As in handing the practical problem it is

As environmental monitoringconstraints imposed federal agencies become stricter, demand for gas sensor with high sensitivities for sensing of variousgases are increasing now a days. To meet the demand of low volume gasdetection, gas sensors should be enhanced in sensitivity, selectivity, recoverytime and response time 1. At the same time they should be cost effective andreliable over long term2. In Metaloxide semiconductor sensor explore measurement  of electric conductivity for sensing the gasesof interest. SnO2 is most widely used material amongsemi-conductor oxide for making sensors due to its low development cost, longlife and good reproducibility 3, 4 , thick film SnO2 device are moststudies and most candidate due to their high level of sensitivity , simpledesign, low weight and cost effectiveness. SnO2 is an n –type , wide–band gap (3.

6 Ev) semiconductor 5. Its electrical conductivity id due to thenon-stoichiometric compositions as a result of oxygen deficiency 6. Thesensing properties of the thick film gas sensor are based on the adsorption ofthe gas molecules on it surface which produce changes in their conductivity7. When gas sensor exposed to atmosphericair, freshly prepared tin-oxide particles adsorbed oxygen atoms on the sensorupper layer surface 8. These oxygen atoms pick up the e-s  from the conduction band of tin oxide and areadsorbed on the particle surface as O- ions. Each tin oxide particleis covered with negativity charged O- ions on the surface. On the other hand , due to depletion e-s , thereexits a layer of positively charged donor atoms just below the particlesurface.

The  O-  adsorbents react with the gases andrelease the  e-s   to the conduction band at higher temperature, when reducing gases came in contact with sensor. Consequently, the depth of the space –charge layer decreases, which result’s in a decrease in the height of the potential barrierfor  the electronic conduction at  the grain boundaries. The concept of ANNanalysis have been discovered nearly 50 years ago, but in handing the practicalproblem it is used only from last 2 –decades9. ANN are collections of smallindividually interconnected processing units. Information is passed between these units along interconnections. An incoming connection has two value associated with it, an input value and aweight.

The output of the unit is a function of the summed value. Once an ANNis trained for a prescribed data it may be ready to be used then for theprediction or classification ANNs can automatically learn to recognize patternin the data real system or from physical models, or other sources. An ANN can handle many input and produceanswer that are suitable for designers in required proper format 10. Artificial Neural Network(ANN) model may be used as alternative method for technological analysis andmatlab based calculation. Artificial Neural Networks have two main components-the processing element called neurons and the connection between them, eachconnection have their own weights. The neurons are the information processorsand the connection functions are the information storage.

Each processingelement first calculates a weighted sum of the input signals and then appliesthe transfer functions . The term ‘ Feed Propagation’ comes due to the trainingmethod used during the training process-back propagation of error. A GradientDescent Backpropagation with adaptive learning rate algorithm is used to adjustthe weights in the hidden and output layer nodes. The result is a network thatproduces the mapping between the input values and output values with help ofthe neurons. In this model perception, Feed-Forward Propagation is one ofsuitable method of artificial neural network, designed for the testing andtraining of data.

Training methodologiesused in forward propagation are purelin, logsin and tansin network transferfunction for all the neurons, which reflects the relationship betweenconcentration as input and sensitivity for different concentration as output ofSnO2 based 1% Pd-doped thick film gas sensor. Sensitivity is testedby artificial neural network. In neural network architecture one layer acts asinput layer, ten neurons acts as the hidden layer and other layer output layer.

In this model input is concentration of methanol and output is the sensitivityof sensor. Though in present work single sensor is exposed to single gas orvapor at a time and ANN is utilized to confirm it with experiments so that thedata collected can be used to train the network when sensor is replaced bysensor array and single gas is replaced by group of gases or vapors to achievehigh selectivity. This model was trained to generate a mapping between theinput concentration of the methanol and output as the sensitivity of themethanol. Sensitivity is dimension less quantity which is obvious from itsexpression In the present work feedforward network used to cross verification of gas sensor sensitivity atdifferent temperature & sample concentration after training & testingfrom practical lab data for the sensitivity of the 1 % Pd-doped SnO2sensor. Feed Forward network uses the Gaussian activation function. Theimportance of such function is that it is non negative for all value of x.