JP2008065477A - Data processing device and method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a data processing device and a method obtaining data group for predetermined evaluation and correlation between those data from learning data and its evaluation. <P>SOLUTION: A data processing device 20 includes a reference data storage part 23 for storing a plurality of reference data, a coupling strength storage part 24 for storing the coupling strength for each of the combinations of the reference data, a reference data update part 21 for approximating or isolating at least a part of a plurality of the reference data to/from the given learning data according to evaluation of the learning data, and a coupling strength update part 22 for selecting at least one of the combinations for the reference data, according to similarity to the learning data, and also updating the coupling strength of the combination of the reference data according to an evaluation to the learning data. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a data processing apparatus and method, and more particularly to a data processing apparatus and method for constructing an intended data set using learning data.

  In the following Patent Document 1, learning data composed of a pair of input data and output data is input to a self-organizing relationship (SOR) network together with an evaluation of the learning data, so that a desirable input / output data pair A method for constructing a set of is disclosed.

Non-Patent Document 1 below discloses a technique for constructing a network topological structure by learning and expressing the topological structure of an input data set in a graph, that is, a so-called Topology Representing Network (TRN). It is disclosed.
JP 2000-122991 A “Topology Representing Network”, T.W. Martinez and K.M. Schulten, Neural Networks, Vol. 7, no. 3,507-522, 1994

  However, according to the technique described in Patent Document 1, each element of a desirable set of input / output data pairs is independent, and information regarding the mutual relationship between the input / output data pairs, such as a phase structure, cannot be obtained. For this reason, for example, even when there are a plurality of desirable relationships between input and output data (that is, even if the mapping relationship between input and output data is a multivalent function relationship), there is a problem that it is not possible to grasp this .

  In addition, the method described in Non-Patent Document 1 is based on the assumption that data having no evaluation value is input, and there is a problem in that a data group of a predetermined evaluation cannot be constructed using evaluation on learning data. .

  The present invention has been made in view of the above problems, and an object of the present invention is to provide a data processing apparatus and method capable of acquiring a predetermined evaluation data group and a relationship between the data from learning data and evaluation thereof. is there.

  In order to solve the above problems, a data processing apparatus according to the present invention includes a reference data storage unit that stores a plurality of reference data, a coupling strength storage unit that stores a coupling strength of each combination of the reference data, and the plurality At least one part of the reference data according to the degree of similarity between the learning data and reference data updating means for approaching or separating the given learning data according to the evaluation of the learning data Combination selection means for selecting a combination of the reference data, and connection strength update means for updating the connection strength of the combination of the reference data selected by the combination selection means according to the evaluation of the learning data. It is characterized by.

  Further, the data processing method according to the present invention includes a step of accepting input of learning data and evaluation for the learning data, and at least part of a plurality of reference data is evaluated for the learning data with respect to the input learning data. And a step of selecting at least one combination of the reference data according to the similarity with the inputted learning data, and a coupling strength of the selected combination of the reference data Is updated according to the evaluation with respect to the input learning data.

  In the present invention, at least part of the reference data approaches or separates from the learning data according to the evaluation. Further, at least one combination of reference data is selected according to the similarity to the learning data, and the coupling strength of the selected reference data combination is updated according to the evaluation with respect to the learning data. According to the present invention, the reference data group has a predetermined evaluation, and the relationship between the reference data can be determined based on the coupling strength.

  Further, according to one aspect of the present invention, it further includes a reference data classification unit that classifies the plurality of reference data based on a coupling strength of each combination of the reference data. In this way, it is possible to grasp what classification the data group of the predetermined evaluation has.

  In this aspect, both the learning data and the reference data may include input data and output data. At this time, the data processing device, for each classification by the reference data classification means, the similarity between the input data included in the reference data belonging to the classification and the given input data, and the respective belonging to the classification Data output means for generating and outputting output data based on the output data included in the reference data may be further included. In this way, a plurality of desirable relationships between input data and output data can be obtained, and when input data is given, a plurality of desirable output data can be output according to each relationship.

  In the aspect of the invention, the connection strength update unit updates the connection strength of the combination of the reference data selected by the combination selection unit by an amount corresponding to the similarity between the combination and the learning data. To do. In this way, the bond strength can be updated appropriately.

  In the aspect of the invention, the combination selection unit calculates the similarity based on the interpolation data of each combination of the reference data and the learning data. The interpolation data is, for example, average data of two reference data. In this way, when the learning data and the interpolation data are approximate, the coupling strength can be increased or decreased.

  Embodiments of the present invention will be described below with reference to the drawings.

  In the present embodiment, a large number of learning data are presented together with respective evaluation values for a data space in which a large number of reference data are arranged, and some or all of the reference data approaches the learning data according to the evaluation values. Or updated to be separated.

  That is, as shown in FIG. 1A, for example, a large number of reference data (vectors) ν having x and y components are arranged in the data space, and a positive evaluation value E indicated by a double circle in the figure. When learning data (vector) 10g having (> 0) is presented, each reference data 12 is brought close to the learning data 10g. At this time, the reference data 12 having a shorter distance (for example, the Euclidean distance) from the learning data 10g is made closer to the learning data 10g. Further, the larger the evaluation value E is, the larger the learning data 10g is approached.

  In addition, as shown in FIG. 5B, when learning data 10b having a negative evaluation value E (<0) indicated by a circle with a cross mark in the figure is presented, The reference data 12 is separated from the learning data 10g. Also at this time, the reference data 12 having a shorter distance (e.g., Euclidean distance) from the learning data 10b is more largely separated from the learning data 10b. Further, the smaller the evaluation value E, the larger the distance from the learning data 10b.

  When a large number of learning data 10g and 10b are presented in this way, as shown in FIG. 2 (a), the reference data 12 is gathered at specific locations in the data space, both of which have a positive evaluation. Become. However, in this state, the relationship between the reference data 12 is unknown. Therefore, in this embodiment, the link strength of each combination of the reference data 12, that is, the coupling strength is calculated. In FIG. 5B, a link of the reference data 12 having a positive coupling strength is indicated by a solid line, and the reference data 12 directly or indirectly connected by a link having a positive coupling strength. It is shown that two subsets 13A, 13B are formed.

  In the present embodiment, the learning data and its evaluation value E are used to calculate the link strength of the reference data 12. That is, as shown in FIG. 3A, when the learning data 10g having a positive evaluation value E (> 0) is presented, the similarity (for example, Euclidean distance) between the learning data 10g The link strength of the reference data 12-1 having the largest similarity and the reference data 12-2 having the next highest similarity (hereinafter referred to as “related link”) 14 is calculated. Increase (strengthen). Further, as shown in FIG. 5B, when the learning data 10b having a negative evaluation value E (<0) is presented, a link of the reference data 12 having a positive coupling strength is provided as the related link. Among them, the similarity between the interpolation data 18 of the reference data 12 at both ends (here, average data (data corresponding to the midpoint of the reference data 12 at both ends in the data space)) and the learning data 10b is the highest. The link 16 is selected and its bond strength is reduced (weakened). Thereby, the connection strength of the related link of the learning data 10g having the positive evaluation value E becomes positive, and the connection strength of the related link of the learning data 10b having the negative evaluation value E has a negative value. Become.

  In the present embodiment, after the connection strength is calculated for the update of the reference data 12 and the links of the respective combinations in this way, the desired output data is obtained for each subset 13 of the reference data 12 for any input data. Is generated and output.

  That is, each learning data and reference data includes an input data component and an output data component respectively indicated by characters x and y in FIGS. 1 to 3, and the reference data 12 group belonging to each subset 13 is The relationship between input data and output data having a positive evaluation is shown. In this embodiment, given arbitrary input data, the similarity between the input data and the input data included in the reference data 12 belonging to each subset 13 is calculated, and each subset 13 is assigned to each subset 13 according to the similarity. The output data included in the reference data 12 to which it belongs is weighted and added (centroid calculation) to generate / output desirable output data. As described above, according to the present embodiment, a multivalent function is acquired from learning data and its evaluation value, and a plurality of output data can be generated and output for given input data.

  FIG. 4 is a configuration diagram of the data processing apparatus according to the present embodiment. A data processing device 20 shown in the figure is a device that executes the above-described processing, and is constituted by, for example, a computer and a program executed therein. Here, the data processing apparatus 20 includes a reference data update unit 21, a bond strength update unit 22, a reference data storage unit 23, a bond strength storage unit 24, a data output unit 25, and a reference data classification unit 26 as shown in FIG. The coupling strength updating unit 22 includes a link strengthening unit 22a and a link weakening unit 22b. In this data processing device 20, input / output data pairs are input as learning data, and input / output data pairs showing a preferable input / output relationship as reference data are acquired by learning. In addition, even when there are multiple preferred input / output relationships, that is, when the mapping relationship of input / output data is a multivalent function, it is handled appropriately to generate preferred output data for any input data. Can be output.

  The data processing device 20 can be used for various control devices, for example. In this case, the input data is a control error (operation signal) related to the controlled object, and the output data is an operation amount related to the controlled object. By using this data processing device 20 as a control device, when a redundant manipulator acquires a path for avoiding an obstacle in a self-organized manner in a work space with an obstacle, or when a manipulator's target hand position is given , Determine the angle of each joint, determine the rotational torque of the actuator required for each joint to reach the target posture when the manipulator's target posture (target angle of each joint) is given, etc. It is possible to suitably perform control of a system having a plurality of preferable operation amounts for the operation signal.

First, the reference data storage unit 23 stores reference data ν _i (i = 1 to N). Each reference data ν _i includes input data w _i and output data u _i . The coupling strength storage unit 24 stores coupling strengths C _{i, j} (i = 1 to N; j = 1 to N) of all links of the reference data ν _i .

When the learning data I _l (l = 1 to M) is input, the reference data updating unit 21 converts at least part of the reference data ν _i stored in the reference data storage unit 23 into the learning data I _l . contrast, those which cause or are closely spaced in accordance with the evaluation value E _l. That is, when the evaluation value E _l is positive and indicates a positive evaluation, the learning data I _l is approached. When the evaluation value E _l is negative and the evaluation is negative, the learning data I _l It is made to separate from. Here, each learning data I _l includes input data x _l and output data y _l .

The connection strength updating unit 22 selects at least one related link according to the similarity to the learning data I _l, and selects the connection strength C _{i, j} of the selected related link according to the evaluation value E _l. Update. In particular, the connection strength updating unit 22 determines the connection strength of related links of learning data I ₁ (hereinafter referred to as “I ⁺ ”) having an evaluation value E ₁ (hereinafter referred to as “E ⁺ ”) greater than zero. The relationship between the link reinforcement unit 22a for updating C _{i, j} and the learning I _l (hereinafter referred to as “I ⁻ ”) having a negative evaluation value E _l (hereinafter referred to as “E ⁻ ”). A link weakening unit 22b for updating the link coupling strength C _{i, j} .

In this embodiment, the coupling strength updater 22, a learning data I _l, and interpolation data of the reference data [nu _i according to each link based on the distance (e.g., Euclidean distance) between each link and the learning data I _l The link with the highest similarity is selected as the related link. As described above, the interpolation data is, for example, average data of two reference data related to each link. Further, when the connection strength C _{i, j} of the selected related link is updated, it is proportional to the similarity between the related link and the learning data I ₁ (for example, it may be calculated by a distance decreasing function such as the Euclidean distance). Update as much as you want. Thereby, the more the related link and the learning data I _l are similar _{, the} larger the update amount of the coupling strength C _{i, j} can be.

The reference data classification unit 26 classifies the reference data ν _i based on the coupling strength C _{i, j} of each combination of the reference data ν _i . Specifically, a subset 13 of reference data ν _i connected directly or indirectly by a link having a coupling strength C _{i, j} larger than a predetermined value (here, zero) is created.

The data output unit 25 outputs the similarity between the input data w _i included in the reference data ν _i belonging to each subset 13 and the given input data x and the output included in the reference data ν _i belonging to the subset 13. Based on the data u _i , output data y corresponding to the input data x is generated and output.

5 to 9 are operation flowcharts of the data processing apparatus 20. FIG. 5 shows the entire learning process for constructing the reference data ν _i and the bond strength C _{i, j} in the reference data storage unit 23 and the bond strength storage unit 24. In this process, first, the reference data updating unit 21 The stored contents of the reference data storage unit 23 are updated based on the learning data I ₁ (S100). Next, the link strengthening unit 22a of the connection strength updating unit 22 increases the connection strength C _{i, j} of the related link based on the learning data I ⁺ having a positive evaluation (S200). Thereafter, the link weakening unit 22b of the connection strength updating unit 22 decreases the connection strength C _{i, j} of the related link based on the learning data I ⁻ having a negative evaluation (S300). Finally, the reference data classification unit 26 classifies the reference data ν _i stored in the reference data storage unit 23 into one or a plurality of subsets 13 based on the storage contents of the coupling strength storage unit 24 (S400). Specifically, the reference data ν _i directly or indirectly connected by a link having a coupling strength C _{i, j} greater than 0 is classified into the same subset 13.

FIG. 6 shows the process of S100 of FIG. 5 in detail. In this process, first, all reference data ν _i stored in the reference data storage unit 23 are initialized with random numbers (S101). Next, all learning data I _l and its evaluation value E _l are acquired (S102). Then, for each learning data I _l , the Euclidean distance from each of the reference data ν _i is calculated, and the proximity rank (rank close to the learning data I _l ) is determined (S103). Thereafter, each reference data ν _i is updated in accordance with the proximity rank and evaluation value E _l for each learning data I _l (S104). Specifically, the proximity ranking relative to the training data I _l will increase the amount close to the training data I _l The more above, also increases the amount of close enough evaluation value E _l is large training data I _l. Thereafter, while gradually reducing the amount closer to learning data I _l when rank is low, the process is repeated predetermined number S102 to S104.

Next, FIG. 7 shows in detail the process of S200 of FIG. In this process, first, the link strength C _{i, j} of all the reference data is initialized to zero (S201). Next, acquires learning data I ⁺ and its evaluation value having a positive evaluation value E ⁺ E ^+, according to the following equation to calculate the Euclidean distance d _i for the learning vector I ⁺ of the reference data [nu _i, The order of proximity to the learning vector I ⁺ is determined (S202). Here, i0 is the identification number of the reference data closest to the learning vector I ⁺ (first in proximity ranking), and the identification number of the next closest reference data (second in proximity ranking), iN-1 is the learning vector I. ^This is the identification number of the reference data farthest from ⁺ .

Next, the reference data ν _i0 and ν _i1 having the first and second proximity ranks are selected, and the average data m _i (= (ν _i0 + ν _i1 ) / 2) of the reference data is calculated. This is data corresponding to the midpoint of the reference data ν _i0 and ν _i1 in the data space. Reference data ν _i0 and reference data ν _i1 are reference data related to both ends of the related link of learning data I ⁺ . Then, using the Euclidean distance between the average data _{m i} and learning vector ^{I +,} it calculates the degree of influence _{f l} to link enhanced by the following equation (S203).

Furthermore, the link strength C _{i0, i1} of the link (related link) between the reference data ν _i0 and the reference data ν _i1 is updated according to the following equation (S204).

Then, the processes of S202 to S204 are repeated for all learning data I ⁺ having a positive evaluation value E ⁺ .

Next, FIG. 8 shows in detail the process of S300 of FIG. In this process, first, learning data I ⁻ having a negative evaluation value E ⁻ and its evaluation value E ⁻ are acquired, and the data of the representative point of the link (from the links whose coupling strengths C _{i, j} are greater than zero ( the average data of the reference data relating to both ends of the link) the most training data I ^- close to (associated link) to elect (S301). The identification numbers of the reference data related to both ends of the related link are iA and iB. Then, the data m _i of the representative points of the selected link training data I ^- with a distance between, to calculate the impact f _l to link weakened by the following equation (S302).

Further, the link strength C _{iA, iB} of the link between the reference data ν _iA and the reference data ν _{iB is} updated according to the following equation (S303).

Then, the processes of S301 to S303 are repeated for all learning data I ⁻ having a negative evaluation value E ⁻ .

Finally, FIG. 9 shows the data output processing by the data output unit 25 in detail. In this process, first, arbitrary input data x input from the outside is acquired (S501), and this input data x and w _i that is an input data component of each reference data ν _i stored in the reference data storage unit 23 are stored. similarity Z _i is calculated according to the following equation.

Next, for each subset 13 of the reference data ν _i determined by the reference data classification unit 26, the data output unit 25 uses the similarity Z _i related to the reference data ν _i belonging to the subset 13 according to the following equation. The output data y _k is generated by adding u _i which is the output data component of the reference data ν _i while weighting. Here, C _k indicates the k th subset 13.

According to the data processing device 20 described above, from the learning data I _l and its evaluation value E _l , the positive evaluation reference data ν _i and the relationship between the reference data ν _i are used as the link strength C _{i, j} of each link. Can be acquired. In particular, when input / output data pairs are used as the learning data I _l and the reference data ν _i , the same number of desired output data corresponding to any input data is obtained, even if there are a plurality of input / output relationships. Properly generate and output.

It is a figure which shows a mode that reference data are updated by the learning data which have a positive or negative evaluation. It is a figure which shows the data space after learning. It is a figure which shows a mode that the coupling | bonding strength of the link between reference data is updated by the learning data which have a positive or negative evaluation. It is a block diagram of the data processor which concerns on embodiment of this invention. It is the whole flow of a learning process. It is a reference data update processing flow. It is a processing flow of link reinforcement. It is a processing flow of link weakening. It is a processing flow of data output.

Explanation of symbols

  10g learning data for positive evaluation, 10b learning data for negative evaluation, 12 reference data, 14, 16 related links, 18 links having positive coupling strength, 20 data processing device, 21 reference data updating unit, 22 coupling strength updating Unit, 22a link strengthening unit, 22b link weakening unit, 23 reference data storage unit, 24 bond strength storage unit, 25 data output unit, 26 reference data classification unit.

Claims (6)

Reference data storage means for storing a plurality of reference data;
A bond strength storage means for storing a bond strength of each combination of the reference data;
Reference data updating means for causing at least a part of the plurality of reference data to approach or separate from given learning data according to an evaluation of the learning data;
A combination selection means for selecting at least one combination of the reference data according to the similarity to the learning data;
A connection strength update unit that updates a connection strength of the combination of the reference data selected by the combination selection unit according to the evaluation of the learning data;
A data processing apparatus comprising: The data processing apparatus according to claim 1,
A reference data classification means for classifying the plurality of reference data based on a binding strength of each combination of the reference data;
A data processing apparatus. The data processing apparatus according to claim 2, wherein
The learning data and the reference data are both configured to include input data and output data,
The data processing device includes:
For each classification by the reference data classification means, the similarity between the input data included in each reference data belonging to the classification and given input data, the output data included in each reference data belonging to the classification, Further including data output means for generating and outputting output data based on
A data processing apparatus. The data processing apparatus according to any one of claims 1 to 3,
The combination selection means calculates the similarity based on the interpolation data of each combination of the reference data and the learning data;
A data processing apparatus. The data processing device according to any one of claims 1 to 4,
The bond strength update means updates the bond strength of the combination of the reference data selected by the combination selection means by an amount corresponding to the similarity between the combination and the learning data.
A data processing apparatus. Receiving learning data and an evaluation input for the learning data;
Making at least some of the plurality of reference data approach or separate from the input learning data according to the evaluation of the learning data;
Selecting at least one combination of the reference data according to the similarity with the learning data input;
Updating the bond strength of the selected combination of reference data according to the evaluation of the input learning data;
A data processing method comprising:

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