We present an interval arithmetic neural network based on the FIR (Finite Impulse Response) network to improve the prediction power and flexibility. The model for the system is obtained by modifying the dynamics of the FIR network to incorporate the interval arithmetic operations. In this paper, we describe the network architecture and behavior of the interval arithmetic FIR network. We present...

The paper proposes a digital circuit design for the logistic-map module used in chaotic stream ciphers, analyzes the factors that may affect the output of the sequences, and develops a calculation method for estimating the output sequential correlative peak interval. With the respective tests using different initial values, the values of parameter u and the computational precisions, extensive e...

A radial basis function neural network (RBFNN) model is employed to predict the short-interval (within 15-minute) traffic series, including flow, speed and occupancy, which are measured in different time intervals, time lags, dimensions of state spaces, and times of day. Aside from describing entirely the methodology of RBFNN, the paper also uses two deterministic functions to test prediction p...

Recently, Diebold and Kilian [Unit root tests are useful for selecting forecasting models, Journal of Business and Economic Statistics 18, 265-273, 2007] and Niwitpong [Effect of Preliminary unit roots on predictors for an unknown mean AR(1) process, Thailand Statistician 7, 71-79, 2009] indicated that the preciseness of a predictor for an AR(1) process can be increased by using the preliminary...

The existing uncertainties during the operation of processes could strongly affect performance forecasting systems, control strategies and fault detection systems when they are not considered in design. Because that, study uncertainty quantification has gained more attention among researchers past decades. From this field study, prediction intervals arise as one techniques most used literature ...

Having a regression model, we are interested in finding two-sided intervals that are guaranteed to contain at least a desired proportion of the conditional distribution of the response variable given a specific combination of predictors. We name such intervals predictive intervals. This work presents a new method to find two-sided predictive intervals for non-parametric least squares regression...