The aim of this research is to forecast the consumer price index (CPI) of education, recreation, and sport in Indonesia using feedforward neural network (FFNN) model. We consider two FFNN models which are differed from the inputs. The inputs of the first model are generated by considering the inputs such as in a time series model, those are the lags of the CPI. Regarding that the pattern of the...

This paper presents short term load forecasting (STLF) in Java Island using recurrent neural network (RNN). The simple one of RNN is Elman, it has one hidden layer and suitable used in time series prediction. It can learn an input-output mapping which is nonlinear. The Elman RNN was proposed for one day a head forecasting, with interval time 30 minutes. Training model divided into weekday, week...

In an islanded microgrid, while considering the complex nature of line impedance, the generalized droop control fails to share the actual real/reactive power between the distributed generation (DG) units. To overcome this power sharing issue, in this paper a new approach based on feed forward neural network (FFNN) is proposed. Also, the proposed FFNN based droop control method simultaneously co...

In this paper, an automatic classifier has been developed using Feed Forward Neural Network (FFNN) to classify the ECG signals between different heartbeats. Here, the classifier is trained independently bymorphological, heartbeat interval features and temporal features using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The trained classifier then classifies the be...

This study proposes a novel approach based on multi-objective artificial bee colony (ABC) for feature selection, particularly for intrusion-detection systems. The approach is divided into two stages: generating the feature subsets of the Pareto front of non-dominated solutions in the first stage and using the hybrid ABC and particle swarm optimization (PSO) with a feed-forward neural network (F...

A system that segments and labels tabla strokes from real performances is described. Performance is evaluated on a large database taken from three performers under different recording conditions, containing a total of 16,834 strokes. The current work extends previous work by Gillet and Richard (2003) on categorizing tabla strokes, by using a larger, more diverse database that includes their dat...

The energy sector which includes gas and oil is concerned to explore develop refined it’s a multitrillion business. As crude very important source of energy, it has valuable impact on country’s economic growth, national security, social stability. Therefore, accurately predicting the price volatility topic research still, challenge for researchers forecast prices. this study conducted address s...

Minimizing dilution is essential in open stope mine design as excessive unplanned can compromise the operation's profitability. One of main challenges associated with empirical graph method used to stopes how determine boundary zones objectively. Hence, this paper explores implementation machine learning classifiers bridge gap conventional method. Stope performance data consisting (unplanned di...

A new hybrid clustering algorithm based on a three-layer feed forward neural network (FFNN), a distribution density function, and a cluster validity index, is presented in this paper. In this algorithm, both feature weighting and sample weighting are considered, and an optimal cluster number is automatically determined by the cluster validity index. Feature weights are learnt via FFNN based on ...

In this paper, we show how Bayesian neural networks can be used for time series analysis. We consider a block based model building strategy to model linear and nonlinear features within the time series. A proposed model is a linear combination of a linear autoregression term and a feedforward neural network (FFNN) with an unknown number of hidden nodes. To allow for simpler models, we also cons...