By Tetsuya Hoya
This ebook is written from an engineer's point of view of the brain. "Artificial brain process" exposes the reader to a huge spectrum of attention-grabbing parts regularly mind technological know-how and mind-oriented stories. during this study monograph an image of the holistic version of a synthetic brain method and its behaviour is drawn, as concretely as attainable, inside of a unified context, which can finally bring about useful realisation by way of or software program. With a view that "the brain is a method continuously evolving", principles encouraged via many branches of reviews regarding mind technology are built-in in the textual content, i.e. synthetic intelligence, cognitive technological know-how / psychology, connectionism, realization stories, common neuroscience, linguistics, development reputation / facts clustering, robotics, and sign processing.
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Additional info for Artificial Mind System - Kernel Memory Approach
It can therefore be concluded that any “catastrophic” forgetting of the previously stored data due to accommodation of new classes did not occur, which meets Criterion 4). g. e. the necessity to tune a number of network parameters to obtain a good convergence rate or worry about any numerical instability such as local minima or long 26 2 From Classical Connectionist Models to PNNs/GRNNs and iterative training of the network parameters. As described earlier, by exploiting the property of PNNs/GRNNs, simple and quick incremental learning is possible due to their inherently memory-based architecture6 , whereby the network growing/shrinking is straightforwardly performed (Hoya and Chambers, 2001a; Hoya, 2004b).
2) does not match the form derived originally from the conditionally probabilistic approach (Specht, 1990, 1991). g. hardware representation. 2) is adopted in this book, since the relative values of the output neurons are given, instead of the original one. 16 2 From Classical Connectionist Models to PNNs/GRNNs In the above, cj is called the centroid vector, σj is the radius, and wj denotes the weight vector between the j-th RBF and the output neurons. In the case of a PNN, the weight vector wj is given as a binary (0 or 1) sequence, which is identical to the target vector.
E. 1) straightforward network conﬁguration (Hoya and Chambers, 2001a; Hoya, 2004b), 2) robust classiﬁcation performance, and 3) capability in accommodating new classes (Hoya, 2003a). These properties are not only desirable for on-line data processing but also inevitable for modelling psychological functions (Hoya, 2004b), which eventually leads to the development of kernel memory concept to be described in the subsequent chapters. Finally, to emphasise the attractive properties of PNNs/GRNNs, a more informative description by means of the comparison with some common connectionist models and PNNs/GRNNs is given.