PPT 206 Instrumentation, Measurement and Control SEM 2 (2012/2013)

1. PPT 206 Instrumentation, Measurement and Control SEM 2 (2012/2013) Data Processing for System Identification Dr. Hayder Kh. Q. Ali hayderali@unimap.edu.my

2. Electronic noses and tongues are arrays of sensors used to characterize complex samples, with the former being arrays of gas sensors while the latter are composed of liquid sensors. These devices are composed of a chemical sensing system and a pattern recognition (PR) system [usually an artificial neural network (ANN)]. The array sensing system allows different properties to be measured simultaneously. Each chemical, which reaches the sensor array, will produce a characteristic pattern and therefore a database of patterns will be built up for a series of chemicals.

3. DATA PROCESSING Figure 6.2 shows the structure of a typical electronic nose/tongue.

4. The PR system allows the devices to be capable of recognizing simple or complex odors or tastes. PR techniques are used for data processing, and the data generated by each sensor are processed by a PR algorithm before the results are analyzed. Advantages of this approach include the following: 1. A reduction in complexity of the sensor coating. 2. The ability to characterize complex mixtures without the need to identify and quantify individual components. 3. It can be exploited for structure-activity studies.

5. Several techniques are available, either separately or together, to perform PR after measurements are obtained. These include principal components analysis (PCA), cluster analysis (CA) and artificial neural network (ANN).

6. PRINCIPAL COMPONENTS ANALYSIS PCA is a method that is used to reduce the dimensionality of the parameter space, and it can also reveal the parameters that determine some inherent structure in the data, which may be interpreted in chemical terms. Thus, PCA algorithms are used to project the data sets into two dimensions (termed the principal components), which allow minimum loss of information.

7. CLUSTER ANALYSIS CA is similar to PCA and is based on the assumption of a close position of similar samples in multidimensional pattern space. Any similarity between two close samples is calculated as a function of the distance between them and displayed on a dendrogram.

8. ARTIFICIAL NEURAL NETWORK ANNs are computer programs based on a simplified model of the brain, and they reproduce its logical operation using a collection of neuron-like entities to carry out processing. These programs are multipurpose and, with suitable training, a single program can solve several problems.

9. Advantages of ANN include the following: it can handle noisy or missing data, no equations are involved, a network can deal with previously unseen data as soon as training has been completed, a large number of variables can be manipulated, and there is good accuracy.

10. There are three main stages involved in the development and use of ANNs [14]: 1. The learning stage: the number of neurons, layers, and type of architecture, transfer function, and algorithm are established, after which the network is allowed to achieve the desired outputs linked to an input. 2. The validation stage: this is achieved by the verification of the capability of the network by using different data from that utilized during the learning stage. 3. The production stage: the network has the ability to provide outputs corresponding to any input.