Associative recognition device

FIELD: physics.

SUBSTANCE: device comprises P units for selecting a maximum signal, P units for calculating an activation function, P groups of multiplier units, P groups of decoders and P groups of units for generating membership function values.

EFFECT: high accuracy of recognition when recognising objects in conditions without or with strong distortion of separate areas of an image, broader functional capabilities.

1 dwg

 

The invention relates to the field of data processing for special applications, in particular for the conversion of signals and images, asked unweighted digital codes in weighted codes, and can be used for processing and recognition of signals and images.

A device that contains the generators of the sawtooth voltage, analog-to-digital and digital-analog converters, elements OR blocks of memory of membership functions, blocks defining at least, blocks comparison blocks subtraction of units, registers, counter and delay components with corresponding connections [SU 1791815, G06F 7/58, 1990].

The disadvantage of this device are relatively narrow functionality.

It is also known device containing n parallel adders, inputs and outputs which are, respectively, the group of inputs and outputs of the device, and n blocks multiplied by the weighting coefficients, and the input of the i-th block is multiplied by the weighting coefficients (i=1...N) is connected to the output of the i-th parallel adder, and each of the outputs of the j-th block is multiplied by the weighting coefficients (j=1...N) is connected with the corresponding weighted input signal of the i-th adder (i = j) [Century A. Nazarov, A. I. Loskutov "Neural network prediction algorithms and optimization systems", St. Petersburg, Russia, "Science & Tech is", 2003, page 231].

A disadvantage of this device are also relatively narrow functionality.

In addition, a device containing a group of multipliers for weighting factors, the inputs of which are the inputs of the device, parallel adder, the inputs of which are connected to the outputs of the multipliers on the weights, and the computing unit activation function, whose input is connected to the output of the parallel adder, and the output is the output of the device [Redko Century, Evolution, neural networks, intelligence: Models and concepts of evolutionary Cybernetics. M: Komkniga, 2006, p. IP.5.1.].

The disadvantage of this device are also relatively narrow features, due to the fact that the device allows you to recognize the signals and images using simulated neuron in the assumption that the weights for the input signals can be determined accurately, so you can use them in the multipliers on the weights. In practice, the values of weight coefficients may not always be precisely defined, and in most cases their values are fuzzy, "blurred", which reduces the accuracy of the device and reduces the functionality of the known device.

The closest in technical essence to the present invention is the mustache is a device associative recognition, containing the first parallel adder and the first computing unit activation function, whose input is connected to the output of the first parallel adder, and the output is the first output of the device associative recognition, R-1 parallel adders from the second to the R-th, R-1 blocks calculate the activation function from the second through P-th inputs of each of which are connected to the outputs of the same parallel adders, and the outputs are the same outputs of the device associative recognition, and R groups of the first through P-th blocks forming the values of membership functions, the outputs of each of which are connected with inputs of the same name parallel adders, each of the R groups of the blocks forming the values of membership functions contains blocks To generate values of the membership function from the first to the K-th inputs of each of which are connected with inputs of the same name blocks values of the membership functions of each of the other groups of the R groups of the blocks forming the values of membership functions and input devices associative recognition [EN 2342702, C2, G06K 9/62, 27/06/2008].

The disadvantage is the closest technical solution is relatively narrow features and a relatively low recognition accuracy for the case when part of the requirements (WMO is different, excitatory signals characterizing the observed image is missing, i.e. the individual elements and the image area are either lacking or completely distorted. In this case, the summation of the values of the membership functions of the elements shown in the image may lead to masking of the objects being recognized due to the effect of missing or severely distorted elements and areas of the image, which reduces the reliability of their recognition.

The required technical result is to increase functionality and improve the recognition accuracy when recognizing objects in the absence and/or strong distortion of the individual areas of the image

The required technical result is achieved that the device containing the P blocks calculate the activation function and R groups of the blocks forming the values of membership functions, each of the R groups of the blocks forming the values of membership functions contains blocks To generate values of membership functions, the inputs of each of which are connected with inputs of the same name blocks values of the membership functions of each of the other groups of the R groups of the blocks forming the values of membership functions and input devices associative recognition, entered the R groups of decoders, with whom containing a series of decoders, the inputs of each of which are connected with inputs of the device associative recognition, the R groups of the tubes containing the multipliers, the inputs of each of which are connected to the outputs of the respective processing unit of membership function, the outputs of the corresponding decoder, and P blocks of the allocation of the maximum signal, the inputs of each of which are connected to the outputs of the respective group of multipliers, and the outputs of each of the R blocks allocation maximum signal connected to the input of the corresponding computing unit activation function of P blocks calculate the activation function.

Additionally, we note that, as will be shown below, the proposed solution can be implemented using known elements of digital technology. Therefore, it meets the criterion of industrial applicability.

The drawing shows an electrical block diagram of the device associative recognition (Fig.1).

The device associative recognition contains R block selection signals from the first 1-1 through 1-p and P blocks calculate the activation function of the first 2-1 to 2-R, the inputs of each of which is connected to the same outputs block selection signals 1-1...1-R.

The device associative recognition also contains R groups with the first 3-1 on R-u 3-R the locks generate values of membership functions, R groups with a 4-1 first through P-th 4-R multipliers and R groups with the first 5-1 on P-5-P of decoders, each of the R groups contains decoders To the decoders from the first to the K-th (5-1-1,5-1-2, ..., 5-1-K, ..., 5-P-K), the inputs of each of which are connected with inputs of the device, each of the R groups contains multipliers For multipliers from the first to the K-th (4-1-1, ... 4-1-K, ... 4-P-1, ... 4-P-K), the inputs of each of which are connected to the outputs of the same blocks forming the values of membership function R groups formations values of membership function and outputs the same name decoders R groups of decoders, and outputs connected to inputs of the same name allocation unit maximum signal 1-1...1-P, while each of the P groups of blocks forming the values of membership functions contains blocks To generate values of membership functions from first to K-th (3-1-1, ..., 3-1, ... 3-P-1, ..., 3-P-K), the inputs of each of which are connected with inputs of the same name blocks values of the membership functions of each of the other groups of the R groups of the blocks forming the values of membership functions and input devices associative recognition.

Block allocation maximum signal is a standard element of computing, and blocks the formation of values of membership functions and blocks calculate the activation function described options on the national level and in the proposed technical solution used examples of their perform from the device prototype.

The device works associative recognition as follows.

Preliminary analyses of the P groups of samples of signals or images by fuzzy "fuzzy" information, for example, through a noisy photos. Therefore, the lack of accurate reference images of samples is replaced by their associative fuzzy, "blurry" images. As a result, for each element of the R image is formed, the membership function of this element in accordance with the most typical signal level corresponding to the reference image. On the basis of such funkci is programming blocks forming the values of membership functions (3-1-1...3-1- ... 3-P-1...3-P-K).

To the inputs of blocks 3-1-1...3-1- ... 3-P-1...3-P To generate values of membership functions receive input (excitatory) signals. At the same time on the same inputs of blocks of each of the R groups of blocks serves the same signals, for example signals characterizing the same image. Each of these signals is converted into a corresponding block 3-1-1...3-1- ... 3-P-1...3-P-K value of membership function, which reflects the fuzzy representation of the compliance level of the input signal corresponding to the reference signal (picture element).

Each of the blocks 3-1-1...3-1- ... 3-P-1...3-P-K is configured to solved membership function.

In blocks 4-1-1, ... 4-1-K, ... 4-P-1, ... 4-P-multipliers generated membership function are multiplied by weighting factors that reflect the idea of the importance of the corresponding picture element in the reference image, which is determined by the value given by the relevant decoder.

Decoders 5-1-1, ..., 5-1-K, ..., 5-P-K depending on the levels of the input signals of the device return the value of the weighting factor for the corresponding picture element. In the absence of input signals, the coefficients on the remaining input signals may increase.

In blocks 1-1...1-R allocation of the maximum signal of the generated values of the signals from the multipliers corresponding groups of blocks of the multipliers are allocated the maximum signals that are fed to the inputs of corresponding units 2-1 to 2-R calculation of the activation function. In the simplest case they can be made in the form of threshold units.

When exceeding their threshold levels on the outputs generated the level of logical units, which signals the recognition of the input signal with a vague idea of the meaning of its individual elements.

Thus, in the proposed device achieves the required technical result consists in the extension of functionality, since the recognition from the expressions is performed using information about the situational significance of individual image elements and rank signal processing by allocating the maximum of the signal.

In addition, increases recognition accuracy, when required (input, excitatory signals characterizing the observed image, either lacking or completely distorted, i.e. the individual elements and the field of images do not coincide with those elements and regions of the images of recognizable objects, taking into account information about the significance of individual image elements. In the proposed technical solution, the selection of the maximum value of a set of values of membership functions virtually eliminates errors when recognizing when required (input, excitatory signals characterizing the observed image, are either absent or are completely distorted, i.e. the individual elements and the image area is almost completely not coincide with the corresponding elements and areas of the images of recognizable objects, taking into account the value of the data element.

The device associative recognition that contains P blocks calculate the activation function and R groups of the blocks forming the values of membership functions, each of the R groups of the blocks forming the values of membership functions contains blocks To generate values of membership functions, the inputs of each of which are connected with inputs of the same name units value the deposits of the membership functions of each of the other groups of the R groups of the blocks forming the values of membership functions and input devices associative recognition, characterized in that the input R groups of decoders, each of which contains the decoders, the inputs of each of which are connected with the inputs of the device, the R groups of blocks of multipliers, each of which contains K blocks multipliers, the inputs of each of which are connected to the outputs of the respective blocks forming the values of membership function of the R groups of the blocks forming the membership function and the outputs of the respective decoders of the R groups of decoders, and outputs connected to respective inputs R block selection signals, and the outputs of each of the R blocks allocation maximum signal connected to the input of the corresponding computing unit activation function of P blocks calculate the activation function.



 

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