Anti-periodic solutions for high-order cellular neural networks with time-varying delays.
Anti-periodic solutions for recurrent neural networks without assuming global Lipschitz conditions.
Application of Neural Networks to Modal Split Modelling
Application of Neural-Net Computing to Transient Stability Assessment and Enhancement of Electric Power Systems
Approximating the Stability Region for a Differential Equation with a Distributed Delay
We discuss how distributed delays arise in biological models and review the literature on such models. We indicate why it is important to keep the distributions in a model as general as possible. We then demonstrate, through the analysis of a particular example, what kind of information can be gained with only minimal information about the exact distribution of delays. In particular we show that a distribution independent stability region may be obtained in a similar way that delay independent...
Approximation of phenol concentration using novel hybrid computational intelligence methods
Asymptotical mean square stability of Cohen-Grossberg neural networks with random delay.
Automaty a mozek
Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network
In this paper the Sigma-if artificial neural network model is considered, which is a generalization of an MLP network with sigmoidal neurons. It was found to be a potentially universal tool for automatic creation of distributed classification and selective attention systems. To overcome the high nonlinearity of the aggregation function of Sigma-if neurons, the training process of the Sigma-if network combines an error backpropagation algorithm with the self-consistency paradigm widely used in physics....
Bifurcation analysis for a two-dimensional discrete-time Hopfield neural network with delays.
Biologically inspired robotic arm control using an artificial neural oscillator.
Boundedness and stability for discrete-time delayed neural network with complex-valued linear threshold neurons.
Canonical models in mathematical neuroscience.
Cognition: Differential-geometrical view on neural networks.
Comparison of supervised learning methods for spike time coding in spiking neural networks
In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recent learning methods? In order...
Complete classification and stability of equilibria in a delayed ring network.
Convergence analysis for principal component flows
A common framework for analyzing the global convergence of several flows for principal component analysis is developed. It is shown that flows proposed by Brockett, Oja, Xu and others are all gradient flows and the global convergence of these flows to single equilibrium points is established. The signature of the Hessian at each critical point is determined.
Convergence and periodicity in a delayed network of neurons with threshold nonlinearity.
Convergence and periodicity of solutions for a class of difference systems.
Convergence of batch split-complex backpropagation algorithm for complex-valued neural networks.