Method for realizing data classification of simulation-field computer on basis of magnetic field

Abstract

The invention discloses a method for realizing data classification of a simulation-field computer on the basis of a magnetic field. The method comprises the steps of: selecting M1*M2 points on a magnetic conducting medium film to arrange probes vertically; from the arranged probes, selecting k2 probes as magnetic field construction input points, selecting k1 probes as data input points, and selecting one probe as a data output point; generating n2 k2-dimensional constructing data Oj(k2) at random, wherein j is equal to 1, 2L Ln2, and each constructing data can construct one simulation-field computer; respectively selecting n1 data from each type of data of Qi(k1) as input data; determining the density of current input to the magnetic field construction input points by each dimensional value of the constructing data, determining the density of current input to the data input points by each dimensional value in the input data, and acquiring a magnetic-field intensity value or magnetic-induction intensity value from the data output points as an output value; and carrying out verification on the constructing data, and selecting the optimal constructing data to acquire a simulation-field computer and further realize data classification. Due to adoption of the method, the speed of data classification is high and the accuracy is high.

Description

Technical field [0001] The invention relates to a data classification method, in particular to a method for realizing computer data classification of an analog field based on a magnetic field. Background technique [0002] Pattern recognition is a basic human intelligence. In daily life, people often use "pattern recognition" to replace part of human brain work. Pattern recognition refers to the process of processing and analyzing various forms of information that characterize things or phenomena in order to describe, identify, classify and explain things or phenomena. In the process of pattern recognition, a large amount of data is usually classified. The speed and accuracy of data classification directly affect the efficiency and accuracy of pattern recognition. [0003] At present, digital computers based on silicon-based microprocessors are usually used to classify large amounts of data obtained in the pattern recognition process. However, the bottleneck caused by Moore's law...

AI Technical Summary

Problems solved by technology

However, bottlenecks brought about by Moore's law, the theoretical light transmission speed limit of microelectronics technology, the uncertainty principle of quantum mechanics, and the second law of thermodynamics make the process of data classification by digital computers slow and inefficient.

At the same time, in the face of two-dimensional and more than two-dimensional data generated in pattern recognition, digital computers can only serially process each dimension of the data, and their classification efficiency is low.

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