The application of a neural-fuzzy logic controller to process control

Kelly, Dwayne; Burton, P.; Rahman, M. A.

doi:10.1109/ijcf.1994.375131

Cited by 5 publications

(2 citation statements)

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“…), and the work developed in [24] was the seminal work. The traditional neurofuzzy models have a limited capacity of adjustment in their structure, and due to the rule number explosion problem these models are generally used in the applications with restrict input number [25]. Moreover, the major part of these neurofuzzy systems are appropriate for supervised training.…”

Section: B the Intelligent Control Based On Neurofuzzymentioning

confidence: 99%

NeuroFuzzy Controller Design and FPGA-Based Embedded System for ACROBOT Model

S.E.CastroCaicedo¹

2017

IJCDS

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This work presents the modeling and control system design for a robot that rides a bicycle using the well-known Acrobot model for slow speeds, which was finally implemented in an FPGA-based embedded system. In this work the implementation of the controller was achieved for the Acrobot option following two ways. The first one implementation was developed based on modern control theories, involving:(a) the states feedback controller issues based on the appropriated poles allocation, guarantying stability and (b) the designs of a LQR (Linear Quadratic Regulator) type controller that minimizes the criterion of quadratic cost. The second one implementation was in turn achieved by means of some intelligent control methods, involving: (a) artificial neural networks, (c) tuning a neurofuzzy system, which joints the capacity of learning of the neural networks with the power of linguistic interpretation of the fuzzy inference systems (neurofuzzy system ANFIS, Adaptive NeuroFuzzy Systems) and (c) fuzzy logic. For testing the designed controller, a simulator has been also developed and connected to the controller embedded in FPGAs.

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Section: B the Intelligent Control Based On Neurofuzzymentioning

confidence: 99%

NeuroFuzzy Controller Design and FPGA-Based Embedded System for ACROBOT Model

S.E.CastroCaicedo¹

2017

IJCDS

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“…Η χρήση του ANFIS για την ανάπτυξη ανάστροφου ελεγκτή (inverse controller) χρησιμοποιήθηκε από τους Kelly, D. (1994), Jang, (1997. Επίσης το ANFIS έχει χρησιμοποιηθεί στην ανάπτυξη ελεγκτή από τους Kwok, H.F. (2003).…”

Section: Layerunclassified

Σύστημα Πρόβλεψης Της Βραχυχρόνιας Τάσης Της Τιμής Των Μετοχών Με Χρήση Νευρο-Ασαφών Μεθόδων

Ατσαλάκης¹,

Atsalakis²

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