In the given article the method of optical information gathering from the fiber-optical measuring network with its subsequent
processing is offered. In this method the algorithms of neural-like networks in computation process is introduced. Each
sensitive area of the fiber-optical measuring line is associated with the own amplifier. Adjustment of amplifiers gain factors
carries out modification of the weighting coefficients of the matrix of connections of the neural network. The training
principles to external physical influences are represented. The selection of the type of the neural network for decision of the
fiber-optical tomography problem of spatial distribution reconstruction has been considered.
A fiber-optic amplitude sensor based on the effect of microbending-caused upsetting of total internal reflection intended
for temperature monitoring is offered. The measuring breadboard is described for investigating the sensor characteristics.
It is shown that several sensors of this type can be integrated in fiber-optic measuring lines to be used in distributed fiberoptic
measuring networks. The characteristics of a fiber-optic measuring line composed of three fiber-optic microbending
amplitude sensors are investigated. The fiber-optical measuring network on base fiber-optic microbending amplitude
sensors with three-direction stacking of lines and dimension 4×4 is suggested.
The fiber-optical measuring system for reconstruct the characteristics of distributed physical fields on developed fiberoptical
measuring network is described. The results of reconstruction of two temperature influences with 46 and 74,5
Celsius degree value is represented.
The purpose of the given work is further solution of actual fiber-optical tomography problem of spatial distribution
reconstruction of the physical influences on the fiber-optical measuring networks. The problem of simultaneous
reconstruction of the places and values of ternary influences on fiber-optical measuring network from 3×3 to n×m
dimension is described. For discussion of this problem were used the algebraic methods for solution of the system
of linear algebraic equations. As the tomography data the integrated data coming from the fiber-optical measuring
lines, assembled according to the perpendicular stacking scheme on the fiber-optical measuring network were used.
The sensitive surface on the basis of fiber-optical measuring network with demodulation phase filters is offered. The purpose of the given work is further solution of actual fiber-optical tomography problem of spatial distribution reconstruction of the physical influences on the fiber-optical measuring networks. The problem of simultaneous reconstruction of the places and values of influences on fiber-optical measuring network from 4×4 dimension is described. For discussion of this problem were used the algebraic methods for solution of the system of linear algebraic equations with combinations of neural-like algorithms perceptron type. As the tomography data the integrated data coming from the fiber-optical measuring lines stacked on two and three directions on fiber-optical measuring network of researched area were used.
In the given article the method of optical information gathering form the fiber-optical measuring network with its subsequent processing is offered. In this method the algorithms of neural-like networks in computation process is introduced. Each sensitive area of the fiber-optical measuring line is associated with the own amplifier. Adjustment of amplifiers gain factors carries out modification of the weighting coefficients of the matrix of connections of the neural netowrk. The training principles to external physical influences are represented. The selection of the type of the neural network for decision of the fiber-optical tomography problem of spatial distribution reconstruction has been considered.
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