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Israel, G.C., Angel, G.C. and Belen, R.M. (2011) Dealing with Limited Data in Ballistic Impact Scenarios: An Empirical Comparison of Different Neural Network Approaches. Application Intelligence, 35, 89-109. http://dx.doi.org/10.1007/s10489-009-0205-8

has been cited by the following article:

• TITLE: Quantum-Inspired Neural Networks with Application

  AUTHORS: Jianping Li

  KEYWORDS: Quantum Computing, Neural Networks, Algorithm Design

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.5 No.6, May 27, 2015

  ABSTRACT: In this paper, a novel neural network is proposed based on quantum rotation gate and controlled- NOT gate. Both the input layer and the hide layer are quantum-inspired neurons. The input is given by qubits, and the output is the probability of qubit in the state . By employing the gradient descent method, a training algorithm is introduced. The experimental results show that this model is superior to the common BP networks.