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This Kind Of Online Service Registration to the Customer.So they Getin full Satisficaation and Full EnjoyMent.achieving good quality and safety of food products detectionof shelf life is important. Goyal and Goyal developed centralnervous system based intelligent computer engineering systemto detect shelf life of soft mouth melting milk cakes [7]. Shelflife is the recommendation of time that products can be stored,during which the defined quality of a specified proportion ofthe goods remains acceptable under expected (or specified)conditions of distribution, storage and display[8].Prediction ofshelf life in laboratory is quiet time consuming and ana system that could predict shelf life of instant coffeeflavoured sterilized drink at low cost and in less time. Theywould be useful to coffee shops owners and food researchersconjugate gradient algorithm, Polak–Ribiére Update conjugategradient algorithm, Powell–Beale restarts conjugate gradient
algorithm, BFG quasi-Newton algorithm, Levenberg–Marquardt algorithm and Bayesian regularization. Levenberg–Marquardt algorithm gave better results, hence it was used astraining function; sum square error was performance functionused during training of feedforward backpropagation neuralnetwork. There is no generalized method to determine theoptimum values for number of hidden layers, neurons in each
hidden layer, etc., as they are function of expected intelligence.Neural network toolbox under Matlab 7.0 softwaconjugate gradient algorithm, Polak–Ribiére Update conjugategradient algorithm, Powell–Beale restarts conjugate gradientalgorithm, BFG quasi-Newton algorithm, Levenberg–Marquardt algorithm and Bayesian regularization. Levenberg–Marquardt algorithm gave better results, hence it was used astraining function; sum square error was performance used during training of feedforward backpropagation neural
network. There is no generalized method to determine thehidden layer, etc., as they are function of expected intelligence.

FB artificial intelligence model consists of input, hidden andoutput layers. Backpropagation learning algorithm was usedfor learning these networks. During training this network,calculations were carried out from input layer of networktoward output layer, and error values were then propagated toprior layers. Feedforward networks often have one or morehidden layers of sigmoid neurons followed by an output layerof linear neurons. Multiple layers of neurons with nonlineartransfer functions allow the network to learn nonlinear andlinear relationships between input and output vectors. Thelinear output layer lets the network produce values outside therange –1 to +1. On the other hand, outputs of a network suchas between 0 and 1 are produced, then the output layer should
use a sigmoid transfer function (logsig) [9].

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