Artificial neural networks (ANNs) are generally considered as the most promising pattern recognition method to process the signals from a chemical sensor array of electronic noses, which makes the system more bionics. This paper presents a chaotic neural network entitled KIII, which modeled olfactory systems, applied to an electronic nose to discriminate six typical volatile organic compounds (VOCs) in Chinese rice wines.
Thirty-two-dimensional feature vectors of a sensor array consisting of eight sensors, in which four features were extracted from the transient response of each TGS sensor, were input into the KIII network to investigate its generalization capability for concentration influence elimination and sensor drift counteraction.
In comparison with the conventional back propagation trained neural network (BP-NN), experimental results show that the KIII network has a good performance in classification of these VOCs of different concentrations and even for the data obtained 1 month later than the training set. Its robust generalization capability is suitable for electronic nose applications to reduce the influence of concentrat ion and sensor drift.
The experimental setup consists of an array of eight MOS sensors in a sealed test chamber (3000 mL), a set of acquisition circuits including a 12-bit A/D converter and an IBM PC compatible computer (as shown in Fig.1). The communication between the signal acquisition circuits and the computer is via a RS232 cable. Eight sensors (TGS880 (2 ×), TGS813 (2 ×), TGS822 (2 ×), TGS800, TGS823) are all commercially available, purchased from Figaro Engineering Inc.
PATTERN RECOGNITION METHOD
The KIII network modeling biological olfactory systems is a massively parallel architecture with multiple layers coupled with both feed forward and feedback loops through distributed delay lines. Fig. 3 shows the topological diagram of the KIII network. Odorant sensory signals from receptors (R) propagate to periglomerular cells (P) and olfactory bulb (OB) layers via the primary olfactory nerve (PON) in parallel.
RESULTS AND DISCUSSION
Trained with one of the samples of each of six VOCs, the Euclidean distances of all samples, including the training set, to different cluster centroids of those six classes are shown in Fig. 6. According to the classification criteria described in Section 3.2, the classification results clearly show that the correction rate of most samples are close to 100%, except two samples were misclassified.
In this paper, a biologically inspired neural network, based on anatomical and electroencephalographic studies of biological olfactory systems, is applied to pattern recognition in electronic noses. Classifying six VOCs commonly presented in the head space of Chinese rice wine, its performance to eliminate the concentration influence and counteract sensor drift is examined and compared with the simple nonparametric algorithm and the well-known BP-NN.
The KIII neural network has a good performance in classification of six VOCs of different concentrations, even for the patterns obtained 1 month later than what was used for training. Its flexibility and robust fault tolerance are quite suitable for electronic nose applications, subjecting to the problems associated with the susceptibility to concentration influence and sensor drift.
Compared with BP-NN, the application of the KIII neural network is time-consuming and requires a lot of memory to solve lots of ODEs constructing the KIII; e.g., a 32-channel KIII network consists of over 200 ODEs. Although one classification performance required about 1 min in our experiments, it is fast enough to satisfy application requirement. Efficient numerical computation methods and DSP and VLSI hardware specially designed for parallel implementation are under research for other real-time applications.
Source: University of California
Authors: Jun Fu | Guang Li | Yuqi Qin | Walter J. Freeman