# Neuron network for finding, localizing and correcting errors in residual classes system

FIELD: computer engineering, possible use in modular neuro-computer systems.

SUBSTANCE: in accordance to invention, neuron network contains input layer, neuron nets of finite ring for determining errors syndrome, memory block for storing constants, neuron nets for computing correct result and OR element for determining whether an error is present.

EFFECT: increased error correction speed, decreased amount of equipment, expanded functional capabilities.

1 dwg, 3 tbl

The invention relates to computer technology and can be used in modular neurocomputer systems.

A device for monitoring and correcting errors in a redundant modular code (patent No. 2022472, CL NM 13/00, EN, 1999), which consists of the input of the code Converter, the output of the Converter unit controls comparison block group item OR block of items I.

However, this device has the following disadvantages:

low speed, large hardware cost and low functionality, because the device uses a single redundant module, the error correction is carried out on a large modulus equal to the range of control numbers.

The closest to the technical nature of the claimed device is a device for detecting and correcting errors in the system of residual classes (A.S. No. 714399, G06F 11/08, 1980), contains two modular block convolution, and the first output register connected to the input of the first and second blocks modular convolution and to the first input of the third adder, the outputs of the first and second blocks modular convolution respectively connected with the first inputs of the first and second adders, the second and third outputs of the register are connected respectively with the second inputs of the first and second adders and with the second and third inputs of the third adder, the output of the memory block is connected to the fourth input of the third adder, the output of which is the output device.

A disadvantage of the known device is the complexity, which is explained by the presence of two blocks modular convolution, low speed, which is proportional to the number of modules in the system of residual classes and small functionality.

The aim of the present invention is the simplification of the device, improve performance and increase functionality.

This objective is achieved in that the device comprises a neural network 8, consisting of neural networks, the end rings 4, 5 and 6 and neural networks end rings 27, 28, which form rests for the control modules of the system of residual classes, outputs 9 are connected with the first inputs of the neural networks of the end rings 10, 11 calculate the syndrome of the error, the second inputs of which are connected to the outputs of neurons 23 redundant modules α_{n+1}and α_{n+2}the outputs of the neural networks of the end rings 10, 11, bus 12 and 13 are connected with the inputs of the memory unit 14 for selecting the constants of the error exits 15, which are received on bus 16, 17 and 18 defining the number of the faulty module (channel), and to the input of the OR element 19, the output 20 which detects the presence of errors, in addition, the outputs of the memory block 14 is received on the first choice of the inputs of the neural networks of the end rings 21,
but on the second inputs of which receive the remains of the wrong number α_{1}that α_{2},...,α_{n}. Fixed number of outputs of the neural networks of the end ring 21 is fed to the outputs 22 of the device.

Consider the error correction method.

Given a controlled number A=(α_{1}that α_{2},...,α_{n}that α_{n+1}that α_{n+2}), where

α_{i}=Amodp_{i}∀i=1, 2,..., n+2, R_{1},R_{2},...,R_{n},R_{n+1},R_{n+2}- base JUICE with two redundant bases of p_{n+1}, R_{n+2}.

The principle of detection, localization and error correction based on the functional integration of all three operations into a single operation. This method is based on the determination of digits in redundant bases on the basis of figures on the working grounds and comparing them with the known initial digits in redundant bases. If the calculated figures α'_{n+1}and α'_{n+2}by means of the control bits is equal to the original numbers α_{n+1}and α_{n+2}these discharges, there is no error, if they are not equal, it is necessary to define the syndrome of the error, equal to the difference of these numbers δ_{1}=(α_{n+1}-α'_{n+1})mod p_{n+1}and δ_{2}=(α_{n+2}-α'_{n+2})mod p_{n+2}, whose values are determined by the error constant. Further summarizing the constant errors with the wrong number, which the traveler is chosen so that
implicit error in the number of eliminated. To localize the error, you need to bitwise compare the adjusted number is wrong, and if α_{i}-α_{i}'≠0, is determined by the erroneous discharge. The proposed method allows to detect, isolate, and correct an error in one of the information channels.

Computation α'_{n+1}and α'_{n+2}will carry out based on the method of system expansion bases, which is based on the use of Chinese theorems about residues and generalized positional notation. Let us first consider the extension to one basis, and then generalize the extension on two grounds.

Let set system bases p_{1}, R_{2},..., R_{n}with range R=R_{1}·p_{2}·...·p_{n}orthogonal bases In_{1}In_{2},...,In_{n}, weight of which m_{1}, m_{2},..., m_{n}and are determined from comparisonExtend system grounds, adding base R_{n+1}then the range of the system will become P_{n+1}=p_{n+1}·P, orthogonal bases systemtheir weightandThe problem consists in determining the numbers α_{n+1}the number And on the basis of p_{n+1}.

Then the number in system is IU grounds R_{
1}, R_{2},..., R_{n}, R_{n+1}will be

wherethe range of the extended basis;

orthogonal bases of the extended grounds.

Imagine orthogonal basesin the generalized positional number system, then

wherethe coefficients of the CSO;

i, j=1,2,...,n.

On the basis of (2) let us write the expression (1) in the form

From the expression (3), we can determine the coefficients α_{i}number, then

where α_{i}- deductions numbers And mod p_{i};

orthogonal bases presented in the round.

Figures α_{i}in view of the round is the sum modulo p_{i}all worksand transfer generated during the formation of the α_{i-1}. The transfer is generated as the number of times when the sum of the digits in the CSO overflows modulo p_{i}. This transfer is used to form numbers α_{i+1}. The last transfer generated when receiving the last digit of the number in the round, is discarded. The proposed method is performed in parallel regimens in the performance of this method with the iterative method is obvious,
because it reduces the conversion time 2(n+1) cycles sync up to three cycles.

Figures α_{i},take values from 0 to p_{i-1}andare constants, so the work α_{i}can be placed in ROM or in the weights of connections between neurons. Addresses works α_{i}are deductions α_{i}numbers And modulo p_{i}.

Example 1. Let R_{1}=2, p_{2}=3, p_{3}=5, p_{4}=7, P_{n+1}=2·3·5·7=210, B_{1}=105, B_{2}=70, B_{3}=126, In_{4}=120.

Then on the basis of (2) we define:

Consider the translation of a number of JUICE in the round.

Let And=11=(1, 2, 1, 4). Then in ROM, place value α_{i},:

The conversion of A number of JUICE in the CSO has the form

Adding the numbers for each module taking into account migration will get the number As presented in the OPS as A=[1,2,1,0].

Consider the method of determining the deduction on an extended basis. Let the JUICE consists of grounds R_{1}, R_{2},...,p_{n.}The volume range of this system isAdd to the Isla grounds JUICE new Foundation p_{
n+1}. The volume range of this systemThen any number from range [0, R_{n+1}] generalized positional number system can be represented in the form

If the number And will lie in the original range [0; P], the round dial α_{n+1}=0. If α_{n+1}≠0, then the value of number is outside the dynamic range. Fact α_{n+1}=0 is used to obtain the remainder (deduction) from dividing the number of And on the new basis)_{n+1}.

Let the number And had an idea (α_{1}that α_{2},..., α_{n}) on the grounds of p_{1}p_{2},..., R_{n.}Added a new Foundation p_{n+1}then the number ofthe system bases p_{1}, R_{2},..., R_{n}p_{n+1}where- the remainder from dividing the number of And R_{n+1}, i.e. the required number by the new base.

To determine this number we use the method of transfer number of JUICE in the round, including an unknown numberin the ongoing operation. However, we simultaneously get the numbers SVR α_{1}that α_{2},...,α_{n}and the expression for the numbers α_{n+1}. But as the condition number of A∈[0;P], the figure α_{n+1}=0.

From the obtained ratio and determine the required digit

Example 2. Let set module system R_{1}=2, p_{2}=3, p_{3}=5, then P=2·3·5=30. And may specify the number And=11=(1, 2, 1). Extend system grounds, adding p_{4}=7. And then=11=(1, 2, 1, |A|_{7}in the system bases p_{1}=2, p_{2}=3, p_{3}=5, p_{4}=7.

The set of constants b_{ij}given in (5) and is given by the matrix

The problem-solving process are shown in table 1.

Table 1 | ||||

Deductions numbers And modulo p_{i} | Modules | |||

p_{1}=2 | p_{2}=3 | p_{3}=5 | p_{4}=7 | |

1 | 1 | 1 | 2 | 3 |

2 | 0 | 4 | 2 | 4 |

1 | 0 | 0 | 1 | 4 |

|A|_{7} | 0 | 0 | 0 | 4·|x|_{7} |

The coefficients of the round numbers A | 1 | 2 | 1 | 5+4·|x|_{7} |

After adding the digits modulo p_{i}get And=[1, 2, 1, 5+4|A|_{7}], but as α_{4}=|5+4·|A|_{7}|_{7}but on condition α_{4}7=-5 or. Multiplicative inverse value ofand since the number 5 is negative, take it to addition modulo 7. So, the less number modulo 7 is determined by the expression |A|_{7}=2·(7-5)=4.

Then an enlarged representation of a number is A=11=(1, 2, 1, 4). As a result of the formation of the figures in the JUICE on the new basis p_{n+1}depends only on the information bits, the expansion operation of deductions can be performed on several added reasons.

Example 3. Let set a system reason (s) JUICE p_{1}=2, p_{2}=3, p_{3}=5. Extend system grounds, adding p_{4}=7, p_{5}=11. Then in the expression (5) is added to another column and one row, namely

Let the number is set to A=17=(l,2,2,|A|_{7}, |A|_{11}), it is necessary to find the remains of the bases of p_{4}=7, p_{5}=11.

The solution process is given in table 2.

Table 2 | |||||

Deductions numbers And modulo p_{i} | Modules | ||||

p_{1}=2 | p_{2}=3 | p_{3}=5 | p_{4}=7 | p_{5}=11 | |

1 | 1 | 1 | 2 | 3 | 5 |

2 | 0 | 4 | 2 | 4 | 14 |

3 | 0 | 0 | 2 | 8 | 4 |

0 | 0 | 0 | 4|A|_{7} | - | |

0 | 0 | 0 | - | 7|A|_{11} | |

The coefficients of the round numbers A | α_{1}=1 | α_{2}=2 | α_{3}=2 | α_{4}=2+4|A|_{7} | α_{5}=2+7|A|_{11} |

After adding the digits modulo p_{i}get

A=[1,2,2,2+4|A|_{7},2+7|A|_{11}],

but as α_{4}=2+4|A|_{7}and α_{5}=2+7|A|_{11}and on condition α_{4}=0 and α_{5}=0, then

Multiplicative

Since |A|_{7}=-4, and |A|_{11}=-5 take their supplements, ie, |A|_{7}=7-4=3 and |A|_{11}=11-5=6.

Then an enlarged representation of a number is equal to the information, i.e. A=(1,2,2,3,6)_{11}and this suggests that the number And unmistakable.

The advantages of the proposed method is extended the I residue system is that:

all calculations are performed in parallel channels for individual modules, and each module is identified with a separate channel;

- does not require calculation of a large number of additional variables, you need only constantsand multiplicative values for the advanced bases;

- you may receive an expanded representation of deductions numbers on several additional grounds, which does not affect the performance of the whole operation of the extension.

We apply this method for the detection, location and correction of errors.

Correction of errors in JUICE-based representation of a number in the extended system. As extended basis take excess (control). For example, in processing and storing data in a computer system rests on a redundant basis, on the one hand, is absolutely true, but on the other hand, they are calculated based on the balances of non-redundant (information) the grounds immediately before the control data. If the calculated excess remains equal to the original, errors in the information bases has not occurred, otherwise an error has occurred on informational grounds. Based on this information eliminates the error and determined a faulty module.

Widen the e limitation on the absolute loyalty of excess bases ensures absolute reliability channels redundant modules, you can provide a known structural technologies (for example, using conventional error correcting codes, or a majority of the schema).

Example 4. The source data are the same as in example 3.

For example, an error occurred on the third information base, then a wrong numberwill be=(1,2,3,3,6).

Excess digits in the fourth and fifth bases, respectively, 3 and 6, and they are absolutely correct.

On the basis of information residues define the syndrome error δ.

Method of detection, correction and localization errors in the JUICE is as follows.

Controlled number α_{1}that α_{2},...,α_{n}that α_{n+1}that α_{n+2}divided into two parts: information, which includes balances on information and belief α_{1}that α_{2},..., α_{n}, and a control part, which includes balances excess (control) grounds α_{n+1}that α_{n+2}.

Further information on the remaining channels defined balances excess grounds. Use table 2, then the third line would be not[0, 0, 2, 8, 4], and[0, 0, 3, 12, 6].

When the operation is performed according to table 2 will receive

Here

Using the found remains defined the syndrome of the error:

The values δ_{1}that δ_{2}formed constants of errors in such a way that, when added together with the information bits of a controlled number And occurred error number is eliminated. Note that if the controlled number will not contain errors, then the value of δ_{1}and δ_{2}zero.

To localize the error, you need to compare the digits of the original number with a fixed number and the discharge, where the comparison is carried out, is determined by the erroneous discharge.

Define constants errors for this example, and bring them in table 3.

Table 3 Constant errors for the JUICE with the bases of p _{1}=2, p_{2}=3, p_{3}=5, p_{4}=7, p_{5}=11 | |||||

The error on the basis of p_{1} | δ_{1}that δ_{2} | The error on the basis of p_{2} | δ_{1}that δ_{2} | The error on the basis of p_{3} | δ_{1}that δ_{2} |

0,0,0 | 0,0 | 0,0,0 | 0,0 | 0,0,0 | 0,0 |

1,0,0 | 6,7 1,4 | 0,1,0 | 1,2 3,10 | 0,0,1 | 4,9 6,6 |

0,0,2 | 3,4 5,1 | ||||

0,2,0 | 4,1 6,9 | 0,0,3 | 4,7 2,10 | ||

0,0,4 | 1,5 3,2 |

According to δ_{1}=1, δ_{2}=5 table 2 determines the magnitude of the error (0,0,4), which is controlled by the number of

Largest errors (0,0,4) is localized erroneous discharge, the discharge on modp_{3}. The use of the syndrome of the error allows all procedures for the detection, localization and correction of errors be combined into a single procedure that can reduce the time error correction and improve efficiency.

The drawing shows a neural network for detecting, locating and correcting errors in the system of residual classes. A neural network consists of input layer neurons 2, 23, the input layer 24, intended for storage of the remaining number of working and reference grounds during the time of error detection, the input of which is connected to the inputs 1(α_{1}that α_{2},..., α_{n}and inputs 25(α_{n+1}that α_{n+2}) neural network; neural the network 8,
designed to calculate the residue numbers for control reasons, the inputs of which are connected to the outputs of neurons 2 holding rests on the working grounds; neural networks end rings 10, 11, designed for calculating syndromes of the error on the test grounds, the first inputs of which are connected respectively to the outputs of the neural network calculation rests on the test grounds, and the latter respectively to the outputs of neurons 23 storing the remains in control reasons, the inputs of which are connected to the bus 25; a memory unit 14 for storing constants, an input connected to the outputs of the neural networks of the end rings 10, 11; neural networks end rings 21, designed to obtain the corrected number by summing the wrong number with constant error, the first inputs of which are connected respectively to the outputs of the memory block, bus 15, which are output tires 16, 17 and 18 forming the non-faulty modules and the input element OR 19, the output 20 which generates a signal "there is an error", and the second - with the outputs of the neurons 2, and the output signals 22 neural networks end rings 21 are the outputs of the neural network bug fixes.

The operation of the neural network for detecting, locating and correcting errors in the system of residual classes is following the way.

The inputs 1, 25 neurons 2 and 23 of the neural network for the detection, location and correction of errors in the system of residual classes served controlled number A=(α_{1}that α_{2},...,α_{n}that α_{n+1}that α_{n+2}). From the outputs of the neurons 2 remains on the working grounds with weights w_{ij}3 is fed to the input of the neural network 8 calculation rests on the control modules. The neural network 8 consists of a set of neural networks end rings 4, 5, 6. The weighting coefficients w_{ij}3 and w_{jk}7 neurons in the neural networks of the end rings, the role of distributed memory, are defined respectivelyand w_{jk}=1. Neural network end rings 4, 5 and 6 implement the computational model presented in table 2.

The output signals of the NJC 5 and 6 of the last line is fed to the input of the NJC 27 and 26, with weights w_{kl}29 equalwhere l is the number of extensible modules and takes the value 1,2,....

Output signals 9 of the NJC 27 and 28 will be negative values: -α'_{n+1}and -α'_{n+2}that come respectively to the inputs 9 NJC 10 and 11, and the second input values arrive α_{n+1}and α_{n+2}on tires, respectively 30 and 31. The NJC 10 and 11 implement a computational model:

δ_{1}=α_{
n+1}-(-α'_{n+1});

δ_{2}=α_{n+2}-(-α'_{n+2}).

The output values of the NJC 10 and 11 of the tire 12 and 13 corresponding to the syndromes of the error, proceed to the inputs of the memory block 14 and pick out the appropriate constant according to table 3. These constants from the output of the memory block 14 to 15 tires come on the appropriate bus 16, 17 and 18, which show the number of the faulty module, and also to the input of the OR element 19, the output 20 which indicates the presence of errors. The inputs of the NJC 21 receives the signal from the output of the memory block, and the second input receives respectively the output signals of the working channels, neuronal 3, the output bus 26, where it is summed with the error constants, chosen so that when the addition is controlled by the number And occurred error number is eliminated. The output of the NJC 21 output bus 22 generates a fixed number.

Neural network for detecting, locating and correcting errors in the system of residual classes (SOC)containing an input layer of neurons, the input of which is fed a controlled number A=(α_{1}that α_{2}, ..., α_{n}that α_{n+1}that α_{n+2}), where α_{i}=Amod p_{i}i=1, 2, ..., n+2, R_{1}, .. R_{n}- working grounds JUICE, R_{n+1}, R_{n+2}control base JUICE, memory block, item, OR, n the output of neural networks finite number of the CA,
characterized in that it outputs of the neurons of the input layer on the working grounds branched respectively to the first inputs of the output of neural networks finite rings designed for a fixed number by summing the wrong number with constant error, and to the inputs of the neural network designed to calculate residues α'_{n+1}and α'_{n+2}for control reasons, the outputs of which are connected to the inputs of the neural network forming negative balances control reasons, the outputs of which are connected respectively to the first inputs of the neural networks of the end ring defining the syndrome error δ_{1}=|α_{n+1}-(-α'_{n+1})|_{p+1}and δ_{2}=|α_{n+2}-(-α'_{n+2})|_{p+2}the outputs of the neurons of the input layer control bases connected respectively to the second inputs of the neural networks of the end ring defining the syndrome of the error, the outputs of which are connected to the address inputs of the memory block that stores the constants defined syndromes of errors δ_{1}and δ_{2}the outputs of the memory block connected to the tires determine the grounds on which the error occurred and to the element OR forming the signal "there is an error", and respectively to the second inputs of the n output neural networks finite rings, vihodiashiy are the outputs of the neural network detection
locating and correcting errors in the JUICE.

**Same patents:**

FIELD: cybernetics, possible use as a cell for neuron networks.

SUBSTANCE: neuron-like element may be used for realization on its basis of neuron network for solving problems of estimation of functioning of complicated open systems, estimation of degree of optimality of various solutions by ensuring possible construction of model of researched system, both hierarchical and recurrent, with consideration of varying original and working condition of its elements and variants of their functioning, during modeling taking into consideration the level of self-sufficiency of neuron-like elements, susceptibility to effect of external signals, type and errors of setting of their parameters and parameters of input signals, and also provision of given precision of self-teaching of neuron network. Device contains input block, block for setting and normalizing weight coefficients, block for computing parameters of input signals, adder, signals share limiter, block for computing input part of condition, block for setting internal state, block for computing internal part of distance, block for counting distance, memory block, analyzer of state change value, block for determining precision of self-teaching of neuron network, block of determined dependencies, switch, output block, control block, random numbers generator.

EFFECT: creation of neuron-like element.

2 cl, 1 dwg

FIELD: computer engineering, in particular, modular neuro-computer means, possible use for finding and correcting errors in modular codes of polynomial residual class system.

SUBSTANCE: in accordance to invention, polynomial residual class system is used, in which as system base minimal polynomials p_{i}(z), i=1,2,...,7, are used, determined in extended Galois fields GF(2^{5}) and neuron network technologies, and also modified zeroing constants determined in current polynomial residual class system are used in parallel.

EFFECT: increased speed of detection and correction of errors in modular codes of polynomial residual class system.

2 dwg, 7 tbl

FIELD: computer engineering, possible use in combination devices, and also devices for storing and transferring information.

SUBSTANCE: device contains original circuit, four groups of AND elements, group of OR elements, encoding device, folding circuit, register, error syndrome circuit, checks circuit, three decoders, corrector.

EFFECT: decreased number of controlling discharges.

1 dwg, 1 app

FIELD: computer engineering, possible use in combination devices, and also in devices for storing and transferring information.

SUBSTANCE: device contains memorizing device, four groups of AND elements, AND element, group of OR elements, seven OR elements, encoding device, register, error syndrome circuit, NOT element, decoder, inversion block, even parity check circuit, corrector.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

FIELD: computer engineering, possible use in combination devices, and also in devices for storing and transferring information.

SUBSTANCE: device contains memorizing device, four groups of AND elements, AND element, group of OR elements, seven OR elements, encoding device, register, error syndrome circuit, NOT element, inversion block, decoder, even parity check circuit, corrector.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

FIELD: computer engineering, possible use in combination devices, and also in devices for storing and transferring information.

SUBSTANCE: device contains original circuit, four groups of AND elements, AND element, group of OR elements, seven OR elements, encoding device, register, error syndrome circuit, NOT element, decoder, corrector.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

SUBSTANCE: device contains memorizing device, four groups of AND elements, AND element, group of OR elements, seven OR elements, encoding device, register, error syndrome circuit, inversion block, decoder, corrector.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

SUBSTANCE: device contains original circuit, four groups of AND elements, AND element, group of OR elements, seven OR elements, encoding device, register, error syndrome circuit, NOT element, decoder, even parity check circuit, corrector.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

SUBSTANCE: device contains memorizing device, four groups of AND elements, AND element, group of OR elements, seven OR elements, encoding device, register, error syndrome circuit, NOT element, decoder, inversion block, even parity check circuit, corrector.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

SUBSTANCE: device contains original circuit, four groups of AND elements, group of OR elements, encoding device, register, error syndrome circuit, checks circuit, three decoders, corrector.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

SUBSTANCE: device contains original circuit, three groups of AND elements, AND element, group of OR elements, OR element, encoding device, register, error syndrome circuit, checks circuit, three decoders.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

FIELD: computer science.

SUBSTANCE: network has end ring neuron network, Hopfield neuron network, demultiplexer and multiplexer.

EFFECT: broader functional capabilities, higher efficiency, higher speed of operation.

1 dwg

FIELD: automatics and computer science, possible use for controlling and correcting errors during relaying of information, and also for performing arithmetical operations by computer.

SUBSTANCE: device has two blocks for calculating error syndrome on basis of control bases, made on two-layer neuron network, register, memory block, output adder, and also due to application of polynomial residuals system, in which as system base, minimal polynomials are used, determined in extended Galois fields GF(2^{ν}) and in terms of neuron network technologies.

EFFECT: decreased dimensions of equipment, higher speed of detection and correction of errors.

3 dwg, 2 tbl

FIELD: engineering of printers and memory devices for printers.

SUBSTANCE: in accordance to suggested method for detecting error in data, received from memory device of replaceable printer component, ensured is first evenness control bit, associated with first data element. First data element and first evenness control bit are stored in memorizing device. Printer includes a set of electro-conductive lines. Memorizing device includes a set of bits. At least one of electro-conductive lines is associated with each bit. First data element and first evenness control bit are read from memorizing device. Electric test of at least one of electro-conductive lines is performed. Error is identified in first data element on basis of first evenness control bit, read from memorizing device, and electric test. Other inventions of group envelop printing system, two variants of realization of replaceable printer component for printing system and method for storing information in replaceable printer component are provided.

EFFECT: creation of memory device with increased reliability, timely detection and correction of errors in replaceable components of printers ensures their continuous operation.

5 cl, 7 dwg

SUBSTANCE: device contains original circuit, three groups of AND elements, AND element, group of OR elements, OR element, encoding device, register, error syndrome circuit, checks circuit, three decoders.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

SUBSTANCE: device contains original circuit, four groups of AND elements, group of OR elements, encoding device, register, error syndrome circuit, checks circuit, three decoders, corrector.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

SUBSTANCE: device contains memorizing device, four groups of AND elements, AND element, group of OR elements, seven OR elements, encoding device, register, error syndrome circuit, NOT element, decoder, inversion block, even parity check circuit, corrector.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

SUBSTANCE: device contains original circuit, four groups of AND elements, AND element, group of OR elements, seven OR elements, encoding device, register, error syndrome circuit, NOT element, decoder, even parity check circuit, corrector.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app

SUBSTANCE: device contains memorizing device, four groups of AND elements, AND element, group of OR elements, seven OR elements, encoding device, register, error syndrome circuit, inversion block, decoder, corrector.

EFFECT: increased trustworthiness of device operation.

1 dwg, 1 app