Understanding Smart Sensors - Nomads.usp

160 Understanding Smart SensorsAn example of a potential application for a neural network is the controlof an automotive fuel injection system [15]. Production vehicles meet currentemissions regulations using calibration and lookup tables. However, emissionregulations for 2003 have further reductions of hydrocarbons (HC), oxides ofnitrogen (NO X ), and carbon monoxide (CO) emissions that may require thefunction-approximation, learning, and adaptive capabilities of neural networks.By using a neural network control, a stoichiometric air-fuel ratio (A/F) can bemaintained over the life of the vehicle even if the engine dynamics change.Experimental results with a neural network and a linear A/F sensor have demonstratedthe capability to control stoichiometry within ±1%, which was betterthan the production control unit.7.5 Combined Fuzzy Logic and Neural NetworksFuzzy logic and neural networks are being combined to utilize the best featuresof each technology. One approach begins with a set of fuzzy rules that havebeen well tuned by an expert using trial-and-error methods. A neural-like adaptivemechanism is then installed in the fuzzy system to handle exceptionalcircumstances after the system is in use. These systems compensate for loadvariables and for wear that occurs over time [16].In an alternate approach, the fuzzy system is coarsely defined by experts.The fuzzy rule base is then refined with a neural network. The neural networkadapts to minimize errors.Another approach combines fuzzy associative memories with neural networks.In general, an associative memory is a neural architecture used in patternrecognition applications. The network associates data patterns with specificclasses or categories it has learned. That combination produces a system inwhich the neural network front end learns rules from training data and thensupplies those rules to a fuzzy logic back end to execute the rules [16].The previous examples started with a fuzzy system and applied neural networklearning. Other researchers have started with neural networks and appliedfuzzy logic. In those systems, the network adapts in a more intelligent manner.The ultimate technology in this area could be evolutive learning, the applicationof genetic algorithms to combine fuzzy and neural systems automatically.Genetic algorithms are guided stochastic search techniques that utilize the basicprinciples of natural selection to optimize a given function [17]. Theseadvanced control concepts are being explored for process optimization in themanufacture of complex ICs.