Abstract
This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN). This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 μm standard CMOS process with a 1.8 V supply. The power consumption was 0.553 mW, and the area was approximately 1.36 × 1.36 mm2. The chip measurements showed that this MLPNN successfully identified the fruit odors of bananas, lemons, and lychees with 91.7% accuracy.
Highlights
The artificial olfactory system, referred to as the electronic nose (E-nose) system, has been used in numerous applications
Because of the complex classification algorithms embedded in the pattern recognition system, a central
This study implemented a low power multilayer perceptron neural network (MLPNN) by analog VLSI circuit to serve as a classification unit in an E-nose
Summary
The artificial olfactory system, referred to as the electronic nose (E-nose) system, has been used in numerous applications. To further reduce the power consumption and device area, analog VLSI implementation of the learning algorithm for E-nose application has been proposed [7,8,9,10,11,12,13,14]. This study implemented a low power MLPNN by analog VLSI circuit to serve as a classification unit in an E-nose. Apart from the input layer, both the hidden and output layers contain several neurons with nonlinear activation functions, which constitute the signal processing unit. Implementing an analog MLPNN circuit reduces the need for power and the size of the pattern recognition unit required to build an E-nose. The hyper tangent function is one of the most commonly used activation functions This function can be implemented by analog VLSI with small chip area and power consumption. The rest of this paper is organized as follows: Section 2 describes the system architecture, Section 3 presents the measurement results, and Section 4 presents the conclusion
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