Using Three Layer Neural Network to Compute Multi-valued Functions
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Using kolmogorov inspired gates for low power nanoelectronics
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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The principal purpose of this research is to discover the underlying properties of linear multilevel threshold devices and networks of linear multilevel threshold devices. The main theoretical developments of this investigation entail generalizing the function-counting theorem associated with linear bi-level threshold devices to that of linear multilevel threshold devices. The investigation reveals surprising connections that underlie linear inequalities, linear separability, linear ordering, and region counting. The results have implications in the field of geometric probability. Computations based on theoretical analysis support Brown's (1964) conjecture and suggest that k/(k-1) is a natural definition for the information-storage capacity of a k-level threshold device. A lower bound on the number of weights required to implement a universal network of k-level threshold devices is derived. Finally, it is shown that the Vapnik-Chervonenki (1971)s dimension (VC-dimension) for the class of multilevel threshold function is d+1 for pattern vectors in ℛd . This VC-dimension is also linked to an error-rate bound for multilevel threshold functions within the framework of uniform learnability