Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Entire DC Network
Design And Implementation Of Two Text Recognition Algorithms, Madhumathi Yendamuri
Design And Implementation Of Two Text Recognition Algorithms, Madhumathi Yendamuri
Theses
This report presents two algorithms for text recognition. One is a neural-based orthogonal vector with pseudo-inverse approach for pattern recognition. A method to generate N orthogonal vectors for an N-neuron network is also presented. This approach converges the input to the corresponding orthogonal vector representing the prototype vector. This approach can restore an image to the original image and thus has error recovery capacility. Also, the concept of sub-networking is applied to this approach to enhance the memory capacity of the neural network. This concept drastically increases the memory capacity of the network and also causes a reduction of the …
A New Method To Optimize The Satellite Broadcasting Schedules Using The Mean Field Annealing Of A Neural Network, Youyi Yu
Theses
This thesis reports a new method for optimizing satellite broadcasting schedules based on the Hopfield neural model in combination with the mean field annealing theory. A clamping technique is used with an associative matrix, thus reducing the dimensions of the solution space. A formula for estimating the critical temperature for the mean field annealing procedure is derived, hence enabling the updating of the mean field theory equations to be more economical. Several factors on the numerical implementation of the mean field equations using a straightforward iteration method that may cause divergence are discussed; methods to avoid this kind of divergence …
Searching For Orthogonal States Of Neural Networks, Heng Wang
Searching For Orthogonal States Of Neural Networks, Heng Wang
Theses
Two approaches to find orthogonal states of neural network are presented in the paper. The first approach is a recursive one, it builds N orthogonal vectors based on N /2 orthogonal vectors. The second approach is a formula approach, in which orthogonal vectors can be obtained using a formula. Using these approaches, orthogonal states of neural network are found. Some properties of the neural network built on these orthogonal vectors are presented in Appendix A and some examples are given in Appendix B.