La escalada a la cima

I he first network was customised to identify two token chemical species in the two gas-plasma argon/hydrogen system. The species to be identified were Ar and H. The

single layei (no hidden layer) ANN employed was partially connected as displayed in Fig. 3.7 .

Using the same spectial data from Picton el. cil. (1996), the ANN was trained on 14

single spectral patterns, and then tested on five mixed spectral patterns. The ANN was

also tested on the 14 single spectral patterns; this tested the learning ability of the netwoik. The single patterns consisted of seven Ar and seven I I spectral data; and the mixed patterns consisted of five Ar/H spectral data. I he ANN was trained on single species patterns to determine whether once it had learnt the pattern of the single species it could identify it within a mixture of patterns. Hence, the training was carried out on seven argon only plasmas and seven hydrogen only plasmas.

Table 3.2 Two Species Classification from ANN trained on single species patterns

The results in Table 3.2 show that the partially connected single layer architecture was successful in recognising Ar and H species in a mixed spectral pattern of argon and hydrogen. Note that the threshold value for classification is 0.5, and so values above 0.5 classify that species. Also, the network output determined the presence of hydrogen (values highlighted in bold) in the spectra from argon plasmas. The presence of H in the aforethought argon only plasmas, was confirmed to have been a by-product ol water (II20) which was present in the chamber at the time of OES collection providing a useful determination of errant species.

These results corroborated Picton el. al.'s (1996) classification results. They also leflected the ability of a single layer feedforward ANN architecture to identify two individual spectral patterns from a mixed pattern once it had been appropriately

trained to convergence. Ibis ability can be attributed to the partially connected nature of the network whereby the relevant species' normalised spectral line inputs are only connected to the output unit that classifies that species.

Modifying an ANN model into a partially connected topology was adopted by Moore

et. al. (199j ) to analyse mixtures ol test gases (H2, CH4, 0 2) quantitatively. The partially connected network had six input units all connected to nine units in a single hidden layer, and three output units (lor H2, CH4, 0 2). Only three hidden units were connected to each output. I his set up produced the best results with a maximum prediction enoi of 10% lor each gas. The predictive capability of the ANN aichitecture can therefore be enhanced by assuming a partially connected topology.

In the case ol the preliminary work presented in this thesis, a simpler partially connected single layer (i.e. no hidden units) network topology was utilised. With the single layer network, the inputs trained on normalised intensities for a particular species are connected to the single output unit for detecting that species. For example, during training, only the weights associated with the three normalised intensity input values tor identifying H were connected to the H output unit detector.

To test the robustness ol the two species detector's partially connected architecture the number of individual patterns to be trained on was increased to four in the next experiment.

3.3.2 Four Species Network

A similar network topology was trained on spectra from three individual plasmas - hydrogen, argon, and methane. The partially connected single layer ANN was trained on the single spectral patterns of Ar, H, CH, and CH+ species. The four species ANN performance is compared with results from testing the two species ANN on new spectral patterns (still obtained from argon/hydrogen plasmas).

|files NI 9 3 NI 9 5 NI 9 7 N I 9 9 N19 1 1

Ar 0.92 0.90 0.92 0.93 0.93

H 0.94 0.94 0.93 0.94 0.94

CH 0.92 0.93 0.85 0.95 0.93

ICH 0.96 0.89 0.89 0.91 0.91

(5 mixed methane/hyd rogen/argon plasmas )

Table j.j> Four Species classification with Four Species Detector

Table j.4 One Species classification with Two Species Detector

The four species ANN was trained on 15 Ar, 10 H. 5 CM and 5 CH+ spectral patterns. Training converged quickly with RMS error — 0.1, having used learning rate of 0.8 and momentum of 0.2 during BP learning. The trained network was tested on five different mixed spectral patterns. The accurate classification of the four species Ar. H, CH, CH shown in I able 3.3 identifies the presence of these species in the mixed spectral patterns of CH4/Ar/H2.

Tables j>.4 and j .5 show results from an arbitrary testing of the two species network (desciibed in section j .j.1) on a different set of single and mixed (two-gas) spectral patterns. It detected H in all seventeen single plasmas; and also detected H and Ar in fifteen mixed plasmas. The highlighted values (in bold) show values below the 0.5 threshold for four of the fifteen Ar/H2 spectra.

3.3.3 Conclusions from Two and Four Species Classifiers

The performance of the partially connected single layer feedforward ANN has determined the following:

• Limitations of the ANN occurred when it had not been introduced to a particular type ot mixture (i.e. a wholly different mixed spectral pattern) during training.

• When the ANN had been trained on a combination of single species spectral patterns (and mixed patterns) it could indicate the presence of errant species that may not have been known to be present initially. An example was the detection of H from water present in the argon only plasma.

• This method can be successfully applied to specific plasma processes that contain a similar number of gases within the plasma mixture. The method can then not only identify which species are present, but also those that are not present, as well as whether some errant species (impurities) are present too.

• The vector length approach for normalising spectral lines in order to pull out the unique fingerprint of optical emission spectral lines from an entire spectral pattern was adopted. This helped to extract relevant features from the OES spectra without losing relevant information (for example, very small spectral lines that locate some species compared with other lines which are relatively large are not #

ignored in the overall spectral search for extracting relevant information).

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) L IB fi A R Y |

• The ANN is robust enough when it comes to the recognition of individual patterns in mixed patterns as long as the network has been trained on a sufficient number of single spectral pattern types.

• The ANNs determination ol the absence of species otherwise known to exist in the plasma is based on the spectral patterns that it has learnt. OES spectral patterns that do not contain emission lines at known wavelength locations as a lesult of possible re-absorption ol species within different types of plasma can contribute to this loss in characterisation of species. A feasible way to reduce this shortcoming was to not only train a network on single spectral patterns but also on a sufficient number of mixed spectral patterns for a more robust species classifier.

Employing a lull) connected MLP ANN to learn the different spectral patterns was the most appropriate next step to determine a more robust species classifier.