Abstract—This paper presents the development of an algorithm that predicts the arrival of a secondary user (SU) to a base station (BS) in a cognitive network based on infrastructure, requesting a Best Effort (BE) or Real Time (RT) type of service with a determined bandwidth (BW) implementing neural networks. The algorithm dynamically uses a neural network construction technique using the geomet...

* corresponding author: [email protected] Abstract This paper presents a comparison study between radial basis function neural network (RBFNN), feed forward multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy (ANFIS) technique to model the activated sludge process (ASP). All of these techniques are based on the nonlinear autoregressive with eXogenous input (NARX) structure. The...

Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable rec...

Conventional treatment methods such as chlorination and ozonation have been proven not to be effective in eliminating degrading contaminants Bisphenol A (BPA) from wastewater. Hence, the degradation of BPA using a photocatalytic reactor has received lot attention recently. In this study, model-based approach multilayer perceptron neural network (MLPNN) coupled with back-propagation, well suppor...

Short term load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of power system. Artificial neural networks have long been proven as a very accurate non-linear mapper. ANN based STLF models generally use Back propagation algorithm which does not converge optimally & requires much longer time for training, which mak...

In recent years, gully erosion has caused soil loss, land degradation, and a large sediment yield in the Mollisols northeastern China, threatening agricultural development national food security. Moreover, prediction of remains great challenge owing to difficulty determining suitable environmental indicators identifying best models for predicting prone areas. Therefore, objective this study was...

Artificial neural networks (ANNs) are computational modeling tools that have recently emerged and found extensive acceptance in many disciplines for modeling complex realworld problems. ANN-based models are empirical in nature, however they can provide practically accurate solutions for precisely or imprecisely formulated problems and for phenomena that are only understood through experimental ...