Recurrent neural networks have been used for time-series prediction with good results. In this dissertation we compare recurrent neural networks with time-delayed feed forward networks, feed forward networks and linear regression models to see which architecture that can make the most accurate predictions. The data used in all experiments is real-world sales data containing two kinds of segment...

An artificial Neural Network (ANN) is a well known universal approximator to model smooth and continuous functions (Brown & Harris, 1994). As ANNs can realize nonlinear models, they are flexible in modeling a wide variety of real-world complex applications, such as handwriting recognition, speech recognition, fault detection, medical inspection (Zhang, 2000), etc. ANNs being applied for pattern...

In this thesis we focused on subsymbolic approach to machine game play problem. We worked on two different methods of learning. Our first goal was to test the ability of common feed-forward neural networks and the mixture of expert topology. We have derived reinforcement learning algorithm for mixture of expert network topology. This topology is capable to split the problem into smaller parts, ...

The abundance of data available on Wireless Sensor Networks makes online processing necessary. In industrial applications for example, the correct operation of equipment can be the point of interest while raw sampled data is of minor importance. Classification algorithms can be used to make state classifications based on the available data. The distributed nature of Wireless Sensor Networks is ...

Artificial neural networks provide a feasible approach to model complex engineering systems. Computational parallelism is assumed as a basis of the neural architectures. In the Russian Federal Nuclear Center VNIITF there exists a neural simulator Nimfa. In the framework of this project parallel versions of training algorithms for feed-forward neural networks based on the MPI standard are develo...

In this paper, model reference neural network structure is used as a controller for vibration suppression of the Euler–Bernoulli beam under the excitation of moving mass travelling along a vibrating path. The non-dimensional equation of motion the beam acted upon by a moving mass is achieved.  A Dirac-delta function is used to describe the position of the moving mass along the beam and its iner...

Protein Secondary Structure Prediction (PSSP) is considered as a challenging task in bioinformatics and so many approaches have been proposed in the literature to solve this problem via achieving more accurate prediction results. Accurate prediction of secondary structure is a critical role in deducing tertiary structure of proteins and their functions. Among the proposed approaches to tackle t...

Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is devel...