By Fallah F
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On the bright side, the linked-list data structure is much more tolerant of "nonstandard" network connectivity schemes; that is, once the code has been written to enable the CPU to step through a standard list of input connections, no code modification is required to step through a nonstandard list. In this case, all the overhead is imposed on the software that constructs the original data structure for the network to be simulated. Once it is constructed, the CPU does 4 This description is an obvious oversimplification, since it does not consider potential differences in the amount of memory used by pointers and floating-point numbers, virtual-memory systems, or other techniques for extending physical memory.
2 Vector Formulation In many of the network models that we shall discuss, it is useful to describe certain quantities in terms of vectors. Think of a neural network composed of several layers of identical processing elements. 2 From Neurons to ANS 21 units, the outputs of that layer can be thought of as an n-dimensional vector, X = (x\ , X2, • • • , xnY, where the t superscript means transpose. In our notation, vectors written in boldface type, such as x, will be assumed to be column vectors. When they are written row form, the transpose symbol will be added to indicate that the vector is actually to be thought of as a column vector.
Huang and Richard P. Lippmann. Comparison between neural net and conventional classifiers. In Proceedings of the IEEE First International Conference on Neural Networks, San Diego, CA, pp. 494 June 1987. Willian Y. Huang and Richard P. Lippmann. Neural net and traditional classifiers. In Proceedings of the Conference on Neural Information Processing Systems, Denver, CO, November 1987. Tarun Khanna. Foundations of Neural Networks. Addison-Wesley, Reading, MA, 1990.  C. Klimasauskas. The 1989 N'euro-Computing Bibliography, MIT Press, Cambridge, MA, 1989.
Algorithm for factorization of logic expressions by Fallah F