Fuzzy Back Propagation Network Research Articles

An optimized tailored nonlinear fluctuation smoothing rule for scheduling a semiconductor manufacturing factory

An optimized tailored nonlinear fluctuation smoothing rule is proposed in this study to improve the performance of scheduling jobs in a semiconductor manufacturing factory. The tailored nonlinear fluctuation smoothing rule is modified from the well-known fluctuation smoothing rules with some innovative treatments. At first, the fuzzy c-means and fuzzy back propagation network approach is applied to estimate the remaining cycle time of every job. Then the nonlinear fluctuation smoothing rules are constructed and tailored to the semiconductor manufacturing factory to be scheduled. Finally, the back propagation network optimization approach is proposed to optimize the rule. To evaluate the effectiveness of the proposed methodology, production simulation is also applied in this study to generate some test data. According to experimental results, the proposed methodology outperformed nine existing approaches in reducing the cycle time averages and standard deviations.

Job remaining cycle time estimation with a post-classifying fuzzy-neural approach in a wafer fabrication plant: A simulation study

A post-classifying fuzzy-neural approach is proposed in this study for estimating the remaining cycle time of each job in a wafer fabrication plant, which has seldom been investigated in past studies but is a critical task for the wafer fabrication plant. In the methodology proposed, the fuzzy back-propagation network (FBPN) approach for job cycle time estimation is modified with the proportional adjustment approach to estimate the remaining cycle time instead. Besides, unlike existing cycle time estimation approaches, in the methodology proposed a job is not preclassified but rather post-classified after the estimation error has been generated. For this purpose, a back-propagation network is used as the post-classification algorithm. To evaluate the effectiveness of the methodology proposed, production simulation is used in this study to generate some test data. According to experimental results, the accuracy of estimating the remaining cycle time could be improved by up to 64 per cent with the proposed methodology.

A fuzzy‐neural approach for output projection in a semiconductor fabrication factory

Output projection is a critical task for a semiconductor fabrication factory. There are two steps in output projection: predicting the output time for every job in the factory, and projecting the outputs into each period. For enhancing the effectiveness of output projection, a fuzzy‐neural approach is proposed in this study. Firstly, a fuzzy back propagation network (FBPN) is applied to generate fuzzy‐valued output time forecasts. Then, the fuzzy output projection function is proposed to project the outputs into each period. To evaluate the effectiveness of the proposed methodology and to make comparison with some traditional approaches, production simulation is also applied in this study to generate test data.

Drill wear prediction using different neural network architectures

In the present work, an attempt has been made to use different artificial neural network (ANN) architectures to achieve more accurate prediction of drill wear. Large numbers of drilling operations, using mild steel as the work-piece and high speed steel (HSS) as the drill, have been performed and drill flank wear has been measured intermittently. Experimental results show a strong dependency of direct and indirect process parameters with drill wear. Experimentally obtained data have been used to train different ANN architectures using different combinations of important process parameters as input and measured flank wear as the output of the network. Relative performances of different ANN based drill wear prediction schemes in regard to prediction of drill wear have been compared. From the present work it has been observed that inclusions of more sensor signals as input to the network results a better-trained network, which can predict wear more accurately. It has also been observed from the present work that standard back propagation neural network (BPNN) predicts wear more accurately compared to fuzzy back propagation network (FBPN) and self-organizing method (SOM), through BPNN is slow in convergence.

A fuzzy back propagation network ensemble with example classification for lot output time prediction in a wafer fab

Lot output time prediction is a critical task to a wafer fabrication plant (wafer fab). To further enhance the accuracy of wafer lot output time prediction, the concept of clustering is applied to Chen’s fuzzy back propagation network (FBPN) approach in this study by pre-classifying wafer lots before predicting their output times with several FBPNs that have the same topology. Each wafer lot category has a corresponding FBPN that is applied to predict the output times of all lots belonging to the category. In choosing the learning examples of each category, whether a wafer lot can be unambiguously classified or not and the accuracy of predicting the output time of the lot are simultaneously taken into account. To validate the effectiveness of the proposed methodology and to make comparison with some existing approaches, the actual data in a wafer fab were collected. According to experimental results, the prediction accuracy of the proposed methodology was significantly better than those of some existing approaches in most cases by achieving a 19–52% (and an average of 38%) reduction in the root-mean-square-error (RMSE). On the other hand, compared with the fuzzy c-means (FCM)-BPN-ensemble approach, the performance of the proposed methodology in the efficiency respect was indeed improved.

A fuzzy-neural approach for estimating the monthly output of a semiconductor manufacturing factory

Estimating the monthly output of each product type in a semiconductor manufacturing factory is a critical task for the plant. The future revenues and/or profits of the factory can be precisely evaluated only after the monthly outputs over the future months are accurately estimated. There are two steps in estimating the monthly output: predicting the completion time for every job in the factory, and then projecting the outputs into each future month. For enhancing the effectiveness of estimating the monthly output, a fuzzy-neural approach is proposed in this study. Firstly, the look-ahead self-organization map fuzzy back-propagation network (SOM-FBPN) approach is applied to predict the completion time for every job in the factory. Then, the fuzzy monthly output function (FMOF) is proposed to project the outputs into each future month. The job completion time forecast generated by the look-ahead SOM-FBPN approach is an asymmetric triangular fuzzy number (TFN), and is skewed to the side that contains the actual value. Such a property was shown to theoretically contribute to the advantage of the proposed FMOF function over a crisp or probabilistic function. Experimental results also confirmed the effectiveness of the proposed methodology in predicting the monthly output and in estimating the monthly revenues and profits.

Predicting Wafer-Lot Output Time With a Hybrid FCM–FBPN Approach

Output-time prediction is a critical task to a wafer fab. To further enhance the accuracy of wafer-lot output-time prediction, the concept of input classification is applied to Chen's fuzzy backpropagation network (FBPN) approach in this paper by preclassifying wafer lots with the fuzzy c-means (FCM) classifier before predicting the output times. In this way, similar wafer lots are clustered in the same category. The data of wafer lots of different categories are then learned with different FBPNs but with the same topology. After learning, these FBPNs form an FBPN ensemble that can be applied in predicting the output time of a new lot. The output of the FBPN ensemble determines the cycle/output time forecast and is obtained by aggregating the outputs of the component FBPNs. Production simulation is applied in this paper to generate test data. According to experimental results, the prediction accuracy of the hybrid FCM-FBPN approach was significantly better than those of many existing approaches.

An intelligent hybrid system for wafer lot output time prediction

Lot output time prediction is a critical task to a wafer fab (fabrication plant). To further enhance the accuracy of wafer lot output time prediction, an intelligent hybrid system is constructed in this study. Firstly, the concept of input classification is applied to Chen’s fuzzy back propagation network (FBPN) approach in this study by pre-classifying wafer lots with the k-means (kM) classifier before predicting the output times with FBPN. Examples belonging to different categories are then learned with different FBPNs but with the same topology. Secondly, the future release plan of the fab, which is influential but has been ignored in traditional approaches, is also incorporated in the intelligent hybrid system. To evaluate the effectiveness of the proposed methodology, production simulation has been applied in this study to generate test examples. According to experimental results, the prediction accuracy of the intelligent hybrid system was significantly better than those of six approaches: BPN, case-based reasoning (CBR), FBPN, look-ahead FBPN, evolving fuzzy rules (EFR), and kM-FBPN without look-ahead in most cases by achieving a 17–47% (and an average of 34%) reduction in the root-mean-squared-error (RMSE) over the comparison basis – BPN.

Short term electric load prediction using Fuzzy BP

The privatization of electricity industry in various parts of the world has increased the significance of the load prediction problem and in particular there is a need to understand and predict the demand for power with greater accuracy, even in case of imprecise input data. Prediction of power demand is essential for an efficient operation of any utility company. In this paper, a fuzzy version of neural network, namely Fuzzy back propagation network (Fuzzy BP) has been developed for short-term electric load prediction. The load is predicted using fuzzy back propagation algorithm. This model is capable of handling imprecise information in input data. The proposed architecture consists of a module with 51 inputs and 24 outputs. The inputs are fuzzified and the outputs are crisp values representing the predicted load. The proposed method is implemented in MATLAB. The simulation results are presented for each day (24 hours) of the week. Besides this, a multi layer perceptron (MLP) was also implemented separately and the load was predicted using back propagation algorithm. The results obtained from Fuzzy BP were found to be satisfactory when compared to those of MLP network.

A Hybrid SOM-BPN Approach to Lot Output Time Prediction in a Wafer Fab

Output time prediction is a critical task to a wafer fab (fabrication plant). To further enhance the accuracy of wafer lot output time prediction, the concept of input classification is applied to Chen's fuzzy back propagation network (FBPN) in this study by pre-classifying input examples with the self-organization map (SOM) classifier before they are fed into the FBPN. Examples belonging to different categories are then learned with the same FBPN but with different parameter values. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the proposed methodology was significantly better than those of two existing approaches, FBPN without example classification, and evolving fuzzy rules (EFR), in most cases by achieving a 15%-45% (and an average of 31%) reduction in the root-mean-squared-error (RMSE).

Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry

Reliable prediction of sales can improve the quality of business strategy. In this research, fuzzy logic and artificial neural network are integrated into the fuzzy back-propagation network (FBPN) for sales forecasting in Printed Circuit Board (PCB) industry. The fuzzy back propagation network is constructed to incorporate production-control expert judgments in enhancing the model's performance. Parameters chosen as inputs to the FBPN are no longer considered as of equal importance, but some sales managers and production control experts are requested to express their opinions about the importance of each input parameter in predicting the sales with linguistic terms, which can be converted into pre-specified fuzzy numbers. The proposed system is evaluated through the real world data provided by a printed circuit board company and experimental results indicate that the Fuzzy back-propagation approach outperforms other three different forecasting models in MAPE measures.

A fuzzy back propagation network for output time prediction in a wafer fab

Output time prediction is a critical task to a wafer fab. Three major techniques commonly applied to predict the output time of a wafer lot include multiple-factor linear regression, production simulation, and back propagation networks (BPNs). Among them, a back propagation network has been shown to be the most promising approach for practical applications, considering both prediction accuracy and efficiency. In this study, a fuzzy back propagation network (FBPN) is constructed to incorporate production-control expert judgments in enhancing the performance of an existing crisp back propagation network. Parameters chosen as inputs to the FBPN are no longer considered as of equal importance, but some production-control experts are requested to express their opinions about the importance of each input parameter in predicting the output time with linguistic terms, which can be converted into pre-specified fuzzy numbers, aggregated, and then multiplied to the normalized value of the corresponding input parameter when fed into the FBPN. Subsequently, the arithmetic for triangular fuzzy numbers is applied to deal with all calculations involved in network learning. A defuzzification operation is finally performed to obtain a crisp output time forecast to enhance the practicability of the forecast. A fab production simulator is applied to generate test examples. The results of five test cases showed the FBPN outperformed the crisp BPN in the efficiency respect, including starting with a much smaller initial root mean square error (RMSE) and requiring much fewer epochs for convergence. In the respect of prediction accuracy measured with the minimal RMSE, the performance of the FBPN was slightly better than that of the BPN. In addition, our experiments also showed that it is possible to significantly reduce the RMSE if the fuzzy-valued output is treated as a weighted interval forecast for the actual cycle time.