Neural Network World; International Journal on Neural and Mass - Parallel Computing and Information Systems

Combination of Neural Networks Forecasters for Monthly Natural Gas Consumption Prediction

This study presents different types of neural network algorithm based model forecasting gas consumption for residential and commercial consumers in Istanbul in Turkey. Using seven neural networks algorithms as forecasting models, we tried to find the best solution on forecasting of monthly natural gas consumption. These models were validated and tested on real monthly data from a distribution area covering two different regions of Anatolian and European sides in Istanbul. The analysis of resu...

Optimal Component Selection Using a Multiobjective Evolutionary Algorithm

A component selection is a crucial problem in Component-Based Software Engineering (CBSE), which is concerned with the assembly of pre-existing software components. We are approaching the component selection involving dependencies between components. We formulate the problem as multiobjective, involving two objectives and one constraint. The approach used is an evolutionary computation technique. The experiments and comparisons with the greedy approach show the effectiveness of the proposed a...

Detection of Retinopathy Diseases Using an Artificial Neural Network Based On the Discrete Cosine Transform

The retinopathy diseases occur when the neurons do not transmit signals from retina to the brain. These disorders are: Diabetic retinopathy, hypertensive retinopathy, macular degeneration, vein branch occlusion, vitreous hemorrhage, and normal retina. This work presents a novel detection algorithm about retinopathy disorders from retina images. For this purpose, the retina images were pre-processed and resized at first. Then the discrete cosine transform was used as feature extraction before ...

Simulated 3d Biped Walking with an Evolution-Strategy Tuned Spiking Neural Network

This paper presents the results of experiments in applying a spiking neural network to control the locomotion of a simulated biped robot. The neural model used in simulations was developed to allow for an analytic solution to a neuron fire time, while maintaining a non-instant post-synaptic potential rise time. The synaptic weights and delays were tuned using an evolution strategy. Simulation experiments demonstrate that within about seven thousand generations the biped is able to acquire a d...