By Marcin Mrugalski
The current e-book is dedicated to difficulties of version of man-made neural networks to powerful fault analysis schemes. It offers neural networks-based modelling and estimation suggestions used for designing strong fault analysis schemes for non-linear dynamic systems.
A a part of the publication makes a speciality of primary concerns similar to architectures of dynamic neural networks, tools for designing of neural networks and fault prognosis schemes in addition to the significance of robustness. The e-book is of an academic worth and will be perceived as an exceptional start line for the new-comers to this box. The e-book can be dedicated to complex schemes of description of neural version uncertainty. particularly, the tools of computation of neural networks uncertainty with powerful parameter estimation are offered. furthermore, a singular strategy for process identity with the state-space GMDH neural community is delivered.
All the techniques defined during this booklet are illustrated via either uncomplicated educational illustrative examples and sensible applications.
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Additional resources for Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis
Ny neurons; 2. Conduct a series of ny competitions between each n-th neuron in the layer and nj randomly selected neurons (the so-called opponent) from the same layer. The n-th neuron is the so-called winner neuron when: (l) (l) Q(ˆ yn,k ) ≤ Q(ˆ yj,k ), j = 1, . . , nj , (l) where yˆj,k denotes a signal generated by the opponent neuron; 3. Select the neurons for the (l + 1)-th layer with the number of winnings bigger than nw (the remaining neurons are removed). The property of soft selection follows from the speciﬁc series of competitions.
2. On the other hand, the introduction of the dynamic neurons increases the parameter space signiﬁcantly. This drawback together with the non-linear and multi-modal properties of the dynamic neuron implies that the parameters estimation still becomes complex. In order to overcome these drawbacks it is possible to use another kind of a dynamic neuron model . Dynamics in this neuron is realized by the introduction of a linear dynamic system - the IIR ﬁlter. In this way, each neuron in the network reproduces the output signal based on the past values of its inputs and outputs.
During the network synthesis new layers are added to the network. The process of network synthesis leads to the evolution of the resulting model structure to obtain the best quality approximation of real system output signals. The process is completed when the optimal degree of network complexity is achieved. Neuron selected (1) yˆ1,k (1) u1,k ... (1) u2,k (1) (n ) ... l yˆopt,k ... (n ) ... l yˆnl ,k (1) yˆn1 ,k Neuron selected Neurons selection ... (1) unu −1,k (1) (n ) yˆ1,kl yˆn,k (1) u3,k unu ,k Neuron selected Neuron removed Neurons selection Fig.
Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis by Marcin Mrugalski