28.Industry Applications by John G. Webster (Editor)

By John G. Webster (Editor)

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Different cable types are still required to accommodate diverse requirements related to the needs of (electrical) safety, of EMC, and of transmission characteristics. Different applications have different requirements concerning the electrical characteristics of the transmission channel and its topology. , point-to-point or bus. In some commercial buildings separate networks for security and/or telephone may also be implemented for contractual reasons. Taking into account: • The transmission characteristics of the links required by different groups of applications BUILDING WIRING • • 5 The difference of cost and place of demand for low- and high-performance cables The need to provide coaxial cables for HE at least for another decade a generic infrastructure for all kinds of applications presently consists of three channel classes, as outlined in column 11 of Table 2, besides the mains distribution network.

Artificial neural networks, with such characteristics as learning, graceful degradation, and speed inherent to parallel distributed architectures, provide a flexible solution to the real-time control of robotic systems. Artificial neural networks are generally used to learn about the process and to coordinate transformation mapping of robots. In many cases, hybrid controllers that include some form of multilayered neural net- 164 AVALANCHE DIODES works are used. In this way, the dynamics of the environment that is contacted can be identified and optimized to determine the parameters of controllers such as PID controllers.

As a function of the resulting gradient, the weights are then updated. Wijn = η P ∂E ∂Wij (27) In this weight update rule, the index n refers to the nth iteration in the process, and ␩ describes the learning rate, being a discrete step size. This procedure is iterated until error E for all outputs is within the predetermined tolerance or until the predefined number of iterations is reached. The process is essentially an improved gradient descent optimization technique performed on the energy surface.

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