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.
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.
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 present paper the characteristics and opportunities of application of the system of parallel input-output of information from the fiber-optical measuring network into computer are considered. The system consists of two pars: on manframe and several expansion blocks. The first part is internal, is connected directly in the socket of the motherboard of the personal computer. It is designed for buffering system signals and development of cojmands of controlling by the system for input-output of signals into personal computer and signals generation onto expansion blocks. The second part is external, connects to the mainframe by means of cables. It designed for transformation of information from the fiber-optical measuring network into signalsof rthe mainframe and instrument settings adaptation. The analysis of speed of procesing of analog and digital data by system is presented. The possible schemes of use of the system for processing quasistationary and dynamic fields are considered.
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.
The new principals of organization of parallel input-output of the optical information in the personal computer from the fiber-optical measuring lines are considered. The device has block structure and has two modes of operation: calibration mode of operation and work mode of operation. In the calibration mode of operation computing system is adaptation to condition of the solution problem of reconstruction information about parameters of monitoring physical fields. In the work mode of operation the device implements the adaptive processing of incoming optical radiation.
The approximate algebraic methods of the solution of a tomography problem of restitution of the performance of extended physical fields in matching with neural-like method of the solution of such problem are considered. The analyses of methods and results of modeling are made.
KEYWORDS: Fiber optics, Fiber optics tests, Fiber optic networks, Error control coding, Data processing, Chemical elements, Optical networks, Logic, Control systems, Data storage
The principle of optical information collection from the fiber-optical measuring network (FOMN) is submitted. The principle of functioning 1-Wire network standard lays in the basis of the implemented method. The represented device is a function block, intended for collecting optical information from 4 fiber-optical measuring line (FOML), converting optical information into digital signals and delivery of digital information about intensity of laser radiation into FOML. The device has a very small amount of elements, that considerably makes more cheap practical realization of such blocks, has small overall dimensions and good operating performances.
The method of output signal processing for distributed fiber-optical measuring networks is developed. This method is based on neurel-like principles of data processing. Mathematical model of the three-layered perceptron was used for reconstruction of physical field distribution measured by fiber-optic distributed network.
The block diagram of the device intended for data processing organize from fiber-optical measuring network (FOMN), modeling and controlling parameters of the temperature field for FOMN is submitted. The principle of functioning 1-Wire netowrk standard lays in the basis of the device. The practical realization of this system allows to collect optical information from 15 fiber-optical measuring lines (FOML), formed the FOMN with packing 4x4, convert optical information into digital signals and delivery digital information about intensity of laser radiation into FOML. The part of modeling and controlling the parameters of the temperature field is necessary to form a matrix of connections of optical neural network.
KEYWORDS: Neural networks, Neurons, Numerical modeling, Mathematical modeling, Data processing, Chemical elements, Multidimensional signal processing, Control systems, Evolutionary algorithms, Detection and tracking algorithms
The problem of optimization of training of the neural networks perceptron type is considered. The method of the select of parameter of the speed of training based on combination of optimum parameters is offered: optimum parameter selected before the beginning training and the optimum parameter after the first cycle of training. The mathematical calculations and deductions of optimum parameter of the speed of training represented. The numerical modeling on an example of the neural networks perceptron type is realized. Is shown that the combination optimum parameter of the speed of training on a first step of training with optimum parameter of the speed of training received previously improves the result of training more than on 0,24% and the speed of training more than one and a half.
The main advantages of neural networks are the flexibility of architecture and ability to training. These advantages allow using neural networks for solving many difficult problems. One of them is the processing of data collected by fiber-optical measuring systems. The principles of organization of optical neural-like system for analog data processing are represented. The practical realization of this system on a matrix of photoelectric cells allows to obtain a parallel processing of an optical information for physical field distribution reconstruction.
In the given paper principles of organization of neural-like system consisting of a matrix of photoelectric cells are represented. The practical realization of this system allows to obtain a parallel processing of an optical information for environmental physical field monitoring . A computer model of the feed-forward neural network with the hidden layer is developed to reconstruct physical field investigated by the fiber optic measuring system. The Gaussian distributions of some physical quantity are selected as learning patterns. Neural network is learned by error back-propagation using the conjugate gradient and coordinate descent minimization of deviation. Learned neural network reconstructs the two-dimensional scalar physical field with distribution having one or two Gaussian peaks.
Main advantages of neural networks are the flexibility of architecture and ability to training. These advantages allow using neural networks for solving many difficult problems. One of them is the processing of data collected by fiber- optical measuring systems. We offered universal neural-like system for optical data processing in optical measuring system.
The main advantages of a neural networks are the flexibility of architecture and ability to training. It allows to perform data processing even when the processing procedure can not be present by the known function. The neural networks based on optical elements allow real time operation rather than electronic neural networks. We offered system, based on neural networks, capabling to solve a broad class of heterogeneous problems and adapting to input information flow. The system are based on neural networks of two types: (1) a single-pass network of perceptron type and (2) recognize network of Hopfield network type. The neural network of the type (1) is realized by holographic scheme and experimental checked when processing the signals fiber- optic measuring network.
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