Forecasting of wave height is necessary in a large number of ocean coastal activities. Recently, neural networks are used for prediction and approximation of wave heights in sea and ocean due to their great convergence rate. In this paper a cascade correlation neural network is used for prediction of wave heights at given times due to the useful capability of this network for prediction and app...

We review some aspects of our recent work on the approximation of functions by neural and generalized translation networks.

In this paper, an innovative neural-network architecture is proposed and elucidated. This architecture, based on the Kolmogorov's superposition theorem (1957) and called the Kolmogorov's spline network (KSN), utilizes more degrees of adaptation to data than currently used neural-network architectures (NNAs). By using cubic spline technique of approximation, both for activation and internal func...

We investigate the linear stability of a neural network with distributed delay, where the neurons are identical. We examine the stability of a symmetrical equilibrium point via the analysis of the characteristic equation both when the connection matrix is symmetric and when it is not. We determine a mean delay and distribution independent stability region. We then illustrate a way of improving ...

In this work, some ubiquitous neural networks are applied to model the landscape of a known problem function approximation. The performance of the various neural networks is analyzed and validated via some well-known benchmark problems as target functions, such as Sphere, Rastrigin, and Griewank functions. The experimental results show that among the three neural networks tested, Radial Basis F...