Minimum description length (MDL) principle is one of the wellknown solutions for overlearning problem, specifically for artificial neural networks (ANNs). Its extension is called representational MDL (RMDL) principle and takes into account that models in machine learning are always constructed within some representation. In this paper, the optimization of ANNs formalisms as information represen...

The artificial neural networks (ANNs) are well suitable to solve a variety class of problems in a knowledge discovery field (e.g., in natural language processing) because the trained networks are more accurate at classifying the examples that represent a problem domain. However, the neural networks that consist of large number of weighted connections (called also links) and activation units oft...

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. ANNs are one of the three main components of computational intelligence and, as such, they have been often hybridized from different perspectives. In this paper, a review of some of the main contributions for hybrid ANNs is given, considering three points of views: mode...

Video games continue to grow in importance as a platform for Artificial Intelligence (AI) research since they offer a rich virtual environment without the noise present in the real world. In this paper, a simulated ship combat game is used as an environment for evolving neural network controlled ship combat strategies. Domain knowledge is used as input to the Artificial Neural Networks (ANNs) t...

Many artificial intelligence (AI) techniques are inspired by problem-solving strategies found in nature. Robustness is a key feature in many natural systems. This paper studies robustness in artificial neural networks (ANNs) and proposes several novel, nature inspired ANN architectures. The paper includes encouraging results from experimental studies on these networks showing increased robustne...

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do no...

Rapid evaluation of demand parameters of different types of  buildings is crucial for social restoration after damaging earthquakes. Previous studies proposed numerous methodologies to measure the performance of buildings for assessing the potential risk under the seismic hazard. However, time-consuming Nonlinear Response History Analysis (NRHA) barricaded implementing a prompt loss estimation ...

Artificial Neural Networks (ANNs) must be able to learn by experience from environment. This property can be considered as being closely related to the approximating capabilities of the networks. Unfortunately at present only the ability of ANNs in approximating deterministic input-output mappings has been exploited. In this article it has been shown that some classes of neural networks, named ...

a local scouring phenomenon is one of the important problems in hydraulic design of groynes. due to constriction and downward flow, the scouring can occur around the groynes. nowadays, the artificial neural networks have a lot of applications in various water engineering problems where there is not any specific relation between effective parameters. in this study, the artificial neural networks...