Forecasting and Diagnosing Cardiovascular Disease Based on Inverse Fuzzy Models

Chernova, I. V.; Сумин, С. А.; Bobyr, M. V.; Seregin, S. P.

doi:10.1007/s10527-016-9545-y

Cited by 16 publications

(2 citation statements)

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“…Let the tilt angles defined to formulas (1)- (3) are input variables of the fuzzy system. Further, consider the fuzzy MISO-system having two input variables: Input and output variables are described by parametric triangular membership functions (MF), which are shown in Figure 5, where μ (x), μ (y), μ (s) are the membership functions of fuzzy sets X, Y, Speed, respectively [9,10]. Fuzzy base of knowledge is set by fuzzy rules (FR) and is shown in Table 1.…”

Section: Fuzzy Model Of Distribution Of Braking Forces On the Enginesmentioning

confidence: 99%

Fuzzy System of Distribution of Braking Forces on the Engines of a Mobile Robot

2016

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Abstract. The article presents a fuzzy system of distribution of braking forces on the engines of a mobile robot during its lifting and going down.The block diagram of the system of distribution of braking forces and location of sensors on a mobile robot is given in the paper. Also, fuzzy mathematical model of redistribution of braking forces depending on the conditions of the movement a mobile robot is shown in the article. The result of the simulation of control parameters are presented in the article. The control system of a mobile robot is demonstrated on the example of an autonomous mini-robot on platform Pirate under the control of microprocessor Arduino Mega 2560.

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Section: Fuzzy Model Of Distribution Of Braking Forces On the Enginesmentioning

confidence: 99%

Fuzzy System of Distribution of Braking Forces on the Engines of a Mobile Robot

2016

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“…In the papers [9,10] authors present a model based on the fuzzy sets theory to rank a risk of sea vessel collision in a heavy traffic zone to agree on the navigational decision support because of safe ship control needed. In this model, a maneuvering board was used to draft a fuzzy production rules system [11][12][13]. In addition, a well-known Mamdani Algorithm was used as an algorithm for fuzzy logic conclusion.…”

Section: Introductionmentioning

confidence: 99%

The Neural-Fuzzy Approach as a Way of Preventing a Maritime Vessel Accident in a Heavy Traffic Zone

Sedova

Sedov

Баженов

2018

Advances in Fuzzy Systems

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The paper dwells on the methodology of neural-fuzzy approach to solving the problem of ship collision prevention in a heavy traffic zone. The authors present the technique of using maneuvering board to form the elements of learning sample. The authors prove that it is rational to use a neural-fuzzy system, where generation is carried out by the lattice method without clustering. The authors investigate the effect of optimization on the quality differences. The researchers define optimal membership functions that are used to generate the input linguistic variables of a neural-fuzzy system.

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