Wen-Hsien Ho

Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm

In this paper, an adaptive network-based fuzzy inference system (ANFIS) with the genetic learning algorithm is used to predict the workpiece surface roughness for the end milling process. The hybrid Taguchi-genetic learning algorithm (HTGLA) is applied in the ANFIS to determine the most suitable membership functions and to simultaneously find the optimal premise and consequent parameters by directly minimizing the root-mean-squared-error performance criterion. Experimental results show that the HTGLA-based ANFIS approach outperforms the ANFIS methods given in the Matlab toolbox and reported recently in the literature in terms of prediction accuracy.

Improved immune algorithm for global numerical optimization and job-shop scheduling problems

In this paper, by using the unified procedures, an improved immune algorithm named a modified Taguchi-immune algorithm (MTIA), based on both the features of an artificial immune system and the systematic reasoning ability of the Taguchi method, is proposed to solve both the global numerical optimization problems with continuous variables and the combinatorial optimization problems for the job-shop scheduling problems (JSP). The MTIA combines the artificial immune algorithm, which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimal antibody. In the MTIA, the clonal proliferation within hypermutation for several antibody diversifications and the recombination by using the Taguchi method for the local search are integrated to improve the capabilities of exploration and exploitation. The systematic reasoning ability of the Taguchi method is executed in the recombination operations to select the better antibody genes to achieve the potential recombination, and consequently enhance the MTIA. The proposed MTIA is effectively applied to solve 15 benchmark problems of global optimization with 30 or 100 dimensions. The computational experiments show that the proposed MTIA can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing improved genetic algorithms reported recently in the literature. In addition, the MTIA is also applied to solve the famous Fisher-Thompson and Lawrence benchmarks of the JSP. The computational experiments show that the proposed MTIA approach can also obtain both better and more robust results than those evolutionary methods reported recently.

Comparison of Artificial Neural Network and Logistic Regression Models for Predicting In-Hospital Mortality after Primary Liver Cancer Surgery

Background Since most published articles comparing the performance of artificial neural network (ANN) models and logistic regression (LR) models for predicting hepatocellular carcinoma (HCC) outcomes used only a single dataset, the essential issue of internal validity (reproducibility) of the models has not been addressed. The study purposes to validate the use of ANN model for predicting in-hospital mortality in HCC surgery patients in Taiwan and to compare the predictive accuracy of ANN with that of LR model. Methodology/Principal Findings Patients who underwent a HCC surgery during the period from 1998 to 2009 were included in the study. This study retrospectively compared 1,000 pairs of LR and ANN models based on initial clinical data for 22,926 HCC surgery patients. For each pair of ANN and LR models, the area under the receiver operating characteristic (AUROC) curves, Hosmer-Lemeshow (H-L) statistics and accuracy rate were calculated and compared using paired T-tests. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and the relative importance of variables. Compared to the LR models, the ANN models had a better accuracy rate in 97.28% of cases, a better H-L statistic in 41.18% of cases, and a better AUROC curve in 84.67% of cases. Surgeon volume was the most influential (sensitive) parameter affecting in-hospital mortality followed by age and lengths of stay. Conclusions/Significance In comparison with the conventional LR model, the ANN model in the study was more accurate in predicting in-hospital mortality and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

Background A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection.

Robust Quadratic-Optimal Control of TS-Fuzzy-Model-Based Dynamic Systems With Both Elemental Parametric Uncertainties and Norm-Bounded Approximation Error

This paper considers the design problem of the robust quadratic-optimal parallel-distributed-compensation (PDC) controllers for Takagi-Sugeno (TS) fuzzy-model-based control systems with both elemental parametric uncertainties and norm-bounded approximation error. By complementarily fusing the robust stabilizability condition, the orthogonal functions approach (OFA), and the hybrid Taguchi genetic algorithm (HTGA), an integrative method is presented in this paper to design the robust quadratic-optimal PDC controllers such that 1) the uncertain TS-fuzzy-model-based control systems can be robustly stabilized, and 2) a quadratic integral performance index for the nominal TS-fuzzy-model-based control systems can be minimized. In this paper, the robust stabilizability condition is proposed in terms of linear matrix inequalities (LMIs). By using the OFA and the LMI-based robust stabilizability condition, the robust quadratic-optimal PDC control problem for the uncertain TS-fuzzy-model-based dynamic systems is transformed into a static constrained-optimization problem represented by the algebraic equations with constraint of LMI-based robust stabilizability condition, thus greatly simplifying the robust optimal PDC control design problem. Then, for the static constrained-optimization problem, the HTGA is employed to find the robust quadratic-optimal PDC controllers of the uncertain TS-fuzzy-model-based control systems. Two design examples of the robust quadratic-optimal PDC controllers for an uncertain inverted pendulum system and an uncertain nonlinear mass-spring-damper mechanical system are given to demonstrate the applicability of the proposed integrative approach.

Process Parameters Optimization: A Design Study for TiO

This paper proposes a procedure for process parameters design by combining both modeling and optimization methods. The proposed procedure integrates the Taguchi method, the artificial neural network (ANN), and the genetic algorithm (GA). First, the Taguchi method is applied to minimize experimental numbers and to collect experimental data representing the quality performances of a system. Next, the ANN is used to build a system model based on the data from the Taguchi experimental method. Then, the GA is employed to search for the optimal process parameters. A process parameters design for a titanium dioxide (TiO2) thin film in the vacuum sputtering process is studied in this paper. The quality objective is to form a smaller water contact angle on the TiO2 thin-film surface. The water contact angle is 4° obtained from the system model of the proposed procedure. The process parameters obtained from the proposed procedure were used to conduct the experiment in the vacuum sputtering process for the TiO2 thin film. The water contact angle given from the practical experiment is 3.93°. The difference percent is 1.75% between 4° and 3.93°. The result obtained from the system model of the proposed procedure is promising. Hence, we can conclude that the proposed procedure is a very good approach in solving the problem of the process parameters design.

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The robust controllability problem for the Takagi-Sugeno (T-S) fuzzy-model-based control systems is studied in this paper. Under the assumption that the nominal T-S fuzzy-model-based control systems are locally controllable (i.e., each fuzzy rule of the nominal T-S fuzzy-model-based control systems has a full row rank for its controllability matrix), a sufficient condition is proposed to preserve the assumed property when the parameter uncertainties are added into the nominal T-S fuzzy-model-based control systems. The proposed sufficient condition can provide the explicit relationship of the bounds on parameter uncertainties to preserve the assumed property. Besides, a robustly global controllability condition and the related robustly global stabilizability condition of the uncertain T-S fuzzy-model-based control systems are also presented in this paper. A nonlinear mass-spring-damper mechanical system with parameter uncertainties is given as an example to illustrate the application of the proposed sufficient conditions.

Thin Film of Vacuum Sputtering Process

By integrating the orthogonal-functions approach (OFA), the hybrid Taguchi-genetic algorithm (HTGA) and a robust stabilizability condition, an integrative method is presented in this paper to design the robust-optimal active vibration controller such that (i) the flexible mechanical system with elemental parametric uncertainties can be robustly stabilized, and (ii) a quadratic finite-horizon integral performance index for the nominal flexible mechanical system can be minimized. The robust stabilizability condition is proposed in terms of linear matrix inequalities (LMIs). Based on the OFA, an algebraic algorithm only involving the algebraic computation is derived for solving the nominal flexible mechanical feedback dynamic equations. By using the OFA and the LMI-based robust stabilizability condition, the robust-finite-horizon-optimal active vibration control problem for the uncertain flexible mechanical dynamic systems is transformed into a static constrained-optimization problem represented by the algebraic equations with constraint of LMI-based robust stabilizability condition; thus greatly simplifying the robustoptimal active vibration control design problem. Then, for the static constrained-optimization problem, the HTGA is employed to find the robust-optimal active vibration controllers of the uncertain flexible mechanical systems. Two design examples are given to demonstrate the applicability of the proposed integrative approach.

Robust Controllability of T–S Fuzzy-Model-Based Control Systems With Parametric Uncertainties

Research highlights? We predict the platelet transfusion requirements for the acute myeloblastic leukemia (AML) patients via an artificial neural network (ANN). ? The hybrid Taguchi-genetic algorithm (HTGA) is applied in this ANN to find the optimal weights of links and biases. ? The HTGA-based ANN can be the mechanism of the decision support systems and may outperform the ANN with backpropagation algorithm given in the Matlab toolbox in terms of prediction accuracy. In this paper, an artificial neural network (ANN) model with the genetic algorithm (GA) is used to predict the platelet transfusion requirements for the acute myeloblastic leukemia (AML) patients. The hybrid Taguchi-genetic algorithm (HTGA) is applied in this ANN to find the optimal parameters (i.e., weights of links and biases govern the input-output relationship of an ANN) by directly maximizing the training accuracy performance criterion. Experimental results show that the HTGA-based ANN model outperforms the ANN model with backpropagation algorithm given in the Matlab toolbox in terms of prediction accuracy. Therefore, this study demonstrated the feasibility of applying the HTGA-based ANN as the mechanism of the decision support systems for the platelet transfusion requirements of the AML patients based on clinical databases.

Robust-optimal active vibration controllers design of flexible mechanical systems via orthogonal function approach and genetic algorithm

In this paper, a model based on the adaptive network-based fuzzy inference system (ANFIS) with the improved genetic algorithm is used to predict the adequacy of vancomycin regimen. The improved genetic algorithm, i.e., hybrid Taguchi-genetic algorithm (HTGA), is applied in the ANFIS to simultaneously find the optimal premise and consequent parameters and a total output layer parameter by directly maximizing the training accuracy performance criterion. Experimental results show that the HTGA-based ANFIS model outperforms the logistic regression model in terms of prediction accuracy. Therefore, this study demonstrates the feasibility of applying the HTGA-based ANFIS as the mechanism of the decision support systems for the adequacy of vancomycin regimen for the patients based on clinical databases.