with emphasis on the applications to dynamic system identification. We demonstrate the use of ANFIS for the hair dryer modeling problem and compare its performance with ..."
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Cited by 3 (0 self)
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This paper presents the continued work of a previously proposed ANFIS (Adaptive NeuroFuzzy b z z y Inference System) architecture with emphasis on the applications to dynamic system identification. We demonstrate the use of ANFIS for the hair dryer modeling problem and compare its performance with the ARX model. 1
Compact TSFuzzy Models through Clustering and OLS plus FIS Model Reduction
 In Proc. of IEEE international
, 2001
"... The identification of uncertain and nonlinear systems is an important and challenging problem. Fuzzy logic models are often a good choice to describe such systems, however in many cases these become complex soon. Generally, too less effort is put into variable selection and in the creation of suitab ..."
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Cited by 3 (2 self)
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The identification of uncertain and nonlinear systems is an important and challenging problem. Fuzzy logic models are often a good choice to describe such systems, however in many cases these become complex soon. Generally, too less effort is put into variable selection and in the creation of suitable local rules. Moreover, in general no model reduction is applied, while this may simplify the model by removing redundant information. This paper proposes a combined method that handles these issues in order to create compact TakagiSugeno (TS) models that can be effectively used to represent complex systems. A new fuzzy clustering method is proposed for the identification of compact TSfuzzy models. The most relevant consequent variables of the TS model are selected by an orthogonal least squares method based on the obtained clusters. For the selection of the relevant antecedent (scheduling) variables a new method has been developed based on Fisher's interclass separability criteria. This overall approach is demonstrated by means of the MPG (miles per gallon) nonlinear regression benchmark. The results are compared with results obtained by standard linear, neurofuzzy and advanced fuzzy clustering based identification tools.
Optimal datadriven rule extraction using adaptive fuzzyneural models
, 2002
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www.elsevier.com/locate/fss Knowledge discovery by a neurofuzzy modeling framework
, 2004
"... In this paper a neurofuzzy modeling framework is proposed, which is devoted to discover knowledge from data and represent it in the form of fuzzy rules. The core of the framework is a knowledge extraction procedure that is aimed to identify the structure and the parameters of a fuzzy rule base, thr ..."
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In this paper a neurofuzzy modeling framework is proposed, which is devoted to discover knowledge from data and represent it in the form of fuzzy rules. The core of the framework is a knowledge extraction procedure that is aimed to identify the structure and the parameters of a fuzzy rule base, through a twophase learning of a neurofuzzy network. In order to obtain reliable and readable knowledge, two further stages are integrated with the knowledge extraction procedure: a preprocessing stage, performing variable selection on the available data to obtain simpler and more reliable fuzzy rules, and a postprocessing stage, that granulates outputs of the extracted fuzzy rules so as to provide a validity range of estimated outputs. Moreover, the framework can address complex multiinput multioutput problems. In such case, two distinct modeling strategies can be followed with the opportunity of producing both a single MIMO model or a collection of MISO models. The proposed framework is verified on a realworld case study, involving prediction of chemical composition of ashes produced by combustion processes carried out in thermoelectric generators located in Italy.
Delaunaybased Local Model Networks for Nonlinear System Identification
"... This paper presents an efficient methodology for nonlinear system identification based on Delaunay networks. These networks [1] share the learning capabilities of artificial neural networks but they are computationally much more efficient. In the approach discussed here, the interpolation nodes of a ..."
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This paper presents an efficient methodology for nonlinear system identification based on Delaunay networks. These networks [1] share the learning capabilities of artificial neural networks but they are computationally much more efficient. In the approach discussed here, the interpolation nodes of a Delaunay network are interpreted as local linear models of a nonlinear plant. Hence, standard parameter estimation techniques can be applied to train the network. Combined with a heuristic strategy for structural optimization, the proposed method constructs nonlinear models which can be implemented on lowcost microcontrollers and can meet strict realtime requirements. Thus, the method is a promising approach in fields of application where computer power is a limited resource. Two examples of automotive applications are discussed and exemplify the approach.