<p>The management area includes a large class of pattern recognition (classification) problems. Traditionally, these problems have been solved by using statistical methods or expert systems. In practice, however, statistical assumptions about the probability distributions of the pattern variables are often not verifiable, and expertise concerning the correct classification is often not explicitly available. These obstacles may make statistical methods and expert system techniques difficult to apply. Since the early 1980s neural network techniques have been widely used in pattern recognition, especially after Rumelhart's back propagation learning algorithm was adapted to the solution of these problems. The standard neural network, using the back propagation learning algorithm, requires no statistical assumptions but uses training sample data to generate classification boundaries, allowing it to perform pattern recognition.</p> <p>In this dissertation the neural network's behavior in classification boundary generation is analyzed. Based on this analysis, three models are developed. The first model improves the classification performance of neural networks in managerial pattern recognition by modifying the training algorithm through the use of monotonicity. Using simulated and real data, the developed model is tested and verified. The second model solves bias problems caused by small sample size in neural network classification results. The third model develops multi-architecture neural networks to supply decision makers with more natural pattern recognition information, based on fuzzy theory.</p> / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/8314 |
| Date | 06 1900 |
| Creators | Wang, Shouhong |
| Contributors | Archer, Norman P., Management Science/Systems |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | thesis |
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