Discrete neural computation: a theoretical foundation
Discrete neural computation: a theoretical foundation
Global Robust Exponential Stability of Interval Neural Networks with Delays
Neural Processing Letters
On Robust Exponential Periodicity of Interval Neural Networks with Delays
Neural Processing Letters
A neural root finder of polynomials based on root moments
Neural Computation
Enumeration of linear threshold functions from the lattice of hyperplane intersections
IEEE Transactions on Neural Networks
A constructive approach for finding arbitrary roots of polynomials by neural networks
IEEE Transactions on Neural Networks
Zeroing polynomials using modified constrained neural network approach
IEEE Transactions on Neural Networks
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The implementation of multi-valued logic with a three layers forward neural network is proposed. The hidden layer is constituted by bi-threshold neurons compared with traditional simple threshold neurons. According to the obtained results in this paper, if a perception with a simple threshold neuron is used in the output layer, then the logical map of {0,1, 茂戮驴 ,n} to {0,1} can be gained. In addition, if a linear neuron is used in the output layer, then the logical map of {0,1, 茂戮驴 ,n} to {0,1, 茂戮驴 ,m} can be obtained. The arithmetic, which is used to design the three layers forward neural network, improves on the traditional digital logic of two values. An example shows that the designable procedure of the network is simple and effective.