By Jānis Barzdinš (auth.), Setsuo Arikawa, Klaus P. Jantke (eds.)
This quantity provides the lawsuits of the Fourth overseas Workshop on Analogical and Inductive Inference (AII '94) and the 5th overseas Workshop on Algorithmic studying thought (ALT '94), held together at Reinhardsbrunn fortress, Germany in October 1994. (In destiny the AII and ALT workshops might be amalgamated and held lower than the only name of Algorithmic studying Theory.)
The ebook includes revised models of forty five papers on all present features of computational studying concept; particularly, algorithmic studying, computing device studying, analogical inference, inductive good judgment, case-based reasoning, and formal language studying are addressed.
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Additional info for Algorithmic Learning Theory: 4th International Workshop on Analogical and Inductive Inference, AII '94 5th International Workshop on Algorithmic Learning Theory, ALT '94 Reinhardsbrunn Castle, Germany October 10–15, 1994 Proceedings
Neural Networks in Process Engineering 50 100 150 200 time (h) 250 Figure 3. Identification data Set 1: RUS inputs 300 350 33 RBFN Identification of an Industrial Polymerization Reactor Model G 150 200 250 time (h) Figure 4. Identification data Set 2: RDS inputs 300 350 Neural Networks in Process Engineering 150 200 time (h) Figure 5. Validation data Set 3: RUS inputs. RBFN Identification of an Industrial Polymerization Reactor Model 35 Table 4. 07 The identification results for data in Sets 1 and 2 using stepwise regression with varying values for the width parameter /3 are shown in Table 4.
N-butanol/water, iso-butanol/water, cumene/water, toluene/water, kerosine/water and Clairsol 350/water. A range of column sizes is involved. This allowed the construction of a training (74 exemplars) and a test data set (15 exemplars) against which the performance of the network could be measured. e. the drop size was modelled in terms of the interfacial tension of the system, as well as the geometry of the column. The differences in densities and viscosities were ignored for simplicity. The network consisted of an input layer with 6 input nodes (for the six input variables), a sigmoidal hidden layer with 4 hidden nodes, and a single-node sigmoidal output layer (for the output variable, d^)- The network converged rapidly (after approximately 10 000 iterations or less than a minute of training on a 486 DX personal computer or better), and could then be used to predict d32-values based on data not used during training of the network.
Although no physical meaning can be attached to the weights in the network, their collective effect enables the network to accurately predict drop sizes and hold-up in an existing column. Trial and error procedure is required to set up the simplest network possible (to minimize the number of weighting parameters), but this is not a long process. An alternative procedure using a mechanistic model such as that discussed by Cauwenberg et al. (1997) requires much more computational effort and has no potential for learning the consequences of inevitable changes with time often experienced in industrial plants.
Algorithmic Learning Theory: 4th International Workshop on Analogical and Inductive Inference, AII '94 5th International Workshop on Algorithmic Learning Theory, ALT '94 Reinhardsbrunn Castle, Germany October 10–15, 1994 Proceedings by Jānis Barzdinš (auth.), Setsuo Arikawa, Klaus P. Jantke (eds.)