Seite - 194 - in Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources

Bibliography single-input fuzzy logic. Fuzzy Sets and Systems, 129(3):335–342,2002. [CZ05] LA Campanone and NE Zaritzky. Mathematical analysis of microwave heating process. Journal of FoodEngineering, 69(3):359–368,2005. [DAF90] AAP De Alwis and PJ Fryer. The use of direct re- sistanceheating in the foodindustry. Journal ofFood Engineering, 11(1):3–27,1990. [DAJ02] Kalyanmoy Deb, Ashish Anand, and Dhiraj Joshi. A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary com- putation, 10(4):371–395,2002. [Dat01] AshimKDatta. Handbookofmicrowave technology for foodapplication. CRCPress,2001. [DC03] Danielle Dougherty and Doug Cooper. A practical multiple model adaptive strategy for multivariable model predictive control. Control Engineering Prac- tice, 11(6):649–664,2003. [dFNGD00] Joa˜o FG de Freitas, Mahesan Niranjan, Andrew H. Gee, and Arnaud Doucet. Sequential Monte Carlo methods to train neural network models. Neural computation, 12(4):955–993,2000. [Dib95] David Dibben. Numerical and experimental modelling of microwave applicators. PhD thesis, University of Cambridge,1995. [Die98] Thomas G Dietterich. The MAXQ method for hi- erarchical reinforcement learning. In ICML, pages 118–126.Citeseer,1998. [Die99] Thomas G Dietterich. Hierarchical reinforcement learning with the MAXQ value function decompo- sition. arXivpreprint cs/9905014, 1999. [DKS+95] James Dougherty, Ron Kohavi, Mehran Sahami, etal. Supervisedandunsuperviseddiscretizationof continuous features. In ICML,pages194–202,1995. 194