Modular multilevel converter (MMC) has attracted much attention for years due to its good performance in harmonics reduction and efficiency improvement. Model predictive control (MPC) based controllers are widely adopted MMC because the design is straightforward different objectives can be simply implemented a cost function. However, computational burden of MPC imposes limitations implementatio...

In this article, we address the nonlinear behavioral modeling of transceivers using feed-forward neural networks (FNNs) such that each modular block functions independently in a high-speed link (HSL) simulation. proposed technique, transceiver models are represented form kernel matrices, which values determined through FNN training. By feeding with information on voltages and protocols, time-do...

Researches on machined surface roughness prediction in the face milling process of steel are presented in the paper. The data for modelling by the application of neural networks have been collected by the central composite design of experiment. Input variables are the parameters of machining (number of revolutions – cutting speed, feed and depth of cut) and the way of cooling, while the machine...

In this paper we present a parallel and modular multi-sieving neural network (PMSN) architecture for constructive learning. This PMSN architecture is dierent from existing constructive learning networks such as the cascade correlation architecture. The constructing element of the PMSNs is a compound modular network rather than a hidden unit. This compound modular network is called a sieving mod...

The evolutionary approach to artificial neural networks has been rapidly developing in recent years and shows great potential as a powerful tool. However, most evolutionary neural networks have paid little attention to the fact that they can evolve from modules. This paper presents a hybrid method of modular neural networks and evolutionary algorithm as a promising model for intelligent systems...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

A modular hybrid neural network architecture, called SHAME, for emotion learning is introduced. The system learns from annotated data how the emotional state is generated and changes due to internal and external stimuli. Part of the modular architecture is domain independent and part must be adapted to the domain under consideration. The generation and learning of emotions is based on the event...