Despite the large volume of publications devoted to neural networks, fuzzy logic, and evolutionary programming, few address the applications of computational intelligence in design and manufacturing. Computational Intelligence in Manufacturing Handbook fills this void as it covers the most recent advances in this area and state-of-the-art applications.
This comprehensive handbook contains an excellent balance of tutorials and new results, that allows you to:
TABLE 6.6 The Best Alternative Routings for Each Part Part No. Routing No. Part No. Routing No. 1 3 16 1 2 1 17 1 3 2 18 2 4 1 19 1 5 1 20 2 6 3 21 1 7 3 22 1 10 4 25 1 11 1 26 2 12 2 27 1 13 1 28 1 14 1 29 1 15 3 30 1 the genetic algorithm discussed previously. The GA simulation is shown in Figure 6.8, and the corresponding optimum routing for each part is shown in Table 6.6. The MCS matrix for the selected process routings is shown in Table 6.7. 6.9.2 Machine Grouping The next
input signals to neuron j and yj(t) is the target output for neuron j. As there are no target outputs for hidden neurons, in Equation 1.16, the difference between the target and actual output of a hidden neuron j is replaced by the weighted sum of the δq terms already obtained for neurons q connected to the output of j. Thus, iteratively, beginning with the output layer, the δ term is computed for neurons in all layers and weight updates determined for all connections. The weight updating process
......................................3-11 Automatic Grouping of Agents into Holonic Clusters ..............................................................3-14 MAS Self-Organization as a Holonic System: Simulation Results ...........................................................3-26 Conclusions ......................................................................3-36 3.1 Introduction Global competition and rapidly changing customer requirements are forcing major changes in the production
occurrence degrees). The construction steps are similar and the holonic behavior is presented also in Figure 3.9. • Shannon fuzzy entropy of the source-plan: Amplitude of force towards knowledge: 39.5714 (max = 49) 7.7790 (max = ∞). (N = 7 agents) 0592/ch03b** Page 32 Friday, December 1, 2000 9:48 AM 3-32 Computational Intelligence in Manufacturing Handbook • The membership matrix of the corresponding proximity relation : 1.0000 0.3420 0.6988 ᏹ : 0.5666 0.5179 0.2372 0.6694 0.3420
constructed by taking the point-wise maximum over all of the fuzzy subsets assigned to the output variable by the inference rule (fuzzy logic OR). In SUM composition, the combined output fuzzy subset is constructed by taking the point-wise sum over all of the fuzzy subsets assigned to the output variable by the inference rule. 0592/frame/ch05 Page 19 Monday, November 6, 2000 2:02 PM 5-19 Application of Fuzzy Set Theory in Flexible Manufacturing System Design O I N Crisp-to-fuzzy P
