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Hybrid evolutionary techniques in feed forward neural network with distributed error for classification of handwritten Hindi ‘SWARS’ Somesh Kumar, Manu Pratap Singh, Rajkumar Goel & Rajesh Lavania a Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India b Department of CS&E, IET, Khandari Campus, Dr. B. R. Ambedkar University,(More)
We develop protocols for preparing a GHZ state and, in general, a pure multi-partite maximally entangled state in a distributed network with apriori quantum entanglement between agents using classical communication and local operations. We investigate and characterize the minimal combinatorics of the sharing of EPR pairs required amongst agents in a network(More)
With device dimensions reaching their physical limits, there has been a tremendous focus on development of post CMOS technologies. Carbon based transistors, including graphene and carbon nanotubes, are seen as potential candidates to replace traditional CMOS devices. In that, floating gate graphene field effect transistors (F-GFETs) are preferred over dual(More)
Function approximation is an instance of supervised learning which is one of the most studied topics in machine learning, artificial neural networks, pattern recognition, and statistical curve fitting. In principle, any of the methods studied in these fields can be used in reinforcement learning. Multi-layered feed-forward neural networks (MLFNN) have been(More)