SCADA: Supervisory Control and Data Acquisition (3rd by Stuart A. Boyer

By Stuart A. Boyer

Supervisory keep an eye on and information acquisition (SCADA) know-how has developed during the last 30 years as a mode of tracking and controlling huge strategies. This newly revised reference e-book deals overviews of SCADA's part applied sciences, in addition to info essential to comprehend the massive photograph. SCADA procedures hide parts that could be measured within the millions of sq. miles, and feature dimensions which may be hundreds and hundreds, sometimes millions, of miles lengthy. Now a mature know-how, SCADA comprises, yet isn't constrained to, software program programs that may be included in a bigger procedure. After finishing its 14 self-study devices, readers might be conversant with SCADA nomenclature and structure, comprehend the fundamental know-how of the system's construction blocks, comprehend its barriers, know the way it could possibly profit specific operations, and feature a foundation for choosing applicable SCADA applied sciences for his or her operational necessities.

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Extra resources for SCADA: Supervisory Control and Data Acquisition (3rd Edition)

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1 use Xj as inputs to emphasise that the signals fed into static neurons are not feedback ones, so not equal to outputs. 4). A usual way of arranging the architecture of static networks is to group neurons into layers,1 or to design connections in such a way that each of them sends its signal directly only to a certain group of other neurons. , yp+ni. This layer, in turn, is connected through weights to the second hidden layer of n2 units and so on until the last layer of q neurons, called the output layer.

For example, in McCP and Boolean nodes the parameters are, respectively: the weights (and bias), and the memory contents. The functions are respectively, 'output a function of the weighted sum of inputs', and 'output a function of the contents of the addressed memory location'. It is useful to be able to classify ANNs in order to compare them and place them relative to each other in our understanding. Attempts have been made to do this such as that in [1]. One simple classification scheme which achieves this requires five pieces of information for the description of an ANN as defined above: • • • • Topology — how the functions are interconnected Architecture — type/use of network (four possibilities) Neuron model — what the functions are Training algorithm — how the parameters are set Operation schedule — timing of function interactions.

The functions X/ depend on the specific function / and are not representable in a parameterised form. The difficulties were overcome by Kurkova [29, 30] by the use of staircase-like functions of a sigmoidal type in a sigmoidal feedforward neural network. This type of function has the property that it can approximate any continuous function on any closed interval with an arbitrary accuracy. Using this fact, Kolmogorov's representation theorem modified by Kurkova is: Theorem 5 Letn € N withn > 2, o\ R -> E be a sigmoidal function, f e C°(En), and e be a positive real number.

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