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In this paper, the idea of the neuro-fuzzy learning algorithm has been extended, by which the tuning parameters in the fuzzy rules can be learned without changing the fuzzy rule table form used in usual fuzzy applications. A new neuro-fuzzy learning algorithm in the case of the fuzzy singleton-type reasoning method has been proposed. Due to the flexibility of the fuzzy singleton-type reasoning ...

The presented control scheme utilizes Adaptive Neuro Fuzzy Inference System (ANFIS) controller to track rotational speed of a reference engine and disturbance rejection during engine idling. To evaluate the performance of the controller a model of the system is developed and simulation results are presented. It is shown that the ANFIS controller is suitable for control systems with large time d...

This paper proposes a new method for online estimation of the induction generator parameters by means of adaptive neuro-fuzzy inference systems (ANFIS). The suggested technique can be applied to induction generators that are used in wind energy conversion systems (WECS). The WECS structure comprises a wind turbine, a three-phase induction generator and two back-to-back power converters. The WEC...

1 This paper describes CINTIA, a Neuro-Fuzzy real-time controller based on Pulse Stream computation techniques and designed for applications in low power embedded systems. The proposed system mixes two di erent approaches, namely Neuro-Fuzzy Controllers and Finite State Automata. The former are implemented by means of a custom neural chip (manufactured by ES2), while the latter are implemented ...

This is the pre-peer reviewed version of the article: Mohanty J R, Verma B B, Ray P K, Parhi D R K, Prediction of mode-I overload-induced fatigue crack growth rates using neuro-fuzzy approach, Expert Systems with Applications, 37(2010), 3075-3087, which has been published in the final form at http://www.sciencedirect.com/science/journal/09574174 Prediction of mode-I overload-induced fatigue cra...

The present paper describes the development of a modular neuro-fuzzy system, designated for enhanced and flexible fault diagnosis. Some specific neural algorithms for identification, recognition, evaluation and classification of the process parametric values are created and applied in the systems structure. The so-developed neuro-fuzzy system is then applied for fault diagnosis in an industrial...

This paper presents a fuzzy perceptron as a generic model of multilayer fuzzy neural networks, or neural fuzzy systems, respectively. This model is suggested to ease the comparision of diierent neuro{fuzzy approaches that are known from the literature. A fuzzy perceptron is not a fuzziication of a common neural network architecture, and it is not our intention to enhance neural learning algorit...

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in this paper, an adaptive neuro fuzzy inference system (anfis) based control is proposed for the tracking of a micro-electro mechanical systems (mems) gyroscope sensor. the anfis is used to train parameters of the controller for tracking a desired trajectory. numerical simulations for a mems gyroscope are looked into to check the effectiveness of the anfis control scheme. it proves that the sy...