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    • 1. 发明专利
    • SIMULTANEOUS EQUATIONS ANALYZING COMPUTER BY RELAXATION METHOD
    • JPH06119368A
    • 1994-04-28
    • JP24809991
    • 1991-09-26
    • HITACHI LTDTANAKA MAMORU
    • SHIMIZU NAOHIKOTANAKA MAMORU
    • G06F15/16G06F15/173G06F17/12G06F15/324
    • PURPOSE:To attain high speed and high performance by calculating the different variable of simultaneous equations by connecting plural calculating elements in the form of a ring, and transferring its own calculated value to the neighboring calculating element in this process. CONSTITUTION:The calculating elements PE1 to PEN are connected in the form of the ring by the data line of a single direction so as to communicate with each other. This ring is provided with a gateway processor GE to communicate with a host CPU, and it executes the setting-up of the calculating element, the sending of a command and the judgement of convergence. In a specified step in the calculation of a relaxation method, each calculating element PE1 to PEN analyzes different linear simultaneous equations respectively, and transfers an analyzed result to the neighboring calculating element. In the next step, the calculating element having received this transfer executes the analysis of a succeeding step by using the received analyzed result. Accordingly, high- speed relaxation method calculation can be executed, and besides, each claculating element need only have small traffic, and the analysis calculation of the high performance can be realyzed.
    • 4. 发明专利
    • ASSOCIATIVE MEMORY
    • JPH02165369A
    • 1990-06-26
    • JP32148088
    • 1988-12-20
    • TANAKA MAMORU
    • TANAKA MAMORUOSUGE YOSHIYUKI
    • G06F15/18G06N3/04G06N99/00G11C15/00G11C15/04
    • PURPOSE:To improve the performance of an associative memory and the degree of integration by calculating a Hamming distance between respective information in plural fields in inputted retrieval data and their corresponding self-completion information in each field and associating data having high similarity to the retrieval data in the ascending order of a total Hamming distance obtained by summing the Hamming distances of plural fields. CONSTITUTION:An associative memory operation means 1 stores self-completion information or mutual completion information corresponding to the self-completion information at the time of learning, and at the time of association, executes logical operation between retrieval data and the stored information. An inter-code distance calculating means 2 calculates an inter-code distance between incomplete information having amibiguity corresponding to the inter-code distance of the self-completion information and the self-completion information at the time of association. A decision means 3 activates the storing addresses of the incomplete information and the complete information having a small inter-code distance and a data associating means 4 applies the activated address information to a memory to associate data correspond to the self-associated or mutually associated complete information from the memory. Consequently, the performance can be improved and integration can be made easy.
    • 6. 发明专利
    • ANALOG/DIGITAL CONVERTER
    • JPH01240018A
    • 1989-09-25
    • JP6585888
    • 1988-03-22
    • TANAKA MAMORU
    • CHIGUSA YASUTAMITANAKA MAMORU
    • H03M1/44
    • PURPOSE:To obtain a high speed and high accuracy AD converter by quantizing an analog input, outputting a most significant order bit train, feeding-back the output of the analog integrator of that time to a selecting means, executing quantization to make the output the analog input newly, outputting the next high-order bit train and repeating an operation subsequently in the same way. CONSTITUTION:A bit number to be converted is divided into several blocks in advance. At first, the basic operation of a sigma/delta/AD conversion is executed by necessary number of times to the most significant bit train. Next, the output of an analog integrator 1 at the final time is made to the analog input of a selecting means 2 newly, and the basic operation is executed by the necessary number of times. Subsequently, the conversion of the bit train is executed in order from high-order. When the basic operations of (n) number of times are divided into the two blocks, first, the basic operations of (m) number of times are executed, and next, the basic operations of (n-m) number of times are executed with the output of the analog integrator 1 at a previous stage as the new analog input. In the case of b=1, n=128 and m=64, for instance, the AD converter of 7 bits can be realized in an existing system, and the A/D converter of 12 bits can be realized in this system.
    • 7. 发明专利
    • COMPUTER FOR ANALYSIS OF SIMULTANEOUS EQUATIONS
    • JPS61175774A
    • 1986-08-07
    • JP1744585
    • 1985-01-30
    • TANAKA MAMORU
    • TANAKA MAMORUASAI HIDEKIASAI MITSUO
    • G06F17/16G06F15/78G06F17/12
    • PURPOSE:To attain the LU analysis of a spurse matrix with high efficiency by storing only the non-zero elements into a local memory of each local unit after ignoring the greater part of the zero elements of an original matrix Y to convert the matrix Y into a band matrix and flowing the elements successively in parallel and every line to (q) pieces of processors corresponding to the band width. CONSTITUTION:This processor contains (q) pieces of local units. Then, the local units 1-3 are all connected with each other via a common data bus 10, a control line 11, a priority circuit 12, etc. All non-zero elements only including the fill-in are stored in each local memory unit {Aj} in the row direction of a matrix after ignoring all zero elements. The operations of the processor are controlled in a division mode (a), a multiplication mode (b) and a gauss erasion mode (c) respectively. In the division mode the unit {Aj} receives an access by a counter 13. Only the local bus corresponding to a pivot aii is enabled by an enabling circuit 15. Then the pivot data aii are transferred to all local processors via a common bus.
    • 9. 发明专利
    • DYNAMIC QUANTIZER USING NEURAL NET
    • JPH10105531A
    • 1998-04-24
    • JP25360196
    • 1996-09-25
    • TANAKA MAMORU
    • JINNO KENYATANAKA MAMORU
    • G06G7/60G06F15/18G06N3/00
    • PROBLEM TO BE SOLVED: To surely perform quantization through parallel processing at high speed and to simplify a mounting circuit by decreasing the number of wirings by limiting coupling loads corresponding to respective cells to one kind and feeding only the difference between the value of sum of products and an input parameter amount back to respective cells. SOLUTION: When the amount of sum of products between the outputs and weight of respective cells is more than the input parameter amount, the cell outputting '+1' is operated so as to transit the state of its output to '0' or '1' and when the amount of sum of products is less than the input parameter amount, the cell outputting '0' or '-1' is operated so as to turn its output to '+1'. Namely, a dynamic quantizing means is provided for dynamically repeating the state transition of an output vector from the cell so as to minimize the differential amount. Then, a dynamic quantizer is dynamically operated so that the difference between a representative quantized value obtained by the sum of products between the outputs and weight of respective cells, and the input parameter amount can be converged within the level of hysteresis width and it is enough to feed only this difference back to respective cells.
    • 10. 发明专利
    • ASSOCIATIVE NEUROCOMPUTER
    • JPH0250757A
    • 1990-02-20
    • JP20109488
    • 1988-08-12
    • TANAKA MAMORU
    • TANAKA MAMORU
    • G06F15/18G06N3/04G06N99/00
    • PURPOSE:To attain learning and association processing based upon highly developed parallelism by providing the title computer with a learning processing means for storing associative patterns on a neural net and an association processing means for obtaining association by decoding an associative pattern into a linear graphic code formed at the time of learning processing. CONSTITUTION:For instance, a binary system for expressing the closed circuit of a graph forming each partial neural net 17 is allowed to correspond to a current code and error correcting capacity determined by a minimum inter-code distance is allowed to correspond to the capacity of ambiguity. At the time of learning processing, a sub-tree leaning input pattern is applied to a sub-tree 21 to be a part of the current code, a current code for a linear graph code is formed without generating a bit error and a tree current code to be the other part of the current code is stored in a tree in the partial neural nets 17 as a tree associative pattern. At the time of association processing, syndrome is calculated by addition using '2' as a modulo, a bit error is corrected and the syndrome is decoded to the current code formed at the time of learning to execute association. Thus, learning and association processing can be attained.