Time-domain optimization of input signals for distributed-parameter systems identification
Journal of Optimization Theory and Applications
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks
Computers and Industrial Engineering
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
An expert system based on wavelet decomposition and neural network for modeling Chua's circuit
Expert Systems with Applications: An International Journal
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
In this paper a neural detector of internal parameter changes in a stationary, non-linear SISO dynamic system is considered. A dynamic system is usually described by an input-output relation or by a set of state equations. Each change of parameter values creates a new non-nominal model of a dynamic system (sometimes with different values of parameters, sometimes with different structure and different values of parameters). Thus the detection of parameter changes can be formulated as a multi-model classification. The LVQ (Learning Vector Quantisation) neural network has been proposed as a classifier. Selected aggregated properties of discrete wavelet decomposition coefficients of the system output have been chosen as the inputs of the LVQ classifier. The output of the classifier points out the current model. The proposed approach to classification can be adopted as a fault detection method where faults are represented by changes of values of internal parameters of a system. The algorithm has been evaluated on the example of a non-linear fluid system with a non-ideal pipe which internal state is characterised by one value of a parameter, chosen from the known set.