Machine learning strategies for control of flotation plants

Control Engineering Practice

Volumn 5, Issue 2, 1997, Pages 263-269

  Aldrich, C ^a   Moolman, D W ^a   Gouws, F S ^a   Schmitz, G P J ^a  

^a STELLENBOSCH UNIVERSITY   (South Africa)

Author keywords

artificial intelligence; chemical industry; inductive machine learning; intelligent control; neural nets

Indexed keywords

FLOTATION PLANTS; INDUCTIVE MACHINE LEARNING; PROBABILISTIC DECISION TREE METHODS;

BACKPROPAGATION; CHEMICAL INDUSTRY; COMPUTER VISION; DECISION THEORY; INTELLIGENT CONTROL; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; NEURAL NETWORKS;

ARTIFICIAL INTELLIGENCE;

EID: 0031079249     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0967-0661(97)00235-9     Document Type: Conference Paper

References (11)

1. Ben-David, A. and J. Mandel (1995). Classification accuracy: machine learning vs. expert knowledge acquisition. Machine Learning, 18, 109-114
2. Breiman, L., J. Friedman, R. Olshen and C. Stone (1984). Classification and Regression Trees, Wadsworth, Belmont, California.
3. Johannsen, G. and J.L. Alty (1991). Knowledge engineering for industrial expert systems. Automatica, 27, 97-114.
4. Lippmann, R.P. (1987). An introduction to computing with neural nets and data parallel algorithms for massively parallel computing. IEEE ASSP Mag., 35, 4-21.
5. Moolman, D.W., C. Aldrich, J.S.J. Van Deventer and D.J. Bradshaw (1995). The interpretation of flotation froth surfaces by using digital image analysis and neural networks. Chemical Engineering Science, 50(22), 3501-3513.
6. Murthy, K.S.M. (1996). On growing better decision trees from data. PhD Dissertion, John Hopkins University, Baltimore, Maryland.
7. Quinlan, J.R. (1988). Decision trees and multi-valued attributes. In: Machine Intelligence (Hayes, J.E., Michie, D. and Richards, J. (Ed.)),
8. Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, California.
9. Rumelhart, D.E., G.E. Hinton and R.J. Williams (1986). Learning internal representations by error propagation. Parallel Distributed Processing., 1, 318-362.
10. Sammon, J.W. (1969). A non-linear mapping for data structure analysis. IEEE Transactions on Computers, C-18, 401-109.
11. Shavlik, J.W., R.J. Mooney and G.G. Towell (1991). Symbolic and neural learning algorithms: An experimental comparison. Machine Learning, 6, 111-143.