Hybrid Adaptive Systems of Computational Intelligence and Their On-line Learning for Green IT in Energy Management Tasks

Bodyanskiy, Yevgeniy; Vynokurova, Olena; Pliss, Iryna; Peleshko, Dmytro

doi:10.1007/978-3-319-44162-7_12

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…operation. In some instances, it is necessary to aggregate different data streams (big data), which are presented as random time series or random consequences [32]. For fuzzy information processing of such random streams or consequences, we can use three steps algorithm:…”

Section: Example Of Computational Library Applicationmentioning

confidence: 99%

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Computational Library of the Direct Analytic Models for Real-Time Fuzzy Information Processing

Kondratenko

2018

2018 IEEE Second International Conference on Data Stream Mining &Amp; Processing (DSMP)

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This paper reveals the computational library of the analytic models for the results of fuzzy arithmetic operations with fuzzy sets. In particular, the focus is on the synthesis of the universal inverse and direct models for maximum of triangular fuzzy numbers with different masks of their parameters. The results of the study verify the efficiency of the suggested computational library with soft computing models for fuzzy information processing in real-time control and decision making.

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Section: Example Of Computational Library Applicationmentioning

confidence: 99%

“…Step 1. Each random stream or consequence can be evaluated by interval value and presented as fuzzy set or fuzzy number [16,32]. For example, in [16] the realization of such random sequences is presented as triangular fuzzy number of such types -"approximate A" or "between B and C";…”

Section: Example Of Computational Library Applicationmentioning

confidence: 99%

Computational Library of the Direct Analytic Models for Real-Time Fuzzy Information Processing

Kondratenko

2018

2018 IEEE Second International Conference on Data Stream Mining &Amp; Processing (DSMP)

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“…The quality assessment results of the developed hybrid structure were obtained using such indicators [20,21]:…”

Section: Modellingmentioning

confidence: 99%

Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms

Bodyanskiy

Setlak

Peleshko

et al. 2015

2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applicati

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In this paper, a new approach for increasing the approximation accuracy with the use of computational intelligence tools is described. It is based on the compatible use of the neural-like structure of the Successive Geometric Transformations Model and the inputs polynomial extension. To implement such an extension, second degree Wiener polynomial is used. This combination improves the method accuracy for solving various tasks, such as classification and regression, including short-term and long-term prediction, dynamic pricing, as well as image recognition and image scaling, e-commerce. Due to the use of SGTM neural-like structure, the high speed of the system is maintained in both training and using modes. The simulation of the described approach is carried out on real data, the time results of the neural-like structure work and the accuracy results (MAPE, RMSE, R) are given. A comparison of the operation of the method with existing ones, such as Support vector regression, Classic linear SGTM neural-like structure, Linear regression (using Stochastic Gradient Descent), Random Forest, Multilayer Perceptron, AdaBoost are made. The advantages of the developed approach, in particular with regard to the highest accuracy among existing ones were experimentally established.

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“…At the intersection of cascade neural networks and deep stacking neural networks [2] deep stacking hybrid networks have emerged [19], [20], where hybrid generalized additive wavelet-neuro-neo-fuzzy systems (HGAWNNFS) were used as stacks-cascades [21]- [25], synthesized on the basis of hybrid systems of computational intelligence and generalized additive models [26]. These systems showed high quality of information processing and high enough speed, although the computational bulkiness of stacks-HGAWNNFS reduces the speed of the network learning.…”

mentioning

confidence: 99%

Deep Evolving Stacking Convex Cascade Neo-Fuzzy Network and Its Rapid Learning

Setlak¹,

Bodvanskiy²,

Pliss³

et al. 2018

Annals of Computer Science and Information Systems

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A deep evolving stacking convex neo-fuzzy network is proposed. It is a feedforward cascade hybrid system, the layers-stacks of which are formed by generalized neo-fuzzy neurons that implement Wang-Mendel fuzzy reasoning. The optimal in the sense of speed algorithms are proposed for its learning. Due to independent layer adjustment, parallelization of calculations in non-linear synapses and optimization of learning processes, the proposed network has high speed that allows to process information in online mode.

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Hybrid Adaptive Systems of Computational Intelligence and Their On-line Learning for Green IT in Energy Management Tasks

Cited by 6 publications

References 19 publications

Computational Library of the Direct Analytic Models for Real-Time Fuzzy Information Processing

Computational Library of the Direct Analytic Models for Real-Time Fuzzy Information Processing

Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms

Deep Evolving Stacking Convex Cascade Neo-Fuzzy Network and Its Rapid Learning

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