fuzzy inference
A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system
... underlying fuzzy inference ...a fuzzy inference system (FIS) whose membership function parameters are tuned (adjusted) using either a backpropagation algorithm alone, or in combination with a ...
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Decremental Learning of Evolving Fuzzy Inference Systems Using a Sliding Window
... Evolving classification systems have appeared in the last decade to meet the need for recognizers that work in chang- ing environments. They use incremental learning to adapt to the data flow and to cope with class ...
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Adaptive Neuro-Fuzzy Inference System for mid term prognostic error stabilization.
... neuro-fuzzy inference system (ANFIS) [5]. ANFIS is an inference system in which the parameters associated with speci cs memberships functions are computed using either a backpropagation gradient ...
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Decremental Learning of Evolving Fuzzy Inference Systems, Application to Handwritten Gestures Recognition
... customizable fuzzy inference system for ...evolving fuzzy inference ...of fuzzy rules ...Evolving Fuzzy Inference System; Recursive Least Squares; Concept Drifts; ...
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ParaFIS:A new online fuzzy inference system based on parallel drift anticipation
... Rennes, France [email protected] Abstract—This paper proposes a new architecture of incremen- tal fuzzy inference system (also called Evolving Fuzzy System - EFS). In the context of ...
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Diagnosis Of Rotor Fault Using Neuro-fuzzy Inference System
... Neuro-Fuzzy Inference System (ANFIS) is the adaptive networks that are functionally equivalent to fuzzy inference system ...Sugeno-type fuzzy inference ...a Fuzzy ...
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Evaluation of surface water quality for drinking purposes using fuzzy inference system
... The water quality index is assessed with the FWQI index, and the results show that the values of WQI and FWQI have similar characteristics regarding the water quality index. Total cita[r] ...
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Evaluation of surface water quality for drinking purposes using fuzzy inference system
... Water-rock interactions and anthropogenic process are the main factors that are controlling the surface water quality. The water quality index is assessed with the FWQI index, and the r[r] ...
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Incremental Learning Of Evolving Fuzzy Inference Systems : Application To Handwritten Gesture Recognition
... Figure 5.8: Incorporating lognormal-based data generation in the learning of evolving handwriting classifiers Using a visualization interface developed by Scribens laboratory that allows[r] ...
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Techniques Based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Estimating and Evaluating Physical Demands at Work Using Heart Rate
... are: fuzzy logic (FL) or fuzzy systems, artificial neural networks (ANN), evolutionary computing, machine learning and probabilistic reasoning ...namely fuzzy systems, artificial neural networks and ...
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Search by Fuzzy Inference in a Children's Dictionary
... / La version de cette publication peut être l’une des suivantes : la version prépublication de l’auteur, la version acceptée du manuscrit ou la version de l’éditeur. Access and use of [r] ...
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2010 — Adaptive network-based fuzzy inference systems for sensorless control of PMSG based wind turbine with power quality improvement features
... simple Fuzzy logic controller (FLC) has a narrow operating range and needs much more manual adjustments by trial and error for higher ...of fuzzy sytstem with the learning capability of ...
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Experiments and design of an inference fuzzy system
... C j at each presentation of examples from the error (y(x j ) − d j ). Unfortunately, in case of model invalidation, we cannot determine never learned rules that cause the gap between the model and the real system. More- ...
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Bipolar aggregation method for fuzzy nominal classification using Weighted Cardinal Fuzzy Measure (WCFM)
... Cardinal Fuzzy Measure As stated in previous paragraph, the difficulty of computing Choquet integral is to define a fuzzy measure over the set N that necessitates obtaining 2 |N | − 2 coefficients that ...
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Approximate Decentralized Bayesian Inference
... decentralized inference (Broderick et ...variational inference, this algorithm leads to poor decentralized pos- terior approximations for unsupervised models with inher- ent ...sampling inference on ...
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Survey sampling targeted inference
... surely. We elaborate further on this issue in Proposition 0.4 below. We wish to follow the same strategy as in Section 3.1 , i.e., to define possibly unequal first order inclusion probabilities depending on V 1 , . . . , ...
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Bayesian inference algorithm on Raw
... For the medium grain implementation, the problem size is split among the tiles (not replicated), so the total load-up time should remain about the same as the number of [r] ...
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Mod/Resc Parsimony Inference
... the problem cannot be approximated within a factor of (n + m) 1/3−ε unless P = NP. 4 Fixed-parameter tractability In this section, we explore a parameterized complexity approach [4, 9, 14] for the Mod/Resc Par- simony ...
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An Extension of SPARQL with Fuzzy Navigational Capabilities for Querying Fuzzy RDF Data
... attach fuzzy degrees to triples, as proposed in ...“simple” fuzzy SPARQL queries ...global fuzzy condition in the query is selective enough for avoiding a plethoric set of answers to ...
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Randomized parcellation based inference.
... Note that a direct comparison of the sensitivity of the different procedures (voxel-level, cluster-level, TFCE, parcel-based), i.e. their rate of detections, is not very meaningful. Indeed, only voxel-level statistics ...
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