Understanding Smart Sensors - Nomads.usp

158 Understanding Smart SensorsTemperaturesensorGassensorSmokesensorHumiditysensorPassiveIR scanRadarscanTasktableData acquisition taskBlackboard readings and buffersTasktableCertainty engine taskBlackboard threat scores and alarmsTarget acquisition and tracingFigure 7.6 A variety of sensors providing input to the fuzzy logic certainty engine of amobile robot. (After: [13].)manner roughly analogous to neurons in the human brain [11]. Neural networksare useful in systems that are difficult to define. They have the additionaladvantage of being able to operate in a high-noise environment. Complex ornumerous input patterns are among the problems that neural networks areaddressing [14].Figure 7.7(a) shows the structure of a neuron [14]. The neuron learnsfrom a set of training data. Each input to the neuron is multiplied by the synapseweight. The neuron sums the results of all the weighted inputs andprocesses the results with a typically nonlinear transfer function to determinethe output. A local memory stores previous computations and modifies theweights as it learns.A neural network consists of a number of neurons connected in somemanner, as indicated in Figure 7.7(b). In that model, a hidden layer exists thatprevents direct interaction between the input and the output. However, thehidden layer is not required, and more than one may exist. Also, the numberof neurons does not have to be the same in each layer. To train or conditionthe neuron for proper response, several sets of inputs with known results areapplied to the neural network. The output is compared for each set of inputs tothe known results, and the weights are adjusted to compensate for errors.