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This paper deals with a design methodology for a neural network with improved robust qualities in notion to handling uncertain input data space variations. The proposed network topology combines the simplicity of the radial basis functions networks to interpret or classify data pairs and the abilities of the intuitionistic fuzzy logic to deal with the vagueness of the data space. A simplified gradient...
This paper presents two parallel implementationsof the Back-propagation algorithm, a widely used approach forArtificial Neural Networks (ANNs) training. These implementationspermit one to increase the number of ANNs trainedsimultaneously taking advantage of the thread-level massiveparallelism of GPUs and multi-core architecture of modernCPUs, respectively. Computational experiments are carried outwith...
Integration of solar generation into power networks can negatively affect the performance of next generation smart energy grids. Rapidly changing output power of this kind is unpredictable and thus one of the solutions is to predict it by computational intelligence techniques. The stochastic component of solar radiation is highly non-linear in nature because of many factors including time of the year,...
This paper proposes a hybrid short-term load forecasting (STLF) framework with a new, more efficient, input selection method. Correlation analysis and ℓ2-norm are used in combination to select suitable inputs to individual Bayesian neural networks (BNNs), which are used to forecast the load. Forecast outputs are then weighted using calculated weighting coefficients and summed to obtain the final forecast...
In this paper, we apply the Beta Basis Function Neural Network (BBFNN) trained with cuckoo search (CS) for time series predictions. The cuckoo search algorithm optimizes the network parameters. In order to evaluate the effectiveness of the proposed method, we have carried out some experiments on four data sets: Mackey Glass, Lorenz attractor, Henon map and Box-Jenkins. We give also simulation examples...
Statistical Process Control (SPC) is widely used for monitoring the performance of processes in manufacturing. Traditional SPC methods require trained individuals to read data which results in slow and limited detection. Much research has been devoted into developing an online automated system for SPC, so that the abnormality can be detected quickly and corrected by the process operation. To build...
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