DIGITAL GAS IDENTIFICATION SYSTEM USING ARTIFICIAL NEURAL NETWORKS
|Mrs.N.Dhanalakshmi1, Mr.K.Vijaya kanth2
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The human nervous system is responsible for critical thinking, reasoning and problem solving. Artificial Neural Networks (ANN) tries to resemble biological neuron. Here, ANN is used for discriminating gas species in an environment by means ofan array of four gas sensors using tin oxide (SnO2)based thin films with different sensitive elements such as copper and platinum.The output signal from the sensors are processed through the signal conditioning unit and fed to ANN which is implemented using Field Programmable Gate Array. The proposed ANN architecture includes multiplier and accumulate unit (MAC),Neuron activation function and classifiers. The MAC unit is implemented using radix-4 booth multiplier for improving multiplication speed and carry save adder for less power consumption. To activate the ANN, hyperbolic tangent (tanh) sigmoid activation function is used. ANN is trained using back propagation algorithm. Most of the gas discrimination system uses ANN with MLP as a classifier. MLP uses nonlinear parameters and does not guarantee optimum results While RBF uses linear parameters, guarantees optimum solution and has a faster learning rate. Here combination of both MLP and RBF is proposed. This proposed architecture is used to recognize four different gases such as hydrogen (H2), carbon monoxide (CO), Methane (CH4) and CO- CH4 mixture. The system can be used in domestic, industrial and military applications.