Chen-Hao Liu

A TSK type fuzzy rule based system for stock price prediction

In this paper, a Takagi-Sugeno-Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction. The TSK fuzzy model applies the technical index as the input variables and the consequent part is a linear combination of the input variables. The fuzzy rule based model is tested on the Taiwan Electronic Shares from the Taiwan Stock Exchange (TSE). Through the intensive experimental tests, the model has successfully forecasted the price variation for stocks from different sectors with accuracy close to 97.6% in TSE index and 98.08% in MediaTek. The results are very encouraging and can be implemented in a real-time trading system for stock price prediction during the trading period.

Sub-population genetic algorithm with mining gene structures for multiobjective flowshop scheduling problems

According to previous research of Chang et al. [Chang, P. C., Chen, S. H., & Lin, K. L. (2005b). Two phase sub-population genetic algorithm for parallel machine scheduling problem. Expert Systems with Applications, 29(3), 705-712], the sub-population genetic algorithm (SPGA) is effective in solving multiobjective scheduling problems. Based on the pioneer efforts, this research proposes a mining gene structure technique integrated with the SPGA. The mining problem of elite chromosomes is formulated as a linear assignment problem and a greedy heuristic using threshold to eliminate redundant information. As a result, artificial chromosomes are created according to this gene mining procedure and these artificial chromosomes will be reintroduced into the evolution process to improve the efficiency and solution quality of the procedure. In addition, to further increase the quality of the artificial chromosome, a dynamic threshold procedure is developed and the flowshop scheduling problems are applied as a benchmark problem for testing the developed algorithm. Extensive tests in the flow-shop scheduling problem show that the proposed approach can improve the performance of SPGA significantly.

A hybrid model by clustering and evolving fuzzy rules for sales decision supports in printed circuit board industry

This research develops a hybrid model by integrating Self Organization Map (SOM) neural network, Genetic Algorithms (GA) and Fuzzy Rule Base (FRB) to forecast the future sales of a printed circuit board factory. This hybrid model encompasses two novel concepts: (1) clustering an FRB into different clusters, thus the interaction between fuzzy rules is reduced and a more accurate prediction model can be established, and (2) evolving an FRB by optimizing the number of fuzzy terms of the input and output variables, thus the prediction accuracy of the FRB is further improved. Numerical data of various affecting factors and actual demand of the past 5 years of the printed circuit board (PCB) factory are collected and inputted into the hybrid model for future monthly sales forecasting. Experimental results show the effectiveness of the hybrid model when comparing it with other approaches. However, the theoretical development of the validity of clustering an FRB into sub clusters remains to be proven.

A fuzzy case-based reasoning model for sales forecasting in print circuit board industries

Reliable prediction of sales can improve the quality of business strategy. Case-based reasoning (CBR), one of the well known artificial intelligence (AI) techniques, has already proven its effectiveness in numerous studies. However, due to the uncertainties in knowledge representation, attribute description, and similarity measures in CBR, it is very difficult to find the similar cases from case bases. In order to deal with this problem, fuzzy theories have been incorporated into CBR allowing for more flexible and accurate models. This research develops a fuzzy case-based reasoning (FCBR) and explores its potential use in supporting a forecaster during the forecast process for forecasting the future sales of a printed circuit board factory. Numerical data of various affecting factors and actual demand of the past 5 years of the printed circuit board (PCB) factory are collected and input into the FCBR for future monthly sales forecasting. Experimental results show the effectiveness of the FCBR model when comparing it with other approaches.

The development of a weighted evolving fuzzy neural network for PCB sales forecasting

Abstract This research develops a weighted evolving fuzzy neural network for PCB sales forecasting and it includes four major steps: first of all, collecting 15 factors among macroeconomic data, downstream production demand and total industrial production outputs and then using the Grey Relation Analysis (GRA) to select a combination of key factors which have the greatest influence on PCB sales. Secondly, taking the time serial factor into consideration, the Winter’s Exponential Smoothing method is applied to predict the tendency of PCB sales and the influences of seasonal effects. Thirdly, applying historical data to proceed the training of Weighted Evolving Fuzzy Neural Network (WEFuNN) and then forecasts the future PCB sales by the WEFuNN. Finally, compare three kinds of performance measurements for each model. The experimental results reveal that the MAPE for WEFuNN model is 2.11% which is the best compared to others. In summary, the WEFuNN model can be applied practically as a sales forecasting tool in the PCB industry.

New operators for faster convergence and better solution quality in modified genetic algorithm

The aim of this paper is to study two new forms of genetic operators: duplication and fabrication. Duplication is a reproduce procedure that will reproduce the best fit chromosome from the elite base. The introduction of duplication operator into the modified GA will speed up the convergence rate of the algorithm however the trap into local optimality can be avoided. Fabrication is an artificial procedure used to produce one or several chromosomes by mining gene structures from the elite chromosome base. Statistical inference by job assignment procedure will be applied to produce artificial chromosomes and these artificial chromosomes provides new search directions and new solution spaces for the modified GA to explore. As a result, better solution quality can be achieved when applying this modified GA. Different set of problems will be tested using modified GA by including these two new operators in the procedure. Experimental results show that the new operators are very informative in searching the state space for higher quality of solutions.

Combining SOM and GA-CBR for flow time prediction in semiconductor manufacturing factory

Flow time of semiconductor manufacturing factory is highly related to the shop floor status; however, the processes are highly complicated and involve more than hundred of production steps. Therefore, a simulation model with the production process of a real wafer fab located in Hsin-Chu Science-based Park of Taiwan is built. In this research, a hybrid approach by combining Self-Organizing Map (SOM) and Case-Based Reasoning (CBR) for flow time prediction in semiconductor manufacturing factory is proposed. And Genetic Algorithm (GA) is applied to fine-tune the weights of features in the CBR model. The flow time and related shop floor status are collected and fed into the SOM for classification. Then, corresponding GA-CBR is selected and applied for flow time prediction. Finally, using the simulated data, the effectiveness of the proposed method (SGA-CBR) is shown by comparing with other approaches.

Fuzzy back-propagation network for PCB sales forecasting

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 printed circuit board industry. The fuzzy back propagation network is constructed to incorporate production-control expert judgments in enhancing the models 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, aggregated and corresponding input parameters when fed into the FBPN. The proposed system is evaluated through the real life data provided by a printed circuit board company. Model evaluation results for research indicate that the Fuzzy back-propagation outperforms the other three different forecasting models in MAPE.

A case-based seat allocation system for airline revenue management

Airline companies usually implement revenue management to increase profits. The revenue management can be performed through seat inventory management. The current system to book seats is first-come first-served. This approach tends to sell low-price seats because low-price requests often appear earlier. It also results in low revenue. In this paper, an expected dynamic probability method and a case-based seat allocation system are proposed to enhance the performance of the seat inventory management. Extensive studies are conducted to compare the performance of first-come first-served method, expected dynamic probability method, and case-based decision support system. The result indicates that the case-based seat allocation system outperforms the other methods.

Evolving case-based reasoning with genetic algorithm in wholesaler's returning book forecasting

In this paper, a hybrid system is developed by evolving Case-Based Reasoning (CBR) with Genetic Algorithm (GA) for reverse sales forecasting of returning books. CBR systems have been successfully applied in several domains of artificial intelligence. However, in conventional CBR method each factor has the same weight which means each one has the same influence on the output data that does not reflect the practical situation. In order to enhance the efficiency and capability of forecasting in CBR systems, we applied the GAs method to adjust the weights of factors in CBR systems, GA/CBR for short. The case base of this research is acquired from a book wholesaler in Taiwan, and it is applied by GA/CBR to forecast returning books. The result of the prediction of GA/CBR was compared with other traditional methods.