@article{lu_fang_2001, title={Solving nonlinear optimization problems with fuzzy relation equation constraints}, volume={119}, ISSN={["0165-0114"]}, DOI={10.1016/S0165-0114(98)00471-0}, abstractNote={An optimization model with a nonlinear objective function subject to a system of fuzzy relation equations is presented. Since the solution set of the fuzzy relation equations is in general a non-convex set, when it is not empty, conventional nonlinear programming methods are not ideal for solving such a problem. In this paper, a genetic algorithm (GA) is proposed. This GA is designed to be domain specific by taking advantage of the structure of the solution set of fuzzy relation equations. The individuals from the initial population are chosen from the feasible solution set and are kept within the feasible region during the mutation and crossover operations. The construction of test problems is also developed to evaluate the performance of the proposed algorithm.}, number={1}, journal={FUZZY SETS AND SYSTEMS}, author={Lu, JJ and Fang, SC}, year={2001}, month={Apr}, pages={1–20} }
 @article{lu_brinkley_fang_1997, title={A fuzzy expert system model for RF receiver module testing}, volume={28}, DOI={10.1080/00207729708929439}, abstractNote={Abstract An expert system for PCS-1900 radio frequency module test failure diagnostics that incorporates fuzzy logic has been implemented in the Wireless Networks manufacturing facility of Nortel in Research Triangle Park, North Carolina. Since the relations between the results of parametric test measurement and failure modes are neither certain nor definite, it is advantageous to employ fuzzy logic in building a knowledge base system. Instead of providing a definite conclusion that specifies a specific failure mode, this system gives a degree of failure for each mode, indicating the possibility of that mode being faulty.}, number={8}, journal={International Journal of Systems Science}, author={Lu, J.-J. and Brinkley, P. and Fang, Shu-Cherng}, year={1997}, pages={791–798} }