Neuron network for detection of errors in symmetrical system of residual classes

FIELD: information technologies.

SUBSTANCE: invention may be used for building of modular neural computers, which function in symmetrical system of residual classes. Stated neuron network comprises unit of neuron network of end ring of senior coefficient generation for generalised positional system of numeration, unit of polarity shift, unit of error detection, buses "with errors" and "without errors".

EFFECT: reduced hardware complexity.

3 dwg

 

The invention relates to the field of computer engineering and can be used to build neural computers operating in a symmetric system of residual classes (JUICE).

A device for detecting errors in the information presented in the JUICE (patent No. 2022471, EN 5H03 M 13/00), which contains the power conversion system of residual classes in the generalized positional number system (JUICE-SVR), blocks of calculating the index number, the computing unit additional code index, the adder, the computing unit entiendes amount, the power comparison unit multiplication by a constant counter. The disadvantage of this device is the complexity of the hardware implementation and low performance.

The closest to this invention is adaptive parallel-pipelined neural network for error correction (patent RU.2279131, G06N 3/04), which contains the block neural network finite rings (NJC) formation of digits in the representation of the generalized positional numeral systems, the block error detection, block error correction and block reconfiguration and localization errors.

However, this device has a large amount of equipment and cannot function in a symmetric JUICE, which operates with both positive and negative, are presented in the JUICE.

The purpose of the invention is to reduce the amount of equipment the equipment. This objective is achieved in that the device entered the block offset of positive and negative regions of the dynamic range, which shifts the Smoking area of the full range, which are allowed negative numbers in the JUICE in the first half of the permitted working range, and allowed positive numbers JUICE shifts in the second half of the permitted area. The proposed shift provides accommodation for positive and negative numbers symmetric JUICE within the operating range of the excess JUICE that will correctly identify the errors of both positive and negative numbers represented in the JUICE.

The figure 1 presents the scheme of the neural network for detecting errors in a symmetric system of residual classes, which contains an input layer neuron 1 to neuron 7, 8, the outputs of which are connected unit shift polarity 2, including the NJC 9 modulo piwhere i=1,2,..., k+r, the outputs of which are connected with the block NJC 19 the formation of the senior ratio SVR 3, the outputs of which are connected with a block error detection 4, consisting of the keys 14 and 15, the outputs of which are the outputs of the neural networks for the detection of errors in a symmetric system of deductions.

The input layer neuron 1 neuron 7 and 8 plays the role of the input register, the input of which receives the values of the digits (residues) controlled bus number 5 and discharges constant shift bus 6. From the outputs of the neurons of the input layer 1, the data arrives at the inputs of the NJC 9 modulo, where i=1,2,..., k+r.

The result of the modular sum of the digits of a controlled number and discharge constant shift of the tire 10, 11, 12, 13 is fed to the input of the neural network determination of the senior ratio SVR 19, consisting of the NJC (patent RU 2256213) modulo piwhere i=1,2,..., k+r. The coefficient senior level SVR bus 18 is supplied to the information input keys 14 and 15, to the control inputs of which receive control signals on buses 16 and 17, respectively, "no error" and "error". Consider the properties of redundancy JUICE and the principle of determining the errors that occur in the codes of JUICE.

Excess JUICE has properties that can be used for error control and fault digital processors. Excess JUICE has a k - work and r - control reasons. To ensure the uniqueness of the representation of each base system JUICE every reason p1, R2,...,Rk,..., Rk+nshould be relatively simple. Working Foundation of the p1, R2,...,Rkrepresent a non-redundant bases, and control r Foundation pk+1..., Rk+rredundant. In excess JUICE number is k+r residual numbers α1α2,..., αkαk+1,..., αk+r. In the symmetric JUICE for the tiravanija negative numbers use additional code, thus

where

Residual numbers αlα2,..., αkare non-redundant numbers, and αk+1,..., αk+r- redundant. Full range of excess JUICE denoted by [O,R], wherecovers the full set of States, represented by all k+r residual numbers. The entire range is divided into an adjacent region defined non-redundant and redundant bases. Region [O, P] is called the working range and scope [O, R] represents the full range.

To get redundancy, the operands and results of arithmetic operations performed in the JUICE, should be taken at the same scale so that they always fall within the operating range. This limit specifies the additional range of the system (area calculation) [- (R-1)/2, (P-1)/2] for odd P and [-P/2, P/2] for even R. note that when encoding additional code, the negative part of the dynamic range is at the upper limit of the full range. Positive number of additional band appears in the region [O(P+1)/2] for odd P and on the field of [O,P/2] for even & Display dynamic range for the corresponding region is shown in figure 2.

As can be seen from the drawing, the dynamic range of the zones, consisting of positive and negative parts is divided into areas in the workplace and full range. This circumstance complicates the detection and correction of errors, because errors are detected by the fact that the number of falls in an invalid area of the full range. Due to the fact that negative numbers appear in the upper part of the invalid region of the full range, the operation error detection implemented by the condition a>R, is the assignment of all negative numbers are misleading, which is not true due to the diversity of the dynamic range.

To overcome this difficulty it is necessary to hold the shift negative by residual rotation of the ring in the position shown on figure 3, resulting in dynamic range will be clearly displayed in the working range.

Shown in figure 3, the rotation is called the offset polarity and can be done by adding before performing the operation of detecting errors constantfor odd P andto each And∈[0,R]. It should be noted that for non-redundant JUICE is vzaimootnoshenie correspondence between integers in the dynamic range and the condition of the permissible working range.

If ithe shift of polarity within the JUICE is

a simple summation of the residues according to the formulain which αicdenotes the residual digits after the shift polarity.

Neural network for error detection in a symmetric system of residual classes works as follows. In the first synchronization cycle controlled by the number in the JUICE and constants Ciwhere i=1, 2,..., k+r arrive at the inputs 5, 6 neurons 7,8. In the second cycle synchronization with the outputs of the neurons 7.8 discharges controlled numbers and constants fed to the input of the NJC pi9 block shift 2. The NJC Ri9 implement a computational model ofin which αicdenotes the residual symmetric figures JUICE after shifting polarity.

In the third cycle synchronization αicthe tire 10, 11, 12 and 13 are received at the inputs of the neural network determination of the senior ratio SVR 19 block 3.

In the fourth cycle synchronization values are big-endian SVR αnbus 18 is supplied to the information input keys 14 and 15, to the control inputs of which receive control signals on buses 16 and 17 and where the key inputs are received simultaneously two signals is formed or the signal "no errors"or the signal "there is an error".

Thus, the detection of an error in simmetrichnaya is carried out for four cycles synchronization.

Neural network for error detection in a symmetric system of residual classes containing block of the neural network end ring formation senior coefficient generalized positional number system, characterized in that it includes a block shift polarity, with inputs controlled numbers and constants shift polarity are connected with the neurons of the input layer, the outputs of which are connected to the inputs of the neural networks of the end rings of the unit shift polarity calculating model
where ai- discharges controlled numbers represented in the system of residual classes, to shift polarity, Withi- a constant shift, pi- module of the system of residual classes, i=1,2,..., k+r, aicresidual numbers of controlled numbers represented in the system of residual classes, after shifting polarity, the outputs of which are connected with inputs of block neural network end ring formation senior coefficient generalized positional numeral systems, the output of which is connected with the information input keys, block error detection, the control inputs of which are connected with tires "there are errors" and "no errors", the outputs of which are the outputs of the neural networks for the detection of errors in a symmetric system of residual classes.



 

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