A Hierarchical Procedure for the Synthesis of ANFIS Networks

Panella, Massimo

doi:10.1155/2012/491237

Cited by 26 publications

(10 citation statements)

References 48 publications

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“…. N. It is made of M different fuzzy rules, and the coefficients are obtained through the use of a clustering procedure in the joint input-output space [48].…”

Section: Honfismentioning

confidence: 99%

Prediction in Photovoltaic Power by Neural Networks

et al. 2017

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Abstract:The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches.

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“…. N. It is made of M different fuzzy rules, and the coefficients are obtained through the use of a clustering procedure in the joint input-output space [48].…”

Section: Honfismentioning

confidence: 99%

Prediction in Photovoltaic Power by Neural Networks

et al. 2017

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“…The main drawback due to conventional clustering approaches is that induced clusters do not always reflect the real data structure. An innovative approach, dubbed Hyperplane Clustering Synthesis (HCS), was firstly proposed in [13]. By HCS, clusters are determined in joint input-output space, with the rules' shape corresponding to the underlying function adopted for the output, that was a hyperplane in the case of ANFIS linear rules.…”

Section: A Synthesis Of Rules By Clusteringmentioning

confidence: 99%

A higher-order fuzzy neural network for modeling financial time series

Panella

Liparulo

Proietti

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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This work investigates on the widespread use of fuzzy neural networks in time series forecasting, concerning in particular the energy commodity markets. We propose a new learning strategy suited to any neural model. The proposed approach is further assessed in the case of higher-order Sugenotype fuzzy rules, which are able to replicate the daily data and to reproduce the same statistical features for various Commodity time series. The data used are obtained from the daily return series of specific energy commodities, such as coal, natural gas, crude oil and electricity, over the period 2001-2010 for both the European and US markets. We will prove that our approach can obtain interesting results in terms of prediction accuracy and volatility estimation, compared to well-known neural and fuzzy neural models and to the ARMA-GARCH statistical paradigm.

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“…This inductive approach often involves the use of fuzzy neural models of the battery [12]- [14]. It seems to be the only one able to handle the large variability of the battery behavior, as further discussed successively in the paper.…”

Section: Background Of Battery Modelsmentioning

confidence: 99%

A data driven circuit model for rechargeable batteries

Panella

Proietti

2014

2014 IEEE International Symposium on Circuits and Systems (ISCAS)

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A valid model of a rechargeable battery is required in several applications, especially to determine its internal charge. Due to the excessive variability of battery behavior, a large amount of data, experimentally obtained and suitably coded, should accompany the model. Exploiting the power of the fuzzy neural approach is a usual methodology to meet this requirement. However, also a strictly circuital approach is feasible, as proposed in the present paper. We suggest the use of a simple circuit model constituted by the series of a nonlinear capacitor and a memristor. Their different characteristics, experimentally determined, are compacted under the form of two small sets of vectors to be associated with the battery model.

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A Hierarchical Procedure for the Synthesis of ANFIS Networks

Cited by 26 publications

References 48 publications

Prediction in Photovoltaic Power by Neural Networks

Prediction in Photovoltaic Power by Neural Networks

A higher-order fuzzy neural network for modeling financial time series

A data driven circuit model for rechargeable batteries

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