Emulation

Another feature contributed to foods by fats and oils is mouthfeel. Mouthfeel is a difficult attribute to emulate since it appears to be a combination of several factors including viscosity, body, lubricity, and mouth coating. There are effects on the cheeks, tongue, and back of the throat. Other mouthfeel properties include resistance to chewing or change in viscosity during mastication, and other factors yet to be identified. 

Attempts to emulate the few large-scale electroorganic processes, eg, production of tetraalkyUead and adiponittile, have often led to fmstration. 

Since biological systems can reasonably cope with some of these problems, the intuition behind neural nets is that computing systems based on the architecture of the brain can better emulate human cognitive behavior than systems based on symbol manipulation. Unfortunately, the processing characteristics of the brain are as yet incompletely understood. Consequendy, computational systems based on brain architecture are highly simplified models of thek biological analogues. To make this distinction clear, neural nets are often referred to as artificial neural networks. 

Although mechanical sampling methods are to be the focus of attention, manual sampling methods are also employed for practical sample collecdion in commerce. Techniques of mechanical sampling should be emulated as closely as possible for best results with sampling by manual procedures. 

The manufaeturer of this system has developed a eontrol strategy by whieh the bypass valves are eontrolled in the event of an expander trip. This enables the valves to preeisely assume the position in whieh, in eonjunetion with the downstream orifiee ehambers, they preeisely emulate the flow loss eaused by the expander prior to the trip event. A properly adjusted and ealibrated eontrol output jump funetion is applied so that the pressure fluetuations that oeeur at a given duty point are kept to a minimum. 

A FLC that emulates a PD eontroller will be required to minimize the error e(t) and the rate of ehange of error de/dt, or ce. 

Artificial Neural Networks (ANNs) attempt to emulate their biological counterparts. McCulloch and Pitts (1943) proposed a simple model of a neuron, and Hebb (1949) described a technique which became known as Hebbian learning. Rosenblatt (1961), devised a single layer of neurons, called a Perceptron, that was used for optical pattern recognition. 

An interesting and important feature of a neural network trained using baek-propa-gation is that no knowledge of the proeess it is being trained to emulate is required. Also, sinee they learn from experienee rather than programming, their use may be eonsidered to be a blaek box approaeh. 

Controller emulation A simple applieation in eontrol is the use of neural networks to emulate the operation of existing eontrollers. It may be that a nonlinear plant requires several tuned PID eontrollers to operate over the full range of eontrol aetions. Or again, an LQ optimal eontroller has diffieulty in running in real-time. Figure 10.28 shows how the eontrol signal from an existing eontroller may be used to train, and to finally be replaeed by, a neural network eontroller. 

In his search for high efficiency, the designer of a gas turbine power plant will attempt to emulate these features of the Carnot cycle. 

It was pointed out in Chapter I that the desire for higher maximum temperature (T nx) in thermodynamic cycles, coupled with low heat rejection temperature (Tmin), is essentially based on attempting to emulate the Carnot cycle, in which the efficiency increases with... 

Nacheifer, m. emulation, nacheilen, v.i. lag, retard follow, trail. Nacheilwinkel, m. angle of lag. nacheinander, adv. one after another, successively. 

Wette,/. bet, wager rivalry, competition. Wetteifer, m. emulation, competition, wetteifem, v.i. vie, contend, compete. 

Flambax emuls. (AGIPS)-comb. wfm Fructine Vichy (Lirca)-comb. wfm Lilo 40 cioccolatini (Giuliani)-comb. wfm Neopurges (IFCI)-comb, wfm... 

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