Nonlinear prediction of manufacturing systems through explicit and implicit data mining

• Published in: Computers & industrial engineering Vol. 33; no. 3; pp. 461 - 464
• Main Authors: Kim, Steven H., Lee, Churl Min
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Seoul Elsevier Ltd 01.12.1997; Oxford Pergamon Press; New York, NY Pergamon Press Inc
• Subjects: Applied sciences; Artificial intelligence; chaos; Chaos theory; CIM; Comparative analysis; Computer science; control theory; systems; Connectionism. Neural networks; Control theory. Systems; Data mining; Exact sciences and technology; Learning; prediction; Predictions; Process control. Computer integrated manufacturing; Studies; prediction; learning; chaos; CIM; data mining; Learning; Chaos; Case study; Statistical method; Computer integrated manufacturing; Prediction; Lorenz model; Case based reasoning; Neural network; Knowledge
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/S0360-8352(97)00168-X

Many processes in the industrial realm exhibit stochastic and nonlinear behavior. Consequently, an intelligent system must be able to ndapt to nonlinear production processes as well as probabilistic phenomena. To this end, an intelligent manufacturing system may draw on techniques from disparate fields, involving knowledge in both explicit and implicit form.In order for a knowledge based system to control a manufacturing process, an important capability is that of prediction: forecasting the future trajectory of a process as well as the consequences of the control action. This paper presents a comparative study of explicitaand implicit methods to predict nonlinear chaotic behavior. The evaluated models include statistica; procedures as well as neural networks and case based reasoning. The concepts are crystallized through a case study in the prediction of chaotic processes adulterated by various patterns of noise.