Tham khảo tài liệu 'artificial mind system – kernel memory approach - tetsuya hoya part 3', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 208 10 Modelling Abstract Notions Relevant to the Mind Input x Fig. . The hierarchically arranged generalised regression neural network HA-GRNN - modelling the notion of attention intuition LTM and STM within the evolutionary process of the HA-GRNN. As the name HA-GRNN denotes the model consists of a multiple of dynamically reconfigurable neural networks arranged in a hierarchical order each of which can be realised by a PNN GRNN see Sect. or a collection of the RBFs and the associated mechanism to generate the output . for both LTM Net 1 and the STM Then in Fig. x denotes the incoming input pattern vector to the HA-GRNN OSTM is the STM output vector OLTM ị i 1 2 . L are the LTM network outputs vị are the respective weighting values for the LTM network outputs and oNET is the final output obtained from the HA-GRNN . given as the pattern recognition result by 3 above . The original concept of the HA-GRNN was motivated from various studies relevant to the memory system in the brain James 1890 Hikosaka et al. 1996 Shigematsu et al. 1996 Osaka 1997 Taylor et al. 2000 Gazzaniga et al. 2002 . Architectures of the STM LTM Networks As in Fig. the LTM networks are subdivided into two types of networks one for generating intuitive outputs LTM Net 1 and the rest LTM Net 2 to LTM Net L for the regular outputs. For the regular LTM each LTM Net 2 to L is the original PNN GRNN and thus has the same structure as shown in the right part of Fig. on 209 Embodiment of Attention Intuition LTM and STM Modules Fig. . The architecture of the STM network - consisting of multiple RBFs and the associated LIFO stack-like mechanism to yield the network output. Note that the STM network output is given as a vector instead of a scalar value Fig. . The architecture of LTM Net 1 - consisting of multiple RBFs and the associated mechanism to yield the network output . by following the winner-takes-all strategy page 15 whereas both the STM and LTM .
