The perspective of neural networks equivalental models (EM) base on vector-matrix procedure with basic operations of continuous and neuro-fuzzy logic (equivalence, absolute difference) are shown. Capacity on base EMs exceeded the amount of neurons in 2,5 times. This is larger than others neural networks paradigms. Amount neurons of this neural networks on base EMs may be 10 -20 thousands. The base operations in EMs are normalized equivalency operations. The family of new operations "equivalency" and "non-equivalency" of neuro-fuzzy logic's, which we have elaborated on the based of such generalized operations of fuzzy-logic's as fuzzy negation, t-norm and s-norm are shown. Generalized rules of construction of new functions (operations) "equivalency" which uses relations of t-norm and snorm to fuzzy negation are proposed. Among these elements the following should be underlined: 1) the element which fulfills the operation of limited difference; 2) the element which algebraic product (intensifier with controlled coefficient of transmission or multiplier of analog signals); 3) the element which fulfills a sample summarizing (uniting) of signals (including the one during normalizing). Synthesized structures which realize on the basic of these elements the whole spectrum of required operations: t-norm, s-norm and new operations -"equivalency" are shown. These realization on the basic of new multifunctional optoelectronical BISPIN-devices (MOEBD) represent the circuit with constant and pulse optical input signals. They are modeling the operation of limited difference. These circuits realize frequency-dynamic neuron models and neural networks. Experimental results of these MOEBD and "equivalency" circuits, which fulfil the limited difference operation are discussed. For effective realization of neural networks on the basic of EMs as it is shown in report, picture elements are required as main nodes to implement element operations "equivalence" ("non-equivalence") of neuro-fuzzy logic's.
The aim of the research is to improve the technical parameters of ultrasonic meters by using the phenomenon of resonance and standing wave. The basis of the resonance method is the using standing acoustic waves arising in the medium due to the interference of the incident and reflected acoustic waves. The paper proposes a mathematical model of the ultrasonic resonance method for measuring parameters of liquid and gaseous media, which can be used for measuring control of parameters such as density, temperature, thickness, flow velocity, and others. To test the adequacy of the proposed model of ultrasonic wave propagation, its computer simulation and experimental studies were carried out. The air was chosen as the test medium (temperature 20° С, velocity 343m/s, atmospheric pressure 1atm). The time diagrams of the signal at the receiver for a distance of 34.3mm, when the resonance condition was satisfied, and for a distance of 34.73mm, when the ant resonance condition was satisfied, were modeled according to the proposed mathematical model. The dependence of the amplitude of the signal at the receiver is given for signal frequencies of 170–20kHz with a transmitter-to-receiver distance of 35.85mm and a sound speed of 340.8m/s. The simulation results confirm the adequacy of the purposed mathematical model. This allows proposing a new class of self-oscillating ultrasonic methods for measuring and control of medium parameters. The block diagram and the principle of operation of the auto-oscillating ultrasound meters for measuring the thickness, and gas temperature of test objects are described.
We analyse the existent methods of cryptographic defence for the facsimile information transfer, consider their shortcomings and prove the necessity of better information protection degree. The method of information protection that is based on presentation of input data as images is proposed. We offer a new noise-immune algorithm for realization of this method which consists in transformation of an input frame by pixels transposition according to an entered key. At decoding mode the reverse transformation of image with the use of the same key is used. Practical realization of the given method takes into account noise in the transmission channels and information distortions by scanners, faxes and others like that. We show that the given influences are reduced to the transformation of the input image coordinates. We show the algorithm in detail and consider its basic steps. We show the possibility of the offered method by the means of the developed software. The realized algorithm corrects curvature of frames: turn, scaling, fallout of pixels and others like that. At low noise level (loss of pixel information less than 10 percents) it is possible to encode, transfer and decode any types of images and texts with 12-size font character. The software filters for information restore and noise removing allow to transfer fax data with 30 percents pixels loss at 18-size font text. This percent of data loss can be considerably increased by the use of the software character recognition block that can be realized on fuzzy-neural algorithms. Examples of encoding and decryption of images and texts are shown.
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