Application of Fuzzy Cognitive Maps for Researching the Process of Thermomechanical Processing of Fibrous Materials
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Fibre Chemistry, Vol. 52, No. 2, July, 2020 (Russian Original No. 2, March-April, 2020)
RESEARCH METHODS APPLICATION OF FUZZY COGNITIVE MAPS FOR RESEARCHING THE PROCESS OF THERMOMECHANICAL PROCESSING OF FIBROUS MATERIALS M. S. Ivanov UDC 681.5 (07) The possibility of using fuzzy cognitive maps in the study of the deformation properties of a fibrous material processed in the thermomechanical zone of the drying chamber of the unit for the production of bulk non-woven material is considered. The connection between the concepts is determined, a system of equations is derived, and the training of fuzzy cognitive maps is described. The methodology of cognitive modeling of complex dynamic objects is presented and a list of concepts characterizing the state of the object of study is presented. Based on the analysis and modeling of the cognitive map, a transient graph of fuzzy cognitive maps is compiled.
In the study of the deformation properties of a fibrous material (FM) processed in the thermomechanical zone of the drying chamber (DC) of the unit for the production of bulk non-woven material, a device is used to control the heat fluxes of the processes of deformation of textile materials (Pat. 187519). The thermomechanical processing of the FM is controlled by a triple-motor complex parametric electric drive, consisting of a take-up pair (TP) drive, a conveyor outlet pair (OP) drive, and a fan (F) drive supplying a heat agent to the drying chamber. These drives are designed on the basis of asynchronous motors with hollow rotor (AM1, AM2, AM3), multifunctional energy-saving voltage regulators (VR1, VR2, VR3) and reduction drives (RD1, RD2, RD3). The surface density sensor (SDS) of the fibrous material installed at the output of the DC determines the degree of spin-drawing during its thermomechanical processing as the ratio of the linear velocities of the take-up pair (V2) and the linear velocity of the outlet pair (V1), i.e. E = V1/V2 (Fig. 1). If the degree of spin-drawing of the FM is within a given interval (Emin, Emax), then it is generally assumed that the final product is of the specified quality. In addition, according to the technological regulations, the temperature of the drying agent should not be lower than the allowable minimum (tmin) and not exceed the established upper limit (tmax). Thus, the goal of control is to ensure that the controlled variables E and t are in the following intervals: Emin ≤ E ≤ Emax; tmin ≤ t ≤ tmax. The following values were set: Emin = 1.0, Emax = 1.05, tmin = 0.83, tmax = 0.98. Consider the main stages of solving the problem using fuzzy cognitive maps (FCM). Given that fuzzy cognitive maps combine the advantages of fuzzy logic (do not require knowledge of exact mathematical models, are based on empirically derived IF–THEN rules, work well with poorly formalized objects), and also have the wellknown advantages of neural networks (non-linear description, training and self-training ability, focus on obtaining optimal control laws), it can be argued that FCMs are a practical tool for
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