By Krzysztof Patan
An unappealing attribute of all real-world structures is the truth that they're susceptible to faults, malfunctions and, extra typically, unforeseen modes of - haviour. This explains why there's a non-stop desire for trustworthy and common tracking structures in line with compatible and e?ective fault analysis techniques. this can be very true for engineering systems,whose complexity is completely growing to be end result of the inevitable improvement of recent in addition to the knowledge and communique know-how revolution. certainly, the layout and operation of engineering structures require an elevated recognition with admire to availability, reliability, security and fault tolerance. therefore, it truly is ordinary that fault analysis performs a basic position in sleek keep watch over concept and perform. this can be re?ected in lots of papers on fault prognosis in lots of control-oriented c- ferencesand journals.Indeed, a largeamount of knowledgeon version basedfault prognosis has been accrued via scienti?c literature because the starting of the Seventies. for this reason, a large spectrum of fault analysis ideas were constructed. an incredible type of fault analysis suggestions is the version established one, the place an analytical version of the plant to be monitored is thought to be available.
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Additional info for Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes
When data about process states or operation condition are passed on to the fault analyser, they are checked against the rule base stored there and a decision about operation conditions of the system is made. Neural networks are an excellent tool to design such fault analysers . The well-known feedforward multi-layer networks are most frequently used. Summarizing, to develop knowledge based diagnostic systems, knowledge about the process structure, process unit fuctions and qualitative models of process units under various faulty conditions are required.
If they are derived from the previous layer, then it is local synapse feedback. On the other hand, if they are derived from the output y(k), it is local output feedback. Moreover, local activation feedback is a special case of the local synapse feedback architecture. e. bj = 0, j = 1, 2, . . , r. Model with local output feedback. Another dynamic neuron architecture was proposed by Gori  (see Fig. 9). In contrast to local synapse as well as local activation feedback, this neuron model takes feedback after the non-linear activation block.
Therefore, neurons and weighted connections should have a certain level of self-organization. Moreover, unsupervised learning is only useful and eﬀective when there is a redundancy of learning patterns. Inputs and neurons in the competitive layer are connected entirely. Furthermore, the concurrent layer is the network output which generates the response of the Kohonen network. 23) where u is the input vector, i is the index of the winner, wj is the weight vector of the j-th neuron. 24) where η(k) is the learning rate and C(k) is a neighbourhood.