Computing with discrete multi-valued neurons

Complex-Valued Neural Networks with Multi-Valued Neurons

Computing discrete logarithm by interval-valued paradigm

Interval-valued computing is a relatively new computing paradigm. It uses finitely many interval segments over the unit interval in a computation as data structure. The satisfiability of Quantified Boolean formulae and other hard problems, like integer factorization, can be solved in an effective way by its massive parallelism. The discrete logarithm problem plays an important role in practice,...

Discrete Multi-Valued Particle Swarm Optimization

We present a new optimization technique based on the Particle Swarm Optimization algorithm to be used for solving problems with unordered discrete-valued solution elements. The algorithm achieves comparable performance to both the previous modification of PSO for binary optimization and to genetic algorithms on a standard set of benchmark problems. Performance is maintained on these problems re...

Nonlinear Viscosity Algorithm with Perturbation for Nonexpansive Multi-Valued Mappings

In this paper, based on viscosity technique with perturbation, we introduce a new non-linear viscosity algorithm for finding a element of the set of fixed points of nonexpansivemulti-valued mappings in a Hilbert space. We derive a strong convergence theorem for thisnew algorithm under appropriate assumptions. Moreover, in support of our results, somenumerical examples (u...

A Feedforward Neural Network based on Multi-Valued Neurons

A feedforward neural network based on multi-valued neurons is considered in the paper. It is shown that using a traditional feedforward architecture and a high functionality multi-valued neuron, it is possible to obtain a new powerful neural network. Its learning does not require a derivative of the activation function and its functionality is higher than the functionality of traditional feedfo...