Identification neural network training

In a attempt to compensate for poor long-term reproducibility in a longterm identification study, Chun et al.128 applied ANNs to PyMS spectra collected from strains of Streptomyces six times over a 20-month period. Direct comparison of the six data sets, by the conventional approach of HCA, was unsuccessful for strain identification, but a neural network trained on spectra from each of the first three data sets was able to identify isolates in those three datasets and in the three subsequent datasets. 

The rest of the paper is organized as follows. The Section 2 describes attack classification and training data set. In the Section 3 the intrusion detection system is described, based on neural network approach. Section 4 presents the nonlinear PCA neural network and multilayer perceptron for identification and classification of computer network attack. In Section 5 the results of experiments are presented. Conclusion is given in Section 6. 

The classical adaptive control scheme is shown in Figure 2.58. Its goal is to use online identification through artificial intelligence (Al), neural networks, and fuzzy logic to adapt the model to the actual process. Al and model predictive control (MPC) can tolerate inaccuracy and uncertainty in the model, and online training can continuously improve the model. 

The first use of neural networks for the interpretation of PyMS spectra was reported by Goodacre (see Further Reading) for the identification of adulterated olive oils. The neural network was trained with standard pyrolysis mass spectra from 12 virgin and 12 adulterated olive oils, and then tested against a bank of further samples. The method successfully discriminated between virgin oils and those which had been adulterated with other seed oils, while PCA... 

A schematic diagram of the neural network-based adaptive control technique is shown in Fig. 4.9. A neural network identification model is trained using a static backpropagation algorithm to generate p(fc + 1), given past values of y and u. The identification error is then used to update the weights of the neural identification model. The control error is used to update the... 

Various neural network-based adaptive control techniques were discussed in this study. A major problem in implementing neural network-based MRACs is the translation of the output error between the plant and the reference model so as to train the neural controller. A technique called iterative inversion, which inverts the neural identification model of the plant for calculating neural controller gains, has been used. Due to the real-time computer hardware limitations, the performance of neural network-based adaptive control systems is verified using simulation studies only. These results show that neural-network based MRACs can be designed and implemented on smart structures. 

Process data, which is used for the identification of a black box model, such as training of a neural network, has to be a good representation of the process considered. Therefore the data set has to be examined carefully and bad data will have to be removed from it. The data set should be further examined to determine the degree of correlation within and between the outputs and inputs of the process. This is needed to determine the structure of the model to be developed. In this chapter some aspects of the analysis of process measurements will be discussed. 

Artificial neural networks (ANNs) are computer-based systems that can learn from previously classified known examples and can perform generalized recognition - that is, identification - of previously unseen patterns. Multilayer perceptions (MLPs) are supervised neural networks and, as such, can be trained to model the mapping of input data (e.g. in this study, morphological character states of individual specimens) to known, previously defined classes. 

---