Tham khảo tài liệu 'artificial mind system – kernel memory approach - tetsuya hoya part 4', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 228 10 Modelling Abstract Notions Relevant to the Mind STM Is represented by a collection of kernel units and partially 11 the associated control mechanism. The kernel units within the STM are divided into the attentive and non-attentive kernels by the control mechanism. LTM Kernel Memory 2 to L Is considered as regular LTM. In practice it is considered that each Kernel Memory 2 to L is partitioned according to the do-main modality specific data. For instance provided that the kernel units within Kernel Memory i i 2 3 . L are arranged in a matrix as in Fig. on the left hand side the matrix can be sub-divided into several data- modality-dependent areas or submatrices . LTM Kernel Memory 1 for Generating the Intuitive Outputs Is essentially the same as Kernel Memory 2 to L except that the kernel units have the direct paths to the input matrix Xjn and thereby can yield the intuitive outputs. In both the STM and LTM parts the kernel unit representation in Fig. or is alternatively exploited. Then in Fig. provided that the kernel units within Kernel Memory i i 1 2 . L are arranged in a matrix as in Fig. on the left hand side 12 the matrix can be sub-divided into several data-dependent areas or sub-matrices . In the figure each modality specific area . auditory visual etc is represented by a column . the total number of columns can be equivalent to Ns the total number of sensory inputs and each column sub-matrix is further sub-divided and responsible for the corresponding data sub-area . alphabetic digit character or voice recognition sub- area and so forth. Thus this somewhat simulates the PRS within the implicit LTM. Then a total of Ns pattern recognition results can be obtained at a time from the respective areas of the i-th Kernel Memory and eventually given as a vector yj . Since the formation of both the STM and LTM parts can be followed by essentially the same evolution schedule as that of the HA-GRNN . from Phase 1 to Phase
