At a Glance: This article describes a prototype of a discrete event simulator-Y4 (Yield Forecaster)-capable of simulating defect related yield loss as a function of time, for a multi-product IC manufacturing line. The methodology of estimating yield and cost is based on mimicking the operation and characteristics of a manufacturing line in the time domain. The effect of particles introduced dur...

In complex task domains, such as games, students may exceed their teachers. Such tasks afford diverse means to tradeoff one type of performance for another, combining task elements in novel ways to yield method variations and strategy discoveries that, if mastered, might produce large or small leaps in performance. For the researcher interested in the development of extreme expertise in the wil...

Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

Yield forecasting is critical to a semiconductor manufacturing factory. To further enhance the effectiveness of semiconductor yield forecasting, a fuzzy-neural approach with collaboration mechanisms is proposed in this study. The proposed methodology is modified from Chen and Lin’s approach by incorporating two collaboration mechanisms: favoring mechanism and disfavoring mechanism. The former h...

This study examines the forecasting performance of Adaptive Neuro Fuzzy Inference System(ANFIS) compared in comparison to statistical autoregressive integrated moving average (ARIMA) and the artificial neural network (ANN) model in forecasting of rice yield production.. To assess the effectiveness of these models, we used 9 years of time series records for rice yield data in Malaysia from 1995 ...

Accurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which conn...

Geostatistical spatial models are widely used in many applied fields to forecast data observed on continuous three-dimensional surfaces. We propose to extend their use to finance and, in particular, to forecasting yield curves. We present the results of an empirical application where we apply the proposed method to forecast Euro Zero Rates (2003–2014) using the Ordinary Kriging method based on ...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

In the context of the GEOLAND EC FP6 project the comparison of different remote sensing based approaches for yield forecasting over large areas in Europe are tested and results inter-compared. In particular the methods tested include the ones in use within the MARS-Crop Yield Forecasting System as the results from the Crop Growth Monitoring System model and vegetation indicators derived from Lo...

Crop decision planning is an important part of effective farm management. Because of the many uncertain factors such as weather variations, technology advances, and crop yields and prices, all of which prove to change considerably, decision planning can be very complex. The focus of this paper is to develop accurate yield and price forecasting models to aid in decision planning. For yield forec...