Source: http://www.google.com/patents/US20030115165?dq=5631832
Timestamp: 2014-08-30 15:17:21
Document Index: 84909747

Matched Legal Cases: ['art 131', 'art 132', 'art 133', 'art 131', 'art 131', 'art 131', 'art 132', 'art 132', 'art 133', 'arts 132', 'arts 133', 'art 131', 'art 132', 'art 133']

Patent US20030115165 - Memory system for use of modeling psychological functions of the brain - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsA STM network 11 for temporarily storing input pattern vectors is formed in Phases 1 and 2, and then layered LTM networks 2 to L are formed successively by assigning output vectors provided by the STM network 11 as input vectors. In phase 4, a LTM network 1 for intuitive outputs to which input pattern...http://www.google.com/patents/US20030115165?utm_source=gb-gplus-sharePatent US20030115165 - Memory system for use of modeling psychological functions of the brainAdvanced Patent SearchPublication numberUS20030115165 A1Publication typeApplicationApplication numberUS 10/253,642Publication dateJun 19, 2003Filing dateSep 25, 2002Priority dateSep 25, 2001Also published asUS7747549Publication number10253642, 253642, US 2003/0115165 A1, US 2003/115165 A1, US 20030115165 A1, US 20030115165A1, US 2003115165 A1, US 2003115165A1, US-A1-20030115165, US-A1-2003115165, US2003/0115165A1, US2003/115165A1, US20030115165 A1, US20030115165A1, US2003115165 A1, US2003115165A1InventorsTetsuya HoyaOriginal AssigneeTetsuya HoyaExport CitationBiBTeX, EndNote, RefManReferenced by (8), Classifications (4), Legal Events (3) External Links: USPTO, USPTO Assignment, EspacenetMemory system for use of modeling psychological functions of the brainUS 20030115165 A1Abstract A STM network 11 for temporarily storing input pattern vectors is formed in Phases 1 and 2, and then layered LTM networks 2 to L are formed successively by assigning output vectors provided by the STM network 11 as input vectors. In phase 4, a LTM network 1 for intuitive outputs to which input pattern vectors are applied directly is formed by taking the parameters of comparatively highly activated centroids among centroids in the LTM networks 2 to L. In phase 5, the parameters of the comparatively highly activated centroids among the centroids in the LTM networks 2 to L are fed back as the parameters of the centroids in the STM network. In phase 3, the LTM networks 2 to L are reconstructed at a particular time or in a fixed period by giving the centroid vectors of the LTM networks 2 to L again as input pattern vectors to the STM network 11. Images(13) Claims(32)
BACKGROUND OF THE INVENTION [0001] 1. Field of the Invention [0002] The present invention relates to modeling of functions relating to psychological activities of the brain. More particularly, the present invention relates to a memory system using an artificial neural network structure (neural network model) for modeling such psychological functions of the brain as �intuition�, �consciousness or awareness�, �memory-chaining� and �emotion expression�. The present invention is applicable to many fields including those of (1) systems of intelligent robotics; (2) pattern recognition systems such as security systems for fingerprint identification, speech recognition and digit/character voice recognition, and image diagnosis; and (3) general domestic electrical appliances. [0003] 2. Description of the Related Art [0004] Interpretation of notions of the emotional and psychological activities of the actual brain is a really challenging problem. Many schools in various disciplines including biology and philosophy have historically developed arguments about the interpretation of functions of the brain relating to emotional and psychological activities among those of the brain. Recently, research and development activities have been made on the basis of information obtained by the progress of biological studies in addition to the advancement of computer technology to develop what is called �brain-style computers�. [0005] It is said that the elucidation of the notion of �intuition� in terms of artificial neural networks is one of key approaches to the development of brain-style computers (Ref. [1]). [0006] On the other hand, various arguments have been made in recent years in the field of robotics about the concrete modeling of the notion of �consciousness� (Refs. [2] to [5]). An intelligent robot is proposed in Ref. [5] as an example of such modeling. This artificial robot is capable of imitating the behavior of an actual animal by using a model of �consciousness�. A virtual machine modeling the behavior of �consciousness� is proposed in Ref. [2]. [0007] However, concrete methods of modeling the notion of �intuition� have not been established in the conventional neural networks, and any method capable of clearly explaining the modeling of the state of �consciousness or awareness� has not been available. SUMMARY OF THE INVENTION [0008] The present invention has been made under the foregoing circumstances and it is therefore an object of the present invention to provide a memory system adapted to embody the states of �intuition� and �awareness�, which are psychological functions of the brain, by using a comparatively simple artificial neural network structure, a method of forming the artificial neural network structure, and a program product of carrying out the method. [0009] Another object of the present invention is to provide a memory system (�memory-chaining� system) adapted to realize �memory-chaining� and �emotion expression� by using a comparatively simple artificial neural network structure, a memory-chaining program product, and a neuron element for use in the memory chaining system. In this specification, the term �memory-chaining� signifies an act of chaining information or processes held for long-term memory, and, psychologically, corresponds to memory called episodic memory, declarative memory or procedural memory (Ref. [9]). [0010] <Prerequisites for Present Invention>
BRIEF DESCRIPTION OF THE DRAWINGS [0045]FIG. 1 is a block diagram of a memory system in a first embodiment of the present invention; [0046]FIGS. 2A and 2B are diagrams of assistance in explaining a generalized regression neural network (GRNN) as a basic element of a hierarchically arranged generalized regression neural network (HA-GRNN) employed in the present invention; [0047]FIG. 3 is a diagrammatic view of a STM network included in the memory system shown in FIG. 1; [0048]FIG. 4 is a diagrammatic view of a LTM network 1 included in the memory system shown in FIG. 1; [0049]FIG. 5 is a flow chart of assistance in explaining a development process of developing the memory system in the first embodiment of the present invention; [0050]FIG. 6 is a diagram of assistance in explaining a schedule of the development process in an example (simulation experiments) of the first embodiment of the present invention; [0051]FIG. 7 is a block diagram of a memory system (memory-chaining system) in a second embodiment of the present invention; [0052]FIGS. 8A and 8B are diagrammatic views of assistance in explaining a hierarchical LTM network unit (long-term memory network unit) of a long-term memory model unit included in the memory-chaining system shown in FIG. 7; [0053]FIG. 9 is a block diagram of a RBF element (neuron) employed in the hierarchical LTM network unit shown in FIGS. 7, 8A and 8B; [0054]FIG. 10 is a typical view of assistance in explaining a method of realizing �memory-chaining� by using the RBF elements shown in FIG. 9; [0055]FIG. 11 is a flow chart of assistance in explaining a method of realizing �memory-chaining� and �emotion expression� in the memory-chaining system shown in FIGS. 7 to 9; [0056]FIG. 12 is a flow chart of assistance in explaining a method of controlling an emotional value counter included in the memory-chaining system shown in FIGS. 7 to 9; and [0057]FIG. 13 is a block diagram of a computer system to which the first and second embodiments of the present invention are applied. DESCRIPTION OF THE PREFERRED EMBODIMENTS [0058] Preferred embodiments of the present invention will be described with reference to the accompanying drawings. [0059] First Embodiment [0060] A memory system in a first embodiment of the present invention will be described. The memory system in the first embodiment models states of �intuition� and �awareness�, i.e., psychological functions of the brain, by an artificial neural network structure. Hierarchically arranged generalized regression neural networks (HA-GRNNs) are evolved according to input pattern vectors to explain and embody states of �intuition� and �awareness�, i.e., psychological functions of the brain. [0061] <Configuration of GRNN>
[0062] A generalized regression neural network (GRNN) as a basic element of a hierarchically arranged generalized regression neural network (HA-GRNN) employed in the memory system in the first embodiment will be described. [0063]FIG. 2A shows a multilayered generalized regression neural network (ML-GRNN) having Ni input neurons, Nh intermediate neurons, and No output neurons. [0064] In FIG. 2A, the input neurons xi (i=1, 2, . . . , Ni) correspond to the elements of an input vector x=[x1, x2, . . . , xNi]T (T: vector transpose). In the following description, underlined alphabetical characters represent vectors. [0065] An output neuron Ok (k=1, 2, . . . , N0) is expressed by Expressions (1) and (2): o k = 1 δ  ∑ j = 1 N b  w j  , k  h j ,  where: ( 1 ) δ = ∑ k = 1 N o  ∑ j = 1 N b  w j  , k  h j ,  w _ j = [ w j , 1 ,  w j , 2 , �  ,  w j , No ] T ,  h j = f  ( x _ , c _ j , σ j ) = 1 2   σ j 2  exp  ( -  x _ - c _ j  2 2 2   σ j 2 ) , ( 2 ) [0066] where cj is a centroid vector, σj is radius, W j is a weight vector between a j-th RBF and the output neurons, and  ⋯  2 2 [0067] is the squared L2 norm. [0068] As shown in FIG. 2A, the configuration of the ML-GRNN is similar to that of a well-known multilayered perception neural network (MLP-NN) (Ref. [7]), except that RBFs are used in the intermediate layer, and linear functions are used in the output layer. [0069] Differing from the conventional MLP-NNs, the GRNNs do not need the iterative training of weight vectors at all; that is, similarly to other RBF-NNs, the GRNNs are adapted to deal with any input-output mapping simply by assigning input vectors to centroid vectors and making weight vectors between the RBFs and outputs coincide with target vectors. This feature of GRNNs is quite beneficial as compared with the nature of prevalently used conventional MLP-NNs that make back-propagation-based weight adaptation, involve long, iterative training, and are unavoidably subject to a danger of their outputs remaining at local minima, which is a very serious problem when the size of a training input vector set is very large. [0070] The specific feature of GRNNs enables flexible formation of networks according to given tasks and is beneficial to the implementation of practical hardware because training can be achieved by adjusting only the two parameters, namely, cj and σj. [0071] The only disadvantage of GRNNs, as compared with MLP-NNs, is the need for storing all the centroid vectors in a memory space, which often means that GRNNs become exhaustive and, hence, operations in a reference mode (test mode) are complex. Nevertheless, since GRNNs can be regarded as a single element of a memory-based architecture, the aforesaid utility of GRNNs is suggestive of using a neural network as a tool adapted to assist the interpretation of the functions, such as �intuition� and �awareness�, of the actual brain. [0072] Referring to FIGS. 2A and 2B, a target vector t(x) corresponding to an input pattern vector x is an indicator function vector expressed by Expression (3). t _  ( x _ ) = ( δ 1 , δ 2 , �  , δ No ) ,  { 1 if   x _   belongs   to   the   class δ j = corresponding   to   o k , 0 otherwise ( 3 ) [0073] When centroid hj is assigned to x, and w j=t(x) is satisfied by the aforesaid property of GRNNs, the entire network is geometrically equivalent to a network having No subnetworks and a decision unit as shown in FIG. 2B. In the following description, the term �GRNN� is used to denote a neural network shown in FIG. 2B. [0074] In summary, the network construction using an ML-GRNN is simply done in the following. (In the field of neural networks, such construction is referred to as �learning�. However, the meaning is rather limited because a network of a ML-GRNN is grown or shrunk by simply assigning an input vector to a corresponding RBF without repeating the adjustment of weight vectors.) [0075] Network Growing: Relation cj=x is set, σj is fixed, and the term wjkhj is added to Expression (2). A target vector t(x) is thus used as a class �label� indicating the number of a subnetwork to which the centroid (RBF) is to belong, which is equivalent to adding a j-th RBF to a corresponding k-th subnetwork. [0076] Network Shrinking: The term wjkhj is deleted from Expression (2). [0077] Thus,a dynamic pattern classifier is formed (Ref. [8]). The construction of this network gives a basis for constructing a HA-GRNN employed in the memory system in the first embodiment of the present invention. [0078] <Setting of Radii of GRNN>
[0123] Step 1: Count the frequency of activation of the most activated centroid among those in the LTM networks 2 to L to monitor the most activated centroid every time an input pattern vector is given to the memory system 10 during the formation and reconstruction of the LTM network group in Phases 2 and 3. The frequency of activation is incremented when the class ID of the incoming pattern vector matches that of the most activated centroid. [0124] Step 2: List all the counted frequencies of activation of the centroids in the LTM networks 2 to L in a particular time or period q to find N centroids having the largest frequencies of activation. The value of N meets a condition represented by Expression (8). N   << ∑ i  ∑ j  M LTMj , i ( 8 ) [0125] Step 3: Move all the N centroids to the LTM network 1 if the total number of centroids in the LTM network 1 is less than or equal to MLTM1−N (MLTM1 is the maximum number of the centroids in the LTM network 1 and it is assumed that N≦MLTM1). Otherwise, leave the (MLTM1−N) centroids in the LTM network 1 untouched and replace the rest of the centroids in the LTM network 1 with N new centroids. [0126] Step 4: Create a direct path to the input pattern vector for each centroid to be added by Step 3. (The arrow 32 in FIG. 1 indicates this process.) [0127] Note that, unlike the other LTM networks 2 to L, the radii of the centroids in the LTM network 1 must not be varied during evolution because the high activation of each centroid by the radii is expected to continue after the N centroids have been moved to the LTM network 1. [0128] The foregoing four phases give a basis of evolution of the memory system 10; and the interpretation of the notion of the two psychological functions of the brain (i.e., �intuition� and �awareness�) is conducted under the evolution of the memory system 10 as follows. [0129] <Interpretation of �Intuition� and �Awareness�>
[0158] Parameters for constructing the HA-GRNN used by the simulation experiments are tabulated in Table 1. TABLE 1 Network Configuration Parameters for the HA-GRNN Parameter Value Max. num. of centroids in STM MSTM = 30 Total num. of LTM networks (L + 1) = 3 Max. num. Of centroids in LTM 1 MLTM1 = 5 Num. Of subnetworks in LTM 2 & 3 Nc1 = 10 Max. num. of centroids in each MLTM2,i = 4, MLTM3,i = 4 subnetwork in LTM 2 & 3 (i = 1, 2, . . ., 10) [0159] In Table 1, MLTM1, MLTM2i and MLTM3i (i=1, 2, . . . , 10, which correspond to class IDs 1, 2, . . . , 10, respectively) are arbitrarily chosen, and Nc1 must be chosen equal to the number of the classes, i.e., the ten digits. With this setting, the total number of centroids in LTM network from 1 to 3, MLTM,Total, is calculated by using Expression (9). The calculated number is 85. M LTM,Total =M LTM,1 +N C1(M LTM,2 +M LTM,3). (9) [0160] <Setting of STM Network>
[0167] The pattern-recognizing ability of the HA-GRNN was examined. After the completion of the evolution process, the STM network was skipped over and only the LTM networks 1 to L were used to evaluate the generalization performance during the examination. [0168] Table 2 shows a confusion matrix obtained by using the HA-GRNN after the completion of the evolution process. TABLE 2 Confusion Matrix Obtained by the HA-GRNN After the Evolution Generalization Digit 0 1 2 3 4 5 6 7 8 9 Total Performance 0 29 3 2 1 1 29/36 80.6% 1 31 1 2 2 31/36 86.1% 2 31 1 3 1 31/36 86.1% 3 31 3 1 1 31/36 86.1% 4 36 36/36 100.0% 5 3 1 27 2 3 27/36 75.0% 6 32 2 2 32/36 88.9% 7 4 32 32/36 88.9% 8 1 1 34 34/36 94.4% 9 4 10 1 21 21/36 58.3% Total 304/360 84.4% [0169] In this case, any �awareness� states were not formed at t3. Table 3 shows, for comparison, a confusion matrix obtained by using a conventional GRNN having the same number of centroids (85 centroids) in each subnetwork as the HA-GRNN. TABLE 3 Confusion Matrix Obtained by the Conventional GRNN Using k-Means Clustering Method Generalization Digit 0 1 2 3 4 5 6 7 8 9 Total Performance 0 35 1 1 34/36 94.4% 1 17 19 17/36 47.2% 2 28 8 28/36 77.8% 3 3 22 10 1 22/36 61.1% 4 36 36/36 100.0% 5 36 36/36 100.0% 6 1 1 34 34/36 94.4% 7 1 3 3 6 23 23/36 63.9% 8 2 1 1 32 32/36 88.9% 9 1 27 8 8/36 22.2% Total 270/360 75.0% [0170] Each of the centroids was found by the well-known McQueen's k-means clustering method (Ref. [27]). To ensure fair comparison, the centroids in each subnetwork were obtained by applying the k-means clustering method to each subset including 54 samples (of incoming pattern vectors) for each of digits /ZERO/ to /NINE/. [0171] It is known from the comparison of the confusion matrices shown in Tables 2 and 3 that the HA-GRNN is superior in generalization performance to the conventional GRNN substantially throughout all the classes, and the generalization performance of the HA-GRNN with the digits excluding the digit /NINE/ is relatively high, whereas the performance of the conventional GRNN varies from digit to digit as shown in Table 3. This fact indicates that a pattern space spanned by the centroids obtained by the k-means clustering method is biased. [0172] <Generation of Intuitive Outputs>
[0175] It is known from Table 2 that the generalization performance for digits /FIVE/ and /NINE/ are relatively unsatisfactory. �Awareness� states were created intentionally for digits /FIVE/ and /NINE/ to evaluate the effect of having an �awareness� model in the HA-GRNN. Ten centroids among 30 centroids in the STM network were fixed at digits, respectively, after the evolution time t3 according to Conjectures 2 and 3. Since the unsatisfactory generalization performance for the digits /FIVE/ and /NINE/ are perhaps due to the insufficient number of the centroids for those classes, the maximum numbers MLTM2,i and MLTM3,i (i=5 and 10) of centroids in the LTM network 2 and 3 were increased. [0176] Table 4 shows a confusion matrix obtained by the HA-GRNN having an �awareness� state of only a digit /NINE/. TABLE 4 Confusion Matrix Obtained by the HA-GRNN After the Evolution (With a �Awareness� State of Digit 9) Generalization Digit 0 1 2 3 4 5 6 7 8 9 Total Performance 0 30 1 3 2 30/36 83.3% 1 31 2 2 1 31/36 86.1% 2 31 1 3 1 31/36 86.1% 3 31 3 1 1 31/36 86.1% 4 36 36/36 100.0% 5 3 1 28 2 2 28/36 77.8% 6 32 2 2 32/36 88.9% 7 4 32 32/36 88.9% 8 1 1 34 34/36 94.4% 9 2 12 22 22/36 61.1% Total 307/360 85.3% [0177] In this case, 8 centroids in total in the LTM networks 2 and 3, i.e., 4 centroids in each of the LTM networks 2 and 3 (the first 8, not 4, most activated centroids selected after Phase 4 were added to the respective subnetworks 10 of the LTM networks 2 and 3. That is, the total number of the centroids in the LTM networks 1 to 3 was increased up to 93. As obvious from Table 4, the generalization performance of the digit /NINE/ was improved by 61.1% as based on the generalization performance shown in Table 2. It is interesting to note that the generalization performance of digits /ZERO/ and /FIVE/ was improved as the consequence of improvement of that of the digit /NINE/. [0178] Table 5 shows a confusion matrix obtained by the HA-GRNN having �awareness� states of both the digits /FIVE/ and /NINE/. TABLE 5 Confusion Matrix Obtained by the HA-GRNN After Evolution (With �Awareness� States of Digits 5 and 9) Generalization Digit 0 1 2 3 4 5 6 7 8 9 Total Performance 0 30 1 3 2 30/36 83.3% 1 31 2 2 1 31/36 86.1% 2 31 1 3 1 31/36 86.1% 3 32 4 32/36 88.9% 4 36 36/36 100.0% 5 1 1 33 1 33/36 91.7% 6 32 2 2 32/36 88.9% 7 4 32 32/36 88.9% 8 1 1 34 34/36 94.4% 9 3 1 10 22 22/36 61.1% Total 313/360 86.9% [0179] Similarly to the case of the �awareness� state of the digit /NINE/, 16 centroids in total for the two digits were added to each of the subnetworks 6 and 10 of the LTM networks 2 and 3. Thus, the total number of centroids in the LTM networks 1 to 3 was increased to 101. As compared with the case shown in Table 2, the generalization performance for the digit /FIVE/, similarly to that for the digits /ZERO/, /THREE/ and /NINE/, as improved drastically. [0180] It is known from those results that, since the improvement of generalization performance for the digit /NINE/ in both the cases was not expected, the pattern space for the digit /NINE/, as compared with those for other digits, is difficult to cover completely. [0181] Thus, the results of the simulation experiments validated the evolution process in the range of the pattern classification task. More concretely, the effectiveness of generalization performance was examined and the superiority of the HA-GRNN to the conventional GRNN using the k-mean clustering method was proved. [0182] Second Embodiment [0183] A memory system (memory-chaining system) in a second embodiment of the present invention will be described. The memory system (memory-chaining system) in the second embodiment is adapted to model �memory-chaining� and �emotion expression�, which are psychological functions of the brain, by an artificial neural network structure. [0184] The configuration of the memory-chaining system 100 in the second embodiment will be described with reference to FIG. 7. [0185] Referring to FIG. 7, the memory-chaining system 100 in the second embodiment comprises an artificial neural network structure modeling �memory-chaining� and �emotion expression�, i.e., psychological functions of the brain. The memory-chaining system 100 is a layered memory system including a short-term memory (STM) model unit 10 and a long-term memory (LTM) model unit 120. [0186] <STM Model Unit>
[0199] The RBF element 130 included in the RBF map unit 124 of the hierarchical LTM network unit 121 will be described with reference to FIG. 9. [0200] Referring to FIG. 9, the RBF element 130 includes a RBF main part 131, a pointer part 132 and an incrementing/decrementing value holding part 133. [0201] The RBF main part 131 provides an activation intensity corresponding to the similarity between an input vector and a centroid vector according to a radial-basis function (RBF). An activation intensity hij(x) provided by the RBF main part 131 of the i,j-th RBF element 130 (RBFij) when an input vector x is given thereto is expressed by Expression (11). h ij  ( x _ ) = exp  ( -  x _ - c _ ij  2 2 2   σ ij 2 ) , ( 11 ) [0202] where cij is a centroid vector and σij is radius. [0203] The RBF main part 131 has a configuration similar to that of an existing RBF and is used for constructing an artificial neural network structure having general-purpose abilities for realizing dynamic pattern recognition. [0204] The pointer part 132 holds a plurality of pieces of pointer information about other related RBF elements and is used for realizing a memory-chaining� function. In FIG. 9, the addresses of the RBF elements to be subsequently chained are held in pij,1 to pij,max. Tree-mode memory-chaining can be realized by thus holding the plurality of pieces of pointer information by the pointer part 132 (FIG. 10). Thus, procedural memory and declarative memory, which are essential to realization of a thinking mechanism in addition to episodic memory. [0205] The incrementing/decrementing value holding part 133 is a register that holds incrementing/decrementing values of emotional value to be added to the emotional value held by the emotional value counter 113 of the STM model unit 110, and are used for realizing emotion-expressing function. In FIG. 9, incrementing/decrementing values of emotional value eij,1 to eij,4 correspond to emotional values E1 to E4 held by the emotional value counter 113, respectively. The emotional values E1 to E4 held by the emotional value counter 113 correspond to emotions �joy�, �anger�, �grief� and �pleasure�, respectively (Ref. [30]). Emotions are not limited to the foregoing four kinds of emotions. [0206] The operation of the memory-chaining system 100 will be described. [0207] In the memory-chaining system 100 shown in FIGS. 7 to 9, an artificial neural network structure having general-purpose abilities for realizing dynamic pattern recognition can be constructed by evolving the hierarchical LTM network unit 121 on the basis of input vectors. As mentioned in the description of the first embodiment, the RBF map unit 124 including the plurality of RBF maps 124 a is constructed by constructing the layered LTM networks 2 to L according to the evolution process of a HA-GRNN (hierarchically arranged generalized regression neural network). LTM construction and learning of the hierarchical LTM network unit 121 of the LTM model unit 120 are performed. [0208] In the process of the LTM construction and learning, operations for learning of the pointer parts 132 and the incrementing/decrementing value holding parts 133 of the RBF elements 130 included in the RBF map unit 124 are carried out simultaneously for learning to realize �memory-chaining� and �emotion expression�. [0209] The following is an example of a sensor information processing procedure. [0210] (a) A portrait of the late brother is shown to an intelligent robot. (→ Giving sensor information (or feature information extracted from sensor information) [0211] (b) The intelligent robot recognizes that a person in the portrait is the brother. (→ Feature image recognition (1) (Perceptional information processing (1))) [0212] (c) The intelligent robot remembers the brother's voices. (→ Voice recognition (1) (Perceptional information processing (2))) [0213] (d) The intelligent robot remembers the features of the brother's lover from the reminded brother's voices. (→ Feature image recognition (2) (Perceptional information processing (3)) [0214] It may safely be considered that the perceptional information processing steps (c) and (d) among the perceptional information processing steps (b) to (d) were derived from the perceptional information processing step (b) by a �memory-chaining� function. Suppose that the perceptional information processing steps (b) to (d) are executed by the RBF elements RBF11, RBF32, and RBF56. Then, memory-chaining of the perceptional information processing steps (b) to (d) can be expressed by Expression (12): RBF11→RBF32→RBF56, (12), [0215] where RBFij denotes the RBF element on the line i and the column j of the RBF map 124 a of RBF map unit 124. RBF11 denotes the RBF element in a feature image recognition area (visual column) most activated to the brother's feature; RBF32 denotes the RBF element in a voice recognition area (auditory column) most activated to the brother's voice; and RBF56 denotes the RBF element in a feature recognition area (visual column) most activated to the feature of the brother's lover. [0216] <Memory-Chaining Learning>
[0243] The �memory-chaining� and �emotion expression� functions are thus realized. The emotional values E1 to E4 set during the realization of the �emotion expression� in the emotional value counter 113 of the STM model unit 120 significantly concern the actual control of the intelligent robot or the like. Therefore it is often necessary to limit the range of the emotional values E1 to E4 in order that the intelligent robot can be smoothly controlled. [0244] Accordingly, in the memory-chaining system 100 in the second embodiment, the LTM controller 122 of the LTM model unit 110 retrieves the RBF elements 130 that make the emotional values E1 to E4 held by the emotional value counter 113 meet a predetermine condition after the completion of memory-chaining and emotion expression, to keep the emotional values E1 to E4 within a predetermined range. Such a control method is supposed to be applied to cases, namely, (1) a case where actual actions (processes) are performed and (2) a case where imaginary actions (processes) (�thinking�) are supposed. In the case (2), since the emotional value counter 113 performs the addition merely temporarily, new imaginary emotional values E1′ to E4′ are prepared, and initial values Ei′ are set equal to Ei (i=1 to 4). [0245] A control procedure for controlling the emotional value counter 113 will be described with reference to FIG. 12. Retrieval cycle count cnt is set to �0� in step 1401, and then a query is made in step 1402 to see if the emotional values Ei (Ei′) (i=1 to 4) meet a condition expressed by Expression (16). E min <E i(E i′)≦E max (16) [0246] If the emotional values Ei (Ei′) do not satisfy Expression (16), the control procedure is ended. If the emotional values Ei (Ei′) satisfy Expression (16), the RBF elements are retrieved and the retrieval count cnt is incremented by one in step 1403. The retrieval of the RBF elements is continued until the retrieval count cnt reaches a maximum retrieval count cntmax and the condition expressed by Expression (16) is satisfied, which means that the control function is effective while the emotional values Ei (Ei′) are within the predetermined range, and an effort to control the emotional values Ei(Ei′) is discontinued. [0247] After one of the RBF elements has been retrieved in step 1403, a query is made in step 1404 to see if actual actions are to be performed. [0248] If actual actions are performed, actions, such as perceptional information processing steps, are performed in step 1405, and then step 1407 is executed. If imaginary actions are performed, imaginary actions, such as perceptional information processing steps, are performed in step 1406 and then step 1407 is executed. [0249] In step 1407, the emotional values Ei (Ei′) held by the emotional value counter 113 are updated, and a query is made in step 1408 to see if Expression (17) is satisfied. minimize  ∑ i = 1 4  d i , ( 17 ) [0250] (1) case where actual actions are performed: [0251] d1=|E1−a1|, d2=|E2|, d3=|E3|, d4=|E4−a4|; [0252] (2) case where imaginary actions are thought: [0253] d1=|E1′−a1|, d2=|E2′|, d3=|E3′|, d4=|E4′−a4|; [0254] 0<a1, a4<b, b is an arbitrary positive constant. [0255] Expression (17) is used for confirming that the sum of the absolute values of d1 to d4 defined as mentioned above is very close to zero, i.e., in the range of 0�(Set value). This means the retrieval of a RBF element that makes the emotional value E1 representing �joy� and the emotional value E4 representing �pleasure� close to the positive values a1 and a4, respectively, and makes the emotional value E2 representing �anger� and the emotional value E3 representing �grief� close to zero. [0256] If the response in step 1408 is affirmative, i.e., if the condition expressed by Expression (17) is satisfied, a query is made in step 1410 to see if the present process corresponds to a case where imaginary actions are thought. If the response in step 1410 is negative, i.e., if the condition corresponds to a case where actual actions are performed, the control procedure is ended. If the condition corresponds to a case where imaginary actions are thought, the intelligent robot performs actual actions (perceptional information processing steps) accompanying the last retrieved RBF element collectively in step 1411. The emotional values E1 to E4 held by the emotional value counter 113 are updated in step 1412 and the control procedure is ended. [0257] If the response in step 1408 is negative, i.e., if the condition expressed by Expression (17) is not satisfied, steps 1402 to 1409 are repeated until the retrieval cycle count cnt coincides with the maximum retrieval cycle count cntmax. [0258] Suppose, by way of example, that the emotional value E3 corresponding to �grief� among the emotional values E1 to E4 held by the emotional value counter 113 of the STM model unit 110 became the greatest after the execution of the series of perceptional information processing steps shown in FIG. 11. In this case, a RBF element that increases the emotional value E1 corresponding to �joy� and the emotional value E4 corresponding to �pleasure�, and reduces the emotional value E3 corresponding to �grief� is retrieved from the RBF map unit 124 according to the condition expressed by Expression (17) to control the emotional value E3 corresponding to �grief�. The RBF element can be highly activated when the intelligent robot spontaneously performs an action or makes something else perform an action to prompt the entry of a condition corresponding to the retrieved RBF element or sensor information; that is, the RBF element can be highly activated by giving an input vector close to the centroid vector of the RBF element to the intelligent robot. Eventually, the emotional values E1 to E4 held by the emotional value counter 113 approach values meeting the condition expressed by Expression (17), i.e., E1=a1, E2=0, E3=0 and E4=a4. [0259] The retrieval of the RBF element in the emotional value counter control method is performed by a trial-and-error method that makes a query to see of the condition expressed by Expression (17) is satisfied. The range of retrieval may be limited on the basis of the condition of the STM network 111 of the STM model unit 110. [0260] When the condition of the comparatively highly activated RBF element 130 among the RBF elements 130 of the hierarchical LTM network unit 121 is held partly in the STM network 111 of the STM model unit 110, the STM network 111 is able to express a current �awareness� states represented by an �attention� function. Therefore, when the range of retrieval is determined for the searching operation of the LTM controller 122 to retrieve the aforesaid RBF element on the basis of the condition of the RBF elements held by the STM network 111, a retrieval operation to retrieve a RBF element is performed in a range focused by the STM network 111, so that the efficiency of the retrieval operation can be improved. For example, if the focus by the STM network 111 is directed particularly to an input vector representing sensor information related with an image of the brother's features, i.e., if the number of RBF elements connected with an image of the brother's features is greater than those of the rest of the RBF elements in the STM network 111, the retrieval operation for retrieving a RBF elements to control the emotional value counter 113 is started with a RBF element closely associated with the image of the brother's features. However, since the focus of the STM network 111 changes continually according to successively entered pieces of sensor information (input vectors), it is possible that the mode of retrieval is changed greatly by sensor information other than the image of the brother's features. Therefore, it is desirable to retrieve a RBF element for controlling the emotional value counter 113 by the method of �retrieving a RBF element that corresponds to an object similar to an object currently focused by the STM network, and that has incrementing/decrementing values of emotional value meeting the condition expressed by Expression (17) �. More concretely, in relation with the image of the features of the late brother, by way of example, if a RBF element corresponding to the image of the features of the living parent, brother or sister of the late brother meets the condition expressed by Expression (17), the same RBF element may be retrieved by the emotional value counter control method. [0261] The memory-chaining system in the second embodiment has the hierarchical LTM network unit 121 including the artificial neural network structure including the plurality of RBF elements 130, and controls the RBF element 130 (the RBF element 130 having the RBF main part 131 that provides an intensity of activation corresponding to the similarity between an input vector and a centroid vector and a pointer part 132 that holds pointer information about the mutually related other RBF elements) by the LTM controller 122 to realize the �memory-chaining� function. Thus, the learning of memory-chaining can be achieved in a comparatively short time and the �memory-chaining� function can be easily realized. Since the number of parameters having a high degree of freedom is small, necessary hardware can be comparatively easily realized. [0262] In the memory-chaining system in the second embodiment, each RBF element 130 includes the incrementing/decrementing value holding part 133, and the current emotional values are determined by adding, to the emotional values held by the emotional value counter 113, the incrementing/decrementing values of emotional value of the RBF element 130 followed during memory-chaining. Therefore, precise �emotion expression� can be achieved by using �memory-chaining�. [0263] The memory-chaining system 100 in the second embodiment is highly flexible and versatile, and, as mentioned above, the memory-chaining system 100 is applicable to emotion-expressing mechanisms, such as intelligent robots. More concretely, when some key stimulus is given as sensor information to an intelligent robot, the intelligent robot achieves �memory-chaining� by using a RBF map formed on the basis of experiments of the intelligent robot, and emotional values corresponding to memory-chaining are set during the process of memory-chaining. Thus, the intelligent robot or the like are able to express emotions easily and precisely. Since the process of memory-chaining carried out by the intelligent robot can be traced and analyzed by a human, materials for final decision for a human are provided and hence a diagnostic system or the like adapted to make a more human, precise decision can be constructed. It is expected that more human sentences can be composed by applying the memory-chaining system 100 in the second embodiment to lexical analysis. [0264] The memory-chaining system 100 in the second embodiment can be realized in hardware by a method that arranges the RBF elements (neurons) 130 in a programmable array or can be realized in software that is executed by, for example, the computer system 40 shown in FIG. 13. The computer system 40 includes the processor 41, the memory 42, the hard disk 43, and peripheral devices including the input device 44 including a keyboard and a mouse, the output device 45 including a display and a printer, the FD drive 46, the CD-ROM drive 47, and the bus 48 interconnecting those component devices. The program is stored in a recording medium from which the computer is able to read data, such as the memory 42, the hard disk 43, the FD 49 or the CD-ROM 50; and the processor 41 reads instructions included in the program sequentially from the recording medium, and executes the instructions to carry out the foregoing procedures. REFERENCES [0265] [1] G. Matsumoto, Y. Shigematsu, and M. 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