Cellular neural networks (CNNs) are one type of interconnected neural network and differ from the well-known Hopfield model in that each cell has a piecewise linear output function. In this paper, we present a multi-valued CNN model in which each nonlinear element consists of a multi-valued output function. The function is defined by a linear combination of piecewise linear functions. We conduc...

Abstract This paper investigates the problem of finite-/fixed-time synchronization for Clifford-valued recurrent neural networks with time-varying delays. The considered drive and response system models are firstly decomposed into real-valued in order to overcome difficulty noncommutativity multiplication Clifford numbers. Then, suitable time-delayed feedback controllers devised investigate err...

| The aim of this work is to present a non-linear extension to Sanger's generalized Hebbian learning algorithm for complex-valued signal processing by neural networks. A possible choice of the involved non-linearity is discussed by recalling the Sudjianto-Hassoun interpretation of non-linear Hebbian learning. An extension of this interpretation to the complex-valued case leads to a Rayleigh non...

We describe a new neuron model, -dimensional vector neuron, which can deal with signals as one cluster, by extending the 3-dimensional vector neuron to dimensions naturally. The -bit parity problem which cannot be solved with a single usual real-valued neuron, can be solved with a single -dimensional vector neuron with the orthogonal decision boundary, which reveals the potent computational pow...