S. Krovvidy and W.G. Wee, "Wastewater treatment system from case-based reasoning," Machine Learning, vol. 10, no. 3, pp. 341--363, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....the resulting systems more reliable and powerful in coping with real world environmental systems. Among the AI methods often used in the development of EDSS in past years, the following are worth noting: Rule Based Reasoning [23, 26, 47, 48] Planning [44, 49 51] Case Based Reasoning [20, 45, 52, 53]. Qualitative Reasoning [54 57] Constraint Satisfaction [44, 58, 59] Model Based Reasoning [60 63] Connexionist Reasoning [64 67] Evolutionary Computing [68 71] Fuzzy Logic Techniques [72 75] 4. Data Interpretation and Data Mining Data Interpretation has been a ....

....models and by limited empirical data. Accurate prediction of the behaviour of such systems requires exploitation of multiple, individually incomplete, knowledge sources. The application of multiple complementary problem solving techniques (i.e. Case Based Reasoning and Constraint Satisfaction) [53, 59, 89], can often help to reduce this uncertainty. In Artificial Intelligence, this situation is often referred to as having an ill structured domain [10] The relationships among the concepts or attributes of the domain are not well enough known and there is not much agreement among the experts. The ....

S. Krovvidy and W.G. Wee, "Wastewater treatment system from case-based reasoning," Machine Learning, vol. 10, no. 3, pp. 341--363, 1993.

....appear at daily operation of the plant. These features make it easier to understand the considerable research effort aroused in Artificial Intelligence during last years focused on diagnosis, design, decision aid, process optimization, and more recently, on process control and supervision of WWTP [10 21]. 1.3. Knowledge based systems This is a case in which a knowledge based system may be useful, since it allows diagnosis and management of the process, both in what might be described as normal conditions, and when these conditions are altered by disturbances that a classical control system is ....

S. Krovvidy and W.G. Wee. "Wastewater Treatment Systems from Case-Based Reasoning". Machine Learning 10, pp. 341-363, 1993.

.... 1993) to the performance of the underlying system (Koton, 1988b; Golding and Rosenbloom, 1991; Hanks and Weld, 1992; F eret and Glasgow, 1993) to the performance of other systems (Bradtke and Lehnert, 1988a; Bradtke and Lehnert, 1988b; Bareiss, 1989b; Hanks and Weld, 1992; Cardie, 1993c; Krovvidy and Wee, 1993; Skalak, 1993) or to evaluate the system s performance before and after learning in novel environments (Ram and Santamar ia, 1993) It should be noted that even in many of these cases the evaluation is only limited. ffl Presented systems have only a small case base and thus the problem of ....

Krovvidy, S. and Wee, W. G. (1993). Wastewater treatment systems from case-based reasoning.

....Perceptrons. However, Radial Basis Functions are potentially useful in hybrid systems because of their fast learning capability. Integrating Neural and Case Based Problem Solving The integration of ANNs with CBR, has been investigated by a relatively small number of researchers, for example Krovvidy et al. 1993) and Kock (1996) CBR techniques are effective in remembering specific cases, that may not be generalisable, whereas ANNs are effective in generalising information, extracting rules and clustering data. The integration of neural network and cased based problem solving may be achieved either ....

Krovvidy, S. and Wee. W. C., Wastewater treatment systems from case-based reasoning. Machine Learning, 10:341-363, 1993.

....exceptions to this practice. In some cases, new techniques or modifications of existing techniques were developed to expand the applicability of ML techniques (e.g. for learning synthesis knowledge in Bridger [78] for architectural design in FABEL [9] or for monitoring water treatment plants [43, 83]) In other cases, several methods and creative knowledge representations were used to address different variations of learning problems (e.g. modeling material stress strain relations [32] While addressing increasingly complex problems, the necessity to integrate several ML techniques for ....

....significantly. There have been studies on knowledge extraction (e.g. feasibility of wind bracing [6] and environmental impact assessment [38] studies solving complete problems in which learning played a major role (e.g. cable stayed bridge design [68] and monitoring water treatment plants [83, 43]) and studies that employed learning as part of their operation (e.g. steel bridge design [1] highway truck load monitoring [29] transmission line towers design [60] and architectural design [9] In addition, there have been studies directed at information modeling for creating estimation ....

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Krovvidy, S., and Wee, W. G. Wastewater treatment systems from case-based reasoning. Machine Learning 10, 3 (1993), 341--363.

....networks. Hamlet (Borrajo Veloso, 1997) learns to improve its search efficiency and resulting plan quality by incrementally refining its control rules using a CBL approach. Meta AQUA (Cox Ram, 1994) uses explicit learning goals to select strategies for recovering from planning failures. Krovvidy and Wee s (1993) CBL system saves partial planning solutions and learns heuristics for selecting them during problem solving. Knowledge on the adaptability of retrieved cases can also be used to successfully bias retrieval behavior (Smyth Keane, 1995a) 2.3 Learning adaptation knowledge Learned adaptation ....

Krovvidy, S., & Wee, W. G. (1993). Wastewater treatment systems from case-based reasoning. Machine Learning, 10, 341--363.

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