2 Neural Network
2.6 Concluding Remarks
The factory of the future and the quality of its products will depend largely on the full integration of intelligent systems for designing, planning, monitoring, modeling, and controlling manufacturing sys- tems and processes. Neural networks have proved able to contribute to solving many problems in manufacturing. In addition to the ability to adapt and learn in dynamic manufacturing environments, neural networks make weak assumptions regarding underlying processes. They are applicable for a wide range of real-world problems. Neural networks, however, are not a substitute for classical methods. Instead, they are viable tools that can be supplementary and used in cooperation with traditional methods, especially in instances where the expense of in-depth mathematical analysis cannot be justified. Furthermore, neural networks by no means replace the computational capabilities provided by digital computers. Instead, neural networks would provide complementary capabilities to existing computers. A number of characteristics of some neural networks seem to limit their use in real-time, real-world manufacturing settings. Problems include lengthy training time, uncertainty of convergence, and the arbitrariness of choosing design parameters. Moreover, neural networks lack the capability for explana- tion of the learning outcome, and it is almost impossible to discern what has been learned from exam- ination of the weights matrices that result from learning. Further research and development are needed before neural networks can be completely and successfully applied for real-world manufacturing. Because neural networks hardware devices are not yet commercially available for manufacturing applications, the use of neural networks is still constrained to simulations on sequential computing machines. Training a large network using a sequential machine can be time-consuming. Fortunately, training usually takes place off line, and the efficiency of training can be improved using more efficient learning algorithms. Furthermore, software tools and insert boards are currently available that permit neural network pro- grams to run on desktop computers, making them applicable to a wide range of manufacturing appli- cations. The advances in VLSI neural chips will eventually accelerate computation and generate solutions with minimum time, space, and energy consumption.
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