A Dynamic Estimation of Service Level Based on Fuzzy Logic for Robustness in the Internet of Things

Jia, Bing; Hao, Lifei; Zhang, Chuxuan; Chen, Dong

doi:10.3390/s18072190

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…The modeling has many applications, such as prediction [1][2][3][4], pattern recognition [5][6][7][8], detection [9][10][11], and classification [12][13][14]. The backpropagation is a supervised algorithm was used in a radial basis mapping (RBM) network for the modeling; the value of the scale parameters was adjusted according to a cost [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm with Radial Basis Mapping Network for the Electricity Consumption Modeling

et al. 2020

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The modified backpropagation algorithm based on the backpropagation with momentum is used for the parameters updating of a radial basis mapping (RBM) network, where it requires of the best hyper-parameters for more precise modeling. Seeking of the best hyper-parameters in a model it is not an easy task. In this article, a genetic algorithm is used to seek of the best hyper-parameters in the modified backpropagation for the parameters updating of a RBM network, and this RBM network is used for more precise electricity consumption modeling in a city. The suggested approach is called genetic algorithm with a RBM network. Additionally, since the genetic algorithm with a RBM network starts from the modified backpropagation, we compare both approaches for the electricity consumption modeling in a city.

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Section: Introductionmentioning

confidence: 99%

Genetic Algorithm with Radial Basis Mapping Network for the Electricity Consumption Modeling

et al. 2020

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“…Networks have many applications like detection [1,2], recognition [3,4], classification [5,6], and prediction [7,8]. Steepest descent is a supervised algorithm that is frequently used for neural network tuning, wherein the value of the scale parameters is adjusted according to the cost map.…”

Section: Introductionmentioning

confidence: 99%

Hessian with Mini-Batches for Electrical Demand Prediction

et al. 2020

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The steepest descent method is frequently used for neural network tuning. Mini-batches are commonly used to get better tuning of the steepest descent in the neural network. Nevertheless, steepest descent with mini-batches could be delayed in reaching a minimum. The Hessian could be quicker than the steepest descent in reaching a minimum, and it is easier to achieve this goal by using the Hessian with mini-batches. In this article, the Hessian is combined with mini-batches for neural network tuning. The discussed algorithm is applied for electrical demand prediction.

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“…The evaluation method is similar to R. Jain's method of arrangement (Jain, 1977) that is based on the weighted estimate aggregating. As estimate processing can be described with the help of many rules, the method forms the fuzzy set of extra estimates using aggregation based on the rules, and it can be also programmed as a fuzzy system (Jia et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Planning the Sowing Structure Applying Optimization Models

Sedlak¹,

Pejanović²,

Ćirić³

et al. 2018

AgricultForest

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The selection of agricultural plants for sowing and planning the economic success in production is done in conditions of uncertainty and high risk. The factors, which cause the biggest uncertainty in achieving returns in agricultural production, can be quantified by the valuation method. The sense, value, manner and process of decision-making problems are determined by the cultural, social, temporal, value, as well as logical context. Fuzzy logic was developed more than five decades ago. T he characteristics of fuzzy logic include operating by fuzzy notions, imprecise authentication tables, and fuzzy inference rules. All these characteristics of fuzzy logic are highly important, especially if we try to exchange or supplement the long-dominating approach of decision making in agricultural management with the descriptive one.The criteria, limitations and performances of measures of alternatives bear in themselves some aspects of indefiniteness: in determinativeness, multiple aspects of meaning, incompleteness and fuzziness. Operational research offers optimization models aimed at finding an activity programme that will yield the best possible results. The models use precisely determined and known data.Constraints are also precisely determined, and the goal function is clearly defined, so that it can be formulated easily and simply. Reality, especially in the agricultural production, however, is different: very often we lack precise information on the value of individual input parameters, or the values of coefficients in constraint and goal functions, and imprecise formulation of limitations themselves is possible as well.This paper presents the valuation method at planning the structure of sowing: wheat, chamomile and mint.

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A Dynamic Estimation of Service Level Based on Fuzzy Logic for Robustness in the Internet of Things

Cited by 8 publications

References 21 publications

Genetic Algorithm with Radial Basis Mapping Network for the Electricity Consumption Modeling

Genetic Algorithm with Radial Basis Mapping Network for the Electricity Consumption Modeling

Hessian with Mini-Batches for Electrical Demand Prediction

Planning the Sowing Structure Applying Optimization Models

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