Training neural networks to capture an intrinsic property of a large volume of high dimensional data is a difficult task, as the training process is computationally expensive. Input attributes should be carefully selected to keep the dimensionality of input vectors relatively small. Technical indexes commonly used for stock market prediction using neural networks are investigated to determine i...

In this paper, design and VLSI Implementation of an Early Branch Prediction (EBP) circuit, based on a variation of Carry Look-ahead scheme is presented. The key features of this design are low area, high speed (2 [Zog 11/21 + I), and high modularity. This design out performs all the EBP designs presented so far. For 64-bit word length the early branch prediction is obtained in 679 ps as simulat...

In Support Vector Machines (SVM's), a non-linear model is estimated based on solving a Quadratic Programming (QP) problem. The quadratic cost function consists of a maximum likelihood cost term with constant variance and a regularization term. By specifying a diierence inclusion on the noise variance model, the maximum likelihood term is adopted for the case of heteroskedastic noise, which aris...

Skillful medium-range weather forecasts are critical for water resources planning and management. This study aims to improve 15-day-ahead accumulated precipitation forecasts by combining biweekly weather and disaggregated climate forecasts. A combination scheme is developed to combine reforecasts from a numerical weather model and disaggregated climate forecasts from ECHAM4.5 for developing 15-...

Roundoff error analysis of a pipelined adaptive differential pulse code modulation (ADPCM) coder is presented. The pipelined coder has been developed by employing the relaxed look-ahead technique. It is shown that the precision of the quantized prediction error and those of the predictor coefficients are critical and analytical expressions for lower bounds on these predsions are derived. In add...

To take into account different character of distinct segments of non-stationary financial time series the multi-agent system based forecasting algorithm is suggested. The primary goal of present paper is to introduce methodological findings that could help to reduce one step ahead forecasting error. In contrast to previous investigation [6], instead of single prediction rule we use a system of ...

Since the reliability of production plans drops largely within several days after plan creation, control faces huge challenges, when trying to foresee work in progress (WIP) level at bottleneck machines and react appropriately. Whereas researchers applied artificial intelligence predict lead times or transition improve planning reliability, only small efforts have been taken on time series pred...

[1] Daily aerosol optical thickness (AOT) at 0.55 mm over the desert regions is needed as a source of validation for numerical models such as the United Kingdom’s Numerical Weather Prediction Unified Model. We examined the relationship between monthly mean ultraviolet (UV) absorbing aerosol index (AI) from the Ozone Monitoring Instrument (OMI) that is available on a daily basis with the Multian...

Accurate and effective stock price prediction is appealing for investors due to the potential of obtaining a very high return. However, it is still a challenging task in the modern business world because of the complex, evolutionary, and nonlinear nature of stock market. Therefore, we proposed two hybrid models, which are Harmony Search (HS) based Extreme Learning Machine (ELM) that is denoted ...

The large-scale integration of wind generation imposes several difficulties in the management of power systems. Wind power forecasting up to a few days ahead contributes to a secure and economic power system operation. Prediction models of today are mainly focused on spot or probabilistic predictions of wind power. However, in many applications, endusers require additional tools for the on-line...