JPS62133589A - Pattern storing and discriminating device - Google Patents
Pattern storing and discriminating deviceInfo
- Publication number
- JPS62133589A JPS62133589A JP27418185A JP27418185A JPS62133589A JP S62133589 A JPS62133589 A JP S62133589A JP 27418185 A JP27418185 A JP 27418185A JP 27418185 A JP27418185 A JP 27418185A JP S62133589 A JPS62133589 A JP S62133589A
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- information processing
- cell
- coupling
- elements
- maximum value
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- 230000010365 information processing Effects 0.000 claims abstract description 24
- 230000002401 inhibitory effect Effects 0.000 claims description 12
- 230000002964 excitative effect Effects 0.000 claims description 3
- 210000004027 cell Anatomy 0.000 abstract description 70
- 230000008878 coupling Effects 0.000 abstract description 12
- 238000010168 coupling process Methods 0.000 abstract description 12
- 238000005859 coupling reaction Methods 0.000 abstract description 12
- 230000003014 reinforcing effect Effects 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 11
- 238000000034 method Methods 0.000 description 7
- 230000001537 neural effect Effects 0.000 description 7
- 238000005728 strengthening Methods 0.000 description 7
- 238000007796 conventional method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000000926 separation method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 239000002581 neurotoxin Substances 0.000 description 2
- 231100000618 neurotoxin Toxicity 0.000 description 2
- 238000003909 pattern recognition Methods 0.000 description 2
- 238000010187 selection method Methods 0.000 description 2
- 230000001235 sensitizing effect Effects 0.000 description 2
- 241000288673 Chiroptera Species 0.000 description 1
- 241000750004 Nestor meridionalis Species 0.000 description 1
- 101710138657 Neurotoxin Proteins 0.000 description 1
- 241000270666 Testudines Species 0.000 description 1
- 235000013527 bean curd Nutrition 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 210000004720 cerebrum Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000001338 self-assembly Methods 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
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Abstract
Description
【発明の詳細な説明】
(産業上の利用分野〕
本発明は、識別すべき種々の形状パターンを記憶し、新
たな人カバターンを記憶したパターンに識別することか
要求される全ての製品のパターン記憶・識別に適用され
るパターン記憶識別装置に関する。DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention is capable of storing various shape patterns to be identified and identifying new human cover patterns with the memorized patterns. The present invention relates to a pattern storage and identification device applied to storage and identification.
従来、この種の装置では、パターン認識機能の高度化か
要求される。このパターン認識機能の高度化をl」的と
したものとしては、特開昭51−352474号公報「
自己組織形成法」及び特開昭58−221474号公報
「自己組織化記憶装置」等か挙げられる。特開昭51−
352474号公報では、各情報処理層からの出力を順
方向に処理することのみによって最終出力を得るように
!:4成されている。さらに、各段の情報処理層にて処
理すべき情報信号は人力層から順次に奥の層に向かって
順ツノ向のみl造れていく構j戊となっている。Conventionally, devices of this type require sophisticated pattern recognition functions. As for the sophistication of this pattern recognition function, Japanese Patent Application Laid-Open No. 51-352474 “
Examples include ``Self-Organization Formation Method'' and ``Self-Organization Memory Device'' published in Japanese Unexamined Patent Publication No. 58-221474. Japanese Patent Application Publication No. 1973-
In Publication No. 352474, the final output is obtained only by processing the output from each information processing layer in the forward direction! :4 has been completed. Furthermore, the information signals to be processed in each information processing layer are constructed in a sequential manner from the human-powered layer to the deeper layers.
また、特開昭58−221474号公報では、帰還型抑
制性結合を設けることにより、[11互に重重の多い類
似パターンに対しても、良好なパターン分離機能を、短
明間の自己組織化によって保持することか+iJ能とな
っている。In addition, in Japanese Patent Application Laid-Open No. 58-221474, by providing a feedback-type suppressive coupling, [11] a good pattern separation function is achieved even for similar patterns that have a lot of overlap with each other. It is possible to hold it by +iJ function.
しかしながら、この種の従来例にあっては次のような問
題かあった。即ち、特開昭 51−352474号公報
では、新たに人力したパターンか、過去に記憶済みのパ
ターンとの市1.(が多く類似したものである場合には
、それら新旧双方のパターンを同一のものと見なしてし
まうと云う問題かあった。また、特開昭58−2214
74号公報では、帰還型抑制性結合を設ける必要かある
ことから、回路か1(雑化すると云う問題があった。However, this type of conventional example has the following problems. That is, in Japanese Patent Application Laid-Open No. 51-352474, a pattern 1. (If there were many similar patterns, there was a problem that both the new and old patterns would be considered the same.
In Japanese Patent No. 74, since it is necessary to provide a feedback suppressive coupling, there is a problem that the circuit becomes complex.
本発明は上記事情を考慮してなされたもので、その(−
1的とするところは、パターン分離機能の向−Lをはか
ることかでき、且つ帰還型抑制性結合等を設ける必要な
く構成の簡略化をはかり得るl<ターン記憶識別装置を
提供することにある。The present invention has been made in consideration of the above circumstances, and the (-
The first objective is to provide a l<turn memory identification device which can measure the direction of the pattern separation function, and which can simplify the configuration without the need to provide a feedback-type suppressive coupling or the like. .
本発明の利子は、自己組織形成法の改良により、パター
ン分離裁能を向上させることにある。An interest of the present invention is to improve pattern separation capabilities through improved self-assembly methods.
即ち本発明は、識別すべき種々の形状パターンを1尼↑
意し、新たな人カバターンを記憶したパターンに識別す
ることか要求される全ての製品のノくターン記t8・識
別に適用されるパターン記憶識別装置において、1.u
Vi、個ずつの興奮性及び抑制性の非線形画素子を混
/1;させて2次元的に配置した情報処理層を曵故個順
次に配列し、前記情報処理層の打1互間に順方向の回層
性結合及び抑制性結合を設けることにより、順次の前記
情報処理層相互間の結合を入力情報及び途中の前記情報
処理層での処理結果に対応させて自己組織化するように
したものである。That is, the present invention allows various shape patterns to be identified to be
In a pattern storage identification device which is applied to the identification of all products requiring identification of a new human cover pattern to a stored pattern, 1. u
Vi, an information processing layer in which excitatory and inhibitory nonlinear pixel elements are mixed and arranged in a two-dimensional manner is sequentially arranged, and the information processing layer is sequentially arranged between each stroke of the information processing layer. By providing directional connections and inhibitory connections, the connections between the sequential information processing layers are self-organized in correspondence with input information and processing results in the intermediate information processing layers. It is something.
上記(13成の本発明では、いくつかのパターンを自己
組織化記憶装置に記憶するに当たり、ある)々ターンの
学習過稈においである段における強化細胞面の曲番号群
か異なる全てのパターンに対して同一、或いは一方か他
方に包含される場合には、残りの非強化細胞面のうちで
、出力が最大値の細胞を持つ細胞面をそのパターンの強
化細胞面と指定する。これにより、他とは違ったfa、
bl フィルターを強化していく。なお、fa、b1強
化の佳ツノは従来法と同様である。In the above-mentioned (13th invention, when storing several patterns in the self-organizing storage device), in the overlearning of each turn, all the different patterns are However, if they are the same or included in one or the other, among the remaining non-enhanced cell faces, the cell face with the cell with the maximum output is designated as the enhanced cell face of that pattern. This makes fa different from others,
We will strengthen the BL filter. Note that the advantages of strengthening fa and b1 are the same as in the conventional method.
〔実施例] 以下、本発明の詳細を実施例を参照して説明する。〔Example] Hereinafter, details of the present invention will be explained with reference to Examples.
一般に、人間の大脳を模した神経回路網における神鋒t
lll胞モデルとしての単位の情報処理素子、即ち神経
素子としては1.入力アナログ信号を非線形処理してO
或いは正のアナログ出力信号を形成するアナログ素子や
、0或いは1の2値出力信号を形成するアナログ閾素子
等を用いる。かかる情報処理素子をパターン情報処理用
として2次元的に配列した情報処理層を多段に縦続配置
して各層間における素子相互間を結合するに当たっては
、個々の神経素子に対して、必ずしも他層の全ての神経
毒J−とフ111合する可能性を与えるわけではなく、
限られた範囲の微小領域内にある神経素子との間におい
てのみ結合の可能性を与えるようにする。In general, Shenfeng t in a neural network that imitates the human cerebrum.
As a unit information processing element, that is, a neural element, as a cell model, 1. Non-linear processing of input analog signal
Alternatively, an analog element that generates a positive analog output signal, an analog threshold element that generates a binary output signal of 0 or 1, or the like is used. When cascading information processing layers in which such information processing elements are two-dimensionally arranged for pattern information processing in multiple stages and connecting the elements in each layer, it is necessary to connect each neural element to another layer. It does not give the possibility of combining with all neurotoxins,
The possibility of connection is provided only between neural elements within a limited range of minute regions.
この領域をその細胞素子の結合可能領域と呼び、しかも
ぶ1−合iiJ能領域内においても中心部と周縁部とに
おいては結合の強度に市みを付けるようにする。This region is called the bondable region of the cell element, and the strength of bonding is differentiated between the center and the periphery even within the bondable region.
1−述のような情91j処理層川L1間における神経素
子の1111合の強度を人力情報のパターンに応じて変
化さけ、所謂れ’i合強度の強化により神経回路網の自
己組織化を行う。ここで、結合強度の強化とは、素子間
に結合がなく結合強度が0の場合には新たに結合を形成
することを意味し、また素子間の結合が既になされてい
る場合にはその結合を更に強めることを意味する。相互
間の結合を行う神経素子としては、その結合により他の
素子に出力の増大と云う興m性効果を与える興m性素子
と、その結合により他の素子に出力の減少と云う抑制効
果を与える抑制性素子との2種類を設定する。1-Avoid changing the strength of the 1111 combination of neural elements between the information 91j processing layer river L1 as described above according to the pattern of human input information, and self-organize the neural network by strengthening the so-called 'i combination strength. . Here, strengthening the bond strength means forming a new bond when there is no bond between elements and the bond strength is 0, and forming a new bond when there is already a bond between elements. It means further strengthening. Neural elements that make connections between each other include sensitizing elements, which have an agonizing effect on other elements by increasing their output, and sensitizing elements, which have an inhibitory effect on other elements by decreasing their output. Two types of suppressive elements are set.
神経素r間の結合の強化を行うに当たって、各層につい
て、前述した結合i−+J能領域内において最大の出力
を形成した神経素子、了、シ<は最良の反応を示した、
即ち人力に最も良く適合した出力を形成した神経毒rの
入力端結合のみを、人力かある場合に限って強化すると
云う規1川に従うものとする。In strengthening the connections between the neural elements r, for each layer, the neural elements that formed the maximum output within the connection i-+J function region, ryo, shi, showed the best response.
In other words, it follows the rule 1 that only the input terminal connection of the neurotoxin r that has formed the output that is most suitable for human power is strengthened only when human power is present.
第1図に情報処理素子をパターン情報処理用として2次
元的に配列した情報処理層を多段に縦続配置したパター
ン記憶識別装置の例を示す。FIG. 1 shows an example of a pattern storage identification device in which information processing layers in which information processing elements are two-dimensionally arranged for pattern information processing are arranged in cascade in multiple stages.
Uoは人力層であり、nx行、ny列に配置したU素子
をU。(れ)として表わす。Ull〜U1.には第1段
の細胞面であり、このU+、z(71)素r−との間に
結合を行いfjJる前段U。層内の結合r+J能6r1
域内に配置4シたU素子をUo(@+u+)と表わす。Uo is the human power layer, and U elements arranged in nx rows and ny columns are U. Expressed as (re). Ull~U1. is the cell surface of the first stage, and a bond is made between this U+, z(71) element r-, and fjJ is the previous stage U. Intralayer bond r + J function 6r1
Four U elements arranged within the area are expressed as Uo(@+u+).
ここで1.1?−1−Klであり、れは九−(n)(、
ny)となる2次元座標を示し、1はnからの2次元座
標のずれを示す。1.1 here? -1-Kl, which is 9-(n)(,
ny), and 1 indicates the deviation of the two-dimensional coordinate from n.
さて、第1図に示したように、各情報処理層、例えば第
2段のl細胞面内の各U素子 U21.(n)は、その
前段のU、の一群のU素子、即ちIU 51(”11
)、U r (れ+u)、”’、U、(れ+t+)f
からそれぞれ可鹿の興亀性結合
fa2 (u)、a、’、(a)、=−、a、、’、
(a)Il、L
を受けているが、図に示すように第1段のV素子V+(
九)も同一のU素子
(U 、1(FLIvJ)、TJ 32(t’L+u>
、−+ 、 U I、kl(M”lll )1から固定
の抑制性♀、−合C+ (u)を受けており、その出
力はIII変の抑制性結合b2により−L述のU 2L
(7t)に加えられている。これらの可変結合a及び
bは前述した結合強化の規則に従って強化されるのであ
る。Now, as shown in FIG. 1, each information processing layer, for example, each U element U21. (n) is a group of U elements of the preceding stage U, that is, IU 51 ("11
), U r (re+u), "', U, (re+t+)f
, respectively, from Kaka's Kokame connection fa2 (u), a,', (a), =-, a,,',
(a) As shown in the figure, the first stage V element V+(
9) The same U element (U, 1 (FLIvJ), TJ 32 (t'L+u>
, −+ , U I, kl(M”ll)1 receives a fixed inhibitory ♀, − combination C+ (u), and its output is U 2L in −L by the inhibitory connection b2 of III change.
(7t) has been added. These variable bonds a and b are strengthened according to the bond strengthening rules described above.
ここで、フ111合強化の規則を第1図を例として具体
的に示すと、次のようになる。Here, the rules for strengthening F111 are concretely shown using FIG. 1 as an example, as follows.
第2段の座標 、の近傍にあるU素子全体の出力は
I U 24 (n、 +uA )、U
2.z(ftl + a )、−。The output of the entire U element near the second stage coordinates is I U 24 (n, +uA ), U
2. z(ftl + a), -.
U 2.に2 (”++ u ) )
で表わされるか、これらの素rの最大値は例えば*(F
LI)でとるとする。但し、
ネ
l こ (1,2,−、に2 ) 、 n
*(’Ih、 + uである。U2. For example, the maximum value of these prime r is expressed as *(F
LI). However, Nel ko (1, 2, -, ni 2), n
*('Ih, + u.
U2を覆いつくす近傍系の1つの近傍について最大値の
選び刀を示したわけであるが、残りの近傍に対しても同
様の操作を行い、最大値を選んでゆく。ところで、1つ
の細胞面には1つの最大値した選べないという制限のた
め、1つの細胞面の異なる近傍で最大値が2個以上選ば
れた場合には、その中での最大値を真の最大値と見なす
。従って、各細胞面には高さ1個の最大値が選ばれたこ
とになり、1[つその最大値は近傍系のある近傍におい
てち最大1直である。このようにして選択された細胞而
を強化細胞面と云う。・
第2図で前述の最大値選択法について説明する。Although we have shown the method for selecting the maximum value for one neighborhood in the neighborhood system that completely covers U2, the same operation is performed for the remaining neighborhoods to select the maximum value. By the way, due to the restriction that one maximum value cannot be selected for one cell surface, if two or more maximum values are selected in different neighborhoods of one cell surface, the maximum value among them is used as the true value. Considered the maximum value. Therefore, one maximum value of height is selected for each cell plane, and the maximum value of 1 is at most 1 in a certain neighborhood of the neighborhood system. The cells selected in this way are called reinforced cell surfaces. - The aforementioned maximum value selection method will be explained with reference to FIG.
第2図で第2段が6個の細胞面からなり、各細胞面が4
個の近f’l (1,2,3,4)で覆われるとする。In Figure 2, the second stage consists of 6 cell surfaces, and each cell surface has 4 cell surfaces.
Suppose that it is covered with near f'l (1, 2, 3, 4).
その時、近傍1の最大値はU2.L (FLI )で、
近傍2の最大値はU 2,1 (n2)で、近f573
の最大値はU2,3(机3)で、近傍4の最大値はU2
j(れ4)でとったとする。U2,3は2つの最大値を
異なる近傍でとるため、その中の最大値を選ぶ。従って
、強化細胞面は1.3.5である。従って、強化される
フィルターa及びbは
ia2.a2.a2.−=、a2 、b211・Llj
l・L 鴇!
Ia2.a2.a2.−.a2 、b21;ジ m、
l+、i Kl、亀 1(a2.a2.a
2.−、a2 、b211、E r、i l、
i kl、i Lであり、強化される電
は、例えば
Δa、ご (III) ccc、 (u) ・U3
1(F’++u+)Δa2 (+u)ocCl (a)
・U’g(t’h+1lj)7.1
Δb2 y: Vl (ガ、)のよう
に決定されていた。Then, the maximum value of neighborhood 1 is U2. In L (FLI),
The maximum value of neighborhood 2 is U 2,1 (n2), and the neighborhood f573
The maximum value of is U2,3 (desk 3), and the maximum value of neighborhood 4 is U2
Suppose that it was taken at j (re 4). Since U2 and U3 have two maximum values in different neighborhoods, the maximum value among them is selected. Therefore, the enhanced cell surface is 1.3.5. Therefore, filters a and b to be strengthened are ia2. a2. a2. -=, a2, b211・Llj
L・L Tow! Ia2. a2. a2. −. a2, b21; di m,
l+, i Kl, turtle 1 (a2.a2.a
2. -, a2, b211, E r, i l,
i kl, i L, and the electricity to be strengthened is, for example, Δa, (III) ccc, (u) ・U3
1(F'++u+)Δa2 (+u)ocCl (a)
・U'g(t'h+1lj)7.1 Δb2 y: Vl It was determined as follows.
次に、第1図のパターン記憶識別装置を元に、本発明に
係わる強化細胞面の選定手法について説明する。Next, a method for selecting reinforced cell surfaces according to the present invention will be explained based on the pattern storage identification device shown in FIG.
いま、00層(こ3つのバタ一二、rAJ、rBJ。Now, the 00th layer (these three bats, rAJ, rBJ.
「C」をサイクリックに呈示しながらa、bを強化して
いき、冷奴Hの呈示後にrAJ、rBJのパターンに対
する強化細胞面が同一、或いは−ノjが他Jjを含む格
好になったとする。例えば、第2段において
rCJの強化細胞面か 1,2.5
rAJの強化細胞面が 2.3.6
rBJの強化Ill胞而か面 2,3
であったとする。rBJの強化細胞面には「C」の強化
細胞面になまれない而3かあるか、rAJの強化細胞面
には完全に含まれている。従って、rBJの強化細胞面
を1.2,3,5.6以外から選定し、その際、残りの
細胞面群での最大値を有する細胞而を指定するようにす
る。このように、サイクリックにパターンを呈示する1
(7に呈示する度に、その直前の1サイクル分の各パタ
ーンの強化細胞面lIを各段毎に比較することにより、
上述したように必要に応じて新しい強化細胞面を選定し
ていく。Suppose that a and b are reinforced while cyclically presenting "C", and after the presentation of cold tofu H, the reinforced cell planes for rAJ and rBJ patterns are the same, or -noj becomes a shape that includes other Jj. . For example, suppose that in the second stage, the reinforced cell surface of rCJ is 1,2.5, the reinforced cell surface of rAJ is 2.3.6, and the reinforced cell surface of rBJ is 2,3. The reinforced cell surface of rBJ has three elements that are not included in the reinforced cell surface of "C", but it is completely included in the reinforced cell surface of rAJ. Therefore, the rBJ enhanced cell plane is selected from those other than 1.2, 3, and 5.6, and at that time, the cell plane having the maximum value among the remaining cell planes is specified. In this way, the pattern is presented cyclically.
(Each time 7 is presented, by comparing the enhanced cell surface II of each pattern for one cycle immediately before each stage,
As mentioned above, new reinforced cell surfaces are selected as necessary.
以1−の(パを作を行うことによって、違うパターンに
対しては違う強化細胞面が選定され、a、bはそれらに
応して強化され、最終的には、各パターンを異なったも
のとして記憶することになる。具体例として、5つの数
字パターンrlJ、r2J。By creating the following (1), different reinforcing cell planes are selected for different patterns, a and b are strengthened accordingly, and finally each pattern is made into a different one. As a specific example, five number patterns rlJ, r2J.
r3J、r4J、r5JをサイクリックにU、層に呈示
する時、呈>J<回数により強化細胞面がどのように変
化していくかを第3図及び第4図に示す。When r3J, r4J, and r5J are cyclically presented in the U layer, FIGS. 3 and 4 show how the enhanced cell surface changes depending on the number of times of presentation>J<.
ここで、第3図は従来方法であり、第4図は本実施例)
J法である。最終段における細胞面数は24てあり、第
3図より呈示回数15回目では「4」と「5」か同じ細
胞面2で出力していることか判る。これに対し本実施例
方法では、第4図により一2示回数15回1−1てはr
lJ、r2J、r3J。Here, Fig. 3 shows the conventional method, and Fig. 4 shows the present example)
This is the J method. The number of cell faces in the final stage is 24, and it can be seen from FIG. 3 that in the 15th presentation, "4" and "5" are output as the same cell face 2. On the other hand, in the method of this embodiment, as shown in FIG.
lJ, r2J, r3J.
r4J、r5Jがそれぞれ18,21,20゜19.3
の1111胞而で出力していることが判る。r4J and r5J are respectively 18, 21, 20°19.3
It can be seen that the output is 1111.
第5図に従来方法による強化細胞間選択装置t例を示し
、第6図に本実施例による強化細胞面選択装置例を示す
。各段毎の選択装置の機能は同じなので、第2図との関
連で第2段について述べる。FIG. 5 shows an example of an enhanced cell-to-cell selection device according to the conventional method, and FIG. 6 shows an example of an enhanced cell-plane selection device according to this embodiment. Since the function of the selection device in each stage is the same, the second stage will be described in connection with FIG.
ます、従来方式につき第5図を用いて説明する。First, the conventional method will be explained using FIG.
■はU層の各細胞面における各近傍の最大値を険出し、
最大値とそれを検出した座標を■の所定の場所に記憶す
る。■の最大値及び最大値検出場所記憶装置は、細胞面
及び近傍の番地をキーにして、最大値及び最大値検出座
標を記憶していく。全ての値を記憶し終イ)った段階で
、■の第1次強化細胞面選択装置により、各細胞面の同
じ近傍に関する最大値を検出し、最大値、最大値をとる
細胞曲番号と細胞面座標を記憶する。第5図の例では、
近傍か1.2,3.4と4つあるので、4つの(最大値
、最大値をとる細胞曲番号と細胞面座標)を記憶する。■ expresses the maximum value of each neighborhood on each cell surface of the U layer,
Store the maximum value and the coordinates at which it was detected in the specified location (■). The maximum value and maximum value detection location storage device (2) stores the maximum value and maximum value detection coordinates using the cell surface and nearby addresses as keys. When all values have been memorized (a), the primary enhanced cell surface selection device (①) detects the maximum value regarding the same neighborhood of each cell surface, and calculates the maximum value, the cell curve number that takes the maximum value, and Memorize cell plane coordinates. In the example in Figure 5,
Since there are four neighbors, 1.2, 3.4, the four values (maximum value, cell curve number and cell plane coordinates that take the maximum value) are stored.
これらの記憶したデータに対して■の第2次強化細胞曲
選択装置において、同一の細胞曲番号か選択されていな
いか調べ、もしあれば、それらの中で最大値ををする細
胞曲番号及び対応する細胞面内座標のみを選択し、残り
を除去する。For these stored data, check whether the same cell song number has been selected in the secondary reinforced cell song selection device (①), and if so, select the cell song number and cell song number that has the maximum value among them. Select only the corresponding in-cell coordinates and remove the rest.
このようにして、■の出力は(強化細胞曲番号と細胞面
内座標)を出力する。In this way, the output of ■ is (reinforced cell track number and cell plane coordinates).
次に、第6図に示す本実施例による強化細胞面選択装置
を用いた例について説明する。Next, an example using the enhanced cell surface selection device according to this embodiment shown in FIG. 6 will be described.
■〜q)は第5図の機能と同様である。本実施例による
追加機能は■〜■である。■は各細胞面における最大値
と最大(l′i検出座標を記憶する装置である。■は記
憶すべきパターンの総数Nに対して、(N−1)個の要
素を持つトコロテン式記憶領域であり、(ψからの人力
に対して、最も占い要素か捨てられていく。(Φから■
への出力は、その段における(強化細胞曲番号、細胞面
内座標)のデータである。例えば、記憶すべきパターン
の総数Nか3であり、そのうちわけがrAJ、rBJ。(2) to (q) are similar to the functions shown in FIG. Additional functions according to this embodiment are ■ to ■. ■ is a device that stores the maximum value and the maximum (l'i detected coordinates) on each cell surface. ■ is a storage area with (N-1) elements for the total number of patterns to be stored (From Φ■
The output to is the data of (enhanced cell track number, coordinates in the cell plane) at that stage. For example, the total number of patterns to be memorized is N or 3, among which rAJ and rBJ.
「C」とすれば、第1図のU。にサイクリックに3つの
パターンを呈示していく。即ち、rAJ→rBJ −r
cJ −rAJ→rBJ→rcJ→・・・・の時系列て
U。のパターンデータか変化していき、ある時点てUo
に「C」があれば、■のンフトバッファの内容で最も古
いものはrAJに関する(強化細胞曲番号、細胞面内座
標lのデータであり、次に古いものはrBJに関するそ
れである。If it is "C", it is U in Figure 1. Three patterns are presented cyclically. That is, rAJ→rBJ −r
The time series of cJ - rAJ → rBJ → rcJ →... is U. The pattern data changes, and at some point Uo
If there is "C" in , the oldest content of the soft buffer in ■ is related to rAJ (reinforced cell song number, data of cell plane coordinate l, and the next oldest is related to rBJ.
総数Nが3であるから、この2つのデータしか入ってい
ない。そこで、■の説明に入るわけたが、■はC)より
(強化細胞面番号、細胞商内座標)を受取り、(′D
の各領域に記憶されている(強化細胞曲番号、細胞商内
座標)と比較し、
くケース1〉の場合、
■より受取った(強化細胞曲番号)の集合が■の各領域
における (強化細胞面内番号)の集合(全部でN−1
個)のいずれに対しても部分集合でないならば、−
C)より受取った
(強化細胞面番号、細胞而内座樟)を■に出力し、これ
でもってa、bを強化する。Since the total number N is 3, only these two pieces of data are included. Therefore, I would like to explain ■, but ■ receives (reinforced cell surface number, cell quotient coordinates) from C), and ('D
In case 1〉, the set of (reinforced cell song numbers) received from cell plane number) (N-1 in total)
If it is not a subset for any of (individuals), - output (reinforced cell face number, cell inside location) received from C) to ■, and strengthen a and b with this.
〈ケース2〉の場合
1、記ケース1の条件をl品定しないならば、−〇)よ
り受取った(強化細胞曲番号)の集合と(1)の各61
′1域における(強化細胞曲番号)の果合の和集合を求
め、それに含まれない細胞面に対して、最大値をとる細
胞面番号及びこれにス1応する細胞面内座標を■に記憶
しているデータより決定し、このデータと■より受取っ
たデータを和集合した
1強化細胞面番号、細胞而内座標)を■に出力し、これ
でもってa、bを強化する。In Case 2, if the conditions of Case 1 are not evaluated, the set of (reinforced cell song numbers) received from -〇) and each 61 of (1)
Find the union of the results of (reinforced cell number) in the region '1, and for the cell planes that are not included in it, calculate the cell plane number that takes the maximum value and the coordinates within the cell plane corresponding to this in ■. Determine from the stored data, and output the 1-reinforced cell surface number, cell internal coordinates), which is the union of this data and the data received from ■, to ■, and use this to strengthen a and b.
以1.詳述したように本発明によれば、異なるa。 Below 1. According to the invention as detailed, different a.
bを強化(自己組織化)していき、結果として異なるパ
ターンを異なるものとして分離して記憶する能力か従来
Jj式に比べて大幅に高くなる。即ち、パターン分解機
能の向−にをはかることができる。b is strengthened (self-organized), and as a result, the ability to separate and memorize different patterns as different things becomes significantly higher than the conventional JJ method. In other words, it is possible to improve the pattern decomposition function.
また、blJ51型抑制結合等を設ける必要もないので
、特願昭58−221474号公報に比して構成の簡略
化をはかることかできる。Furthermore, since there is no need to provide a blJ51 type suppression coupling, etc., the structure can be simplified compared to Japanese Patent Application No. 58-221474.
第1図は本発明の一実施例に係わるパターン記憶識別装
置4を示す概略構成図、第2図は従来の最大値選択法を
説明するための模式図、第3図は従来法によるパターン
分111F能力例を示す模式図、第4図は本゛丈施例に
よるパターン分離能力例を示す模式図、第5図は従来方
式による強化細胞面選択装置例を示す概略構成図、第6
図は本実施例による強化細胞曲選択例を示す概略(13
成図である。
U3−・人力層、U 1,1− U l、Kl ”’
第1段細胞面、U2,1〜U2,8□・・・第2段細胞
面、U3,1〜U 3.旧・・・第3段細胞面、a・・
・+1■変興蕩性結合、b・・可変抑制性結合、C・・
・固定の抑制性結合、U・・・興亀性素子、■・・・抑
制性素子。FIG. 1 is a schematic configuration diagram showing a pattern storage identification device 4 according to an embodiment of the present invention, FIG. 2 is a schematic diagram for explaining a conventional maximum value selection method, and FIG. 3 is a pattern classification diagram according to a conventional method. FIG. 4 is a schematic diagram showing an example of pattern separation capability according to the present embodiment; FIG.
The figure is an outline (13
It is a complete drawing. U3-・Manpower layer, U 1, 1- U l, Kl "'
1st stage cell surface, U2,1 to U2,8□...2nd stage cell surface, U3,1 to U3. Old... 3rd stage cell surface, a...
・+1■ Variable inhibitory connections, b... Variable inhibitory connections, C...
・Fixed inhibitory bond, U...inhibitory element, ■...inhibitory element.
Claims (1)
せて2次元に配置した情報処理層を複数個順次に配列し
、前記情報処理層の相互間に順方向の興奮性結合及び抑
制性結合を設けることにより、順次の前記情報処理層相
互間の結合を入力情報及び途中の前記情報処理層での処
理結果に対応させて自己組織化したことを特徴とするパ
ターン記憶識別装置。A plurality of information processing layers in which a plurality of excitatory and inhibitory nonlinear threshold elements are mixed and arranged in two dimensions are sequentially arranged, and forward excitatory connections and inhibitory connections are established between the information processing layers. A pattern storage identification device characterized in that by providing connections, the connections between the successive information processing layers are self-organized in correspondence with input information and processing results in the intermediate information processing layers.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP27418185A JPS62133589A (en) | 1985-12-05 | 1985-12-05 | Pattern storing and discriminating device |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP27418185A JPS62133589A (en) | 1985-12-05 | 1985-12-05 | Pattern storing and discriminating device |
Publications (1)
Publication Number | Publication Date |
---|---|
JPS62133589A true JPS62133589A (en) | 1987-06-16 |
Family
ID=17538166
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP27418185A Pending JPS62133589A (en) | 1985-12-05 | 1985-12-05 | Pattern storing and discriminating device |
Country Status (1)
Country | Link |
---|---|
JP (1) | JPS62133589A (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5438629A (en) * | 1992-06-19 | 1995-08-01 | United Parcel Service Of America, Inc. | Method and apparatus for input classification using non-spherical neurons |
US5452399A (en) * | 1992-06-19 | 1995-09-19 | United Parcel Service Of America, Inc. | Method and apparatus for input classification using a neuron-based voting scheme |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPS58221474A (en) * | 1982-06-17 | 1983-12-23 | Nippon Hoso Kyokai <Nhk> | self-organizing storage |
-
1985
- 1985-12-05 JP JP27418185A patent/JPS62133589A/en active Pending
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPS58221474A (en) * | 1982-06-17 | 1983-12-23 | Nippon Hoso Kyokai <Nhk> | self-organizing storage |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5438629A (en) * | 1992-06-19 | 1995-08-01 | United Parcel Service Of America, Inc. | Method and apparatus for input classification using non-spherical neurons |
US5452399A (en) * | 1992-06-19 | 1995-09-19 | United Parcel Service Of America, Inc. | Method and apparatus for input classification using a neuron-based voting scheme |
US5638491A (en) * | 1992-06-19 | 1997-06-10 | United Parcel Service Of America, Inc. | Method and apparatus for hierarchical input classification using a neural network |
US5664067A (en) * | 1992-06-19 | 1997-09-02 | United Parcel Service Of America, Inc. | Method and apparatus for training a neural network |
US5974404A (en) * | 1992-06-19 | 1999-10-26 | United Parcel Service Of America, Inc. | Method and apparatus for input classification using a neural network |
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