Using the iterative learning algorithm as data source for ANFIS training

Boldicor, C.; Comnac, V.; Topa, I.; Coman, Simona

doi:10.1109/aqtr.2010.5520759

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Other more complex training algorithms have been proposed in the scientific literature [11][12][13][14]. In this paper we present a less complex method and with high capacity of generalization, based on a GA, called G-ANFIS.…”

Section: Training Algorithmsmentioning

confidence: 98%

Evolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory

et al. 2011

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In this paper an evolutive algorithm is used to train an adaptative-network-based fuzzy inference system (ANFIS), particularly a genetic algorithm (GA). The GA is able to train the antecedent and consequent parameters of an ANFIS, which is used for energy load profile forecasting in an automated factory. This load forecasting is useful to support an intelligent energy management system (IEMS), which enables the user to optimize the energy consumptions by means of getting the optimal work points, scheduling the production according to these points, etc. The proposed training algorithm showed excellent results with complex plants like industrial energy consumers in the user-side, where the randomness of the loads is higher than in utility loads. Real data from an automated car factory were used to test the presented algorithms. Appropriated results were obtained.Peer ReviewedPostprint (published version

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Section: Training Algorithmsmentioning

confidence: 98%

Evolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory

et al. 2011

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“…Adaptation of parameters can be made in different ways [3], [4]:  gradient based training, reinforcement Learning [5]  using a generic algorithm for tuning the parameters [6], [7], [8], [9]  particle swarm optimization, hybrid learning algorithm [3]. At the ANFIS hardware implementation we took into consideration that parameters to be tunable from the embedded processor, thus the testing of different training algorithms being possible.…”

Section: A Training Algorithmmentioning

confidence: 99%

Embedded Adaptive Neuro Fuzzy Inference System with Hardware Implemented Real Time Parameter Update

2015

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Abstract:In the paper a Sugeno architecture based hardware implemented neuro adaptive inference system's training algorithm is presented. The block diagram of the neuro-adaptive inference system output computing implemented in hardware is discussed, and the implementation in reconfigurable circuit of real-time parameter tuning is presented. The proposed system functionality based on measurements achieved is demonstrated. The resulted architecture has a very high processing speed, and the parameter adaptation works in parallel with the output processing. The proposed architecture can also be used for different training algorithms' development.

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STLF in the user-side for an iEMS based on evolutionary training of Adaptive Networks

Cárdenas

Giacometto

García

et al. 2012

Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies &Amp; Factory Automation (ETFA 2012)

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Using the iterative learning algorithm as data source for ANFIS training

Cited by 5 publications

References 19 publications

Evolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory

Evolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory

Embedded Adaptive Neuro Fuzzy Inference System with Hardware Implemented Real Time Parameter Update

STLF in the user-side for an iEMS based on evolutionary training of Adaptive Networks

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