Control learning algorithm

It is important to emphasize that, in the above examples, knowledge of the PES was not required for the optimization process. The adaptive-control learning algorithm explores the available phase space and optimizes the evolution of the wave packet on the excited state PES without any prior knowledge of the surface. Thus, the intrinsic information about the excited-state dynamics of these polyatomic systems remains concealed in the detailed shape and phase of the optimized pulse. Inevitably, however, scientific curiosity, together with a desire to imder-stand how chemical reactions can be controlled, has led to pioneering studies that aim to identify the underlying rules and rationale that lead to a particular pulse shape or phase relationship that produces the optimum yield. 

Fig. 6. A closed loop apparatus for optimally identifying quantum Hamiltonian information. The closed loop operations aim to reveal one or more control experiments that identify the best quality Hamiltonian information. Hamiltonian quality is used as the feedback signal for the learning algorithm guiding the laboratory experiments.

Judson and Rabitz [60] have provided a numerical demonstration of an existence theorem for feedback control in the guiding of the evolution of the state of a system. The example they consider is the transfer of 100% of the population from the vibrationless ground rotational state of KC1 to the vibrationless state with j = 3, m = 0, by a suitable field. The novel idea they exploit is to use the population transfer generated by a trial field as input to an adaptive learning algorithm for comparison with the desired popula-... 

Despite these notable successes, much remains to be done before coherent control can become a practical tool. Virtually all the successes to date have involved very simple molecules. Although learning algorithms may prove to be useful for controlling complex molecules, they have so far shed little light on the dynamics involved. Two very important problems where experiments lag far behind theory are the selective control of molecules with different chirality and the control of bimolecular reactions. A major... 

Fig. 8.2. A closed loop apparatus for manipulating quantum dynamics phenomena [26]. A learning algorithm guides the pulse shaper to optimize tailored laser pulses to act as photonic reagents. The tailored laser pulses induce quantum dynamic excursions in a sample. Under high-duty-cycle closed-loop operation, the process can home in on a particular pulse shape that steers the system as close as possible to the desired target. On each excursion of the loop, a new quantum system is prepared in the same initial state for controlled manipulation.

Optimal chirped-pulse schemes for achieving population inversion ( molecular 7r pulses ) and to explain the chirp-dependence of multiphoton absorption yields have been described by Cao (Cao and Wilson, 1997 Cao, et al., 1998 Cao, et al., 2000). The learning algorithm approach has been reviewed by Levis, et al., (2001) and Rabitz, et al., (2000). The use of masks, arrays, and computer controlled liquid crystal devices for phase and amplitude control has been described by Kawashima, et al., (1995) Weiner, (1995) Krause, et al., (1997) and Tull, et al., (1997). Schemes for storing information in the rotation-vibration levels of diatomic molecules have been implemented by Ballard, et al., (2002) and Stauffer, et al., (2002). 

FIGURE 15.6 Model for the hybrid modeling of controllers using feedback error learning algorithm. The inclined arrow represents the learning. 

Fig. 6.50 Optimization control loop of laser pulse shaping by a learning algorithm with feedback [727]...

All of this can be controlled using computer software, which should also include an adaptive learning algorithm, capable of computing the next step towards producing the optimum product yield and then tailoring the laser pulse in an appropriate way (this is termed adaptive closed-loop control). After each laser pulse,... 

Figure 19.1 Diagram showing the arrangement for closed-loop learning control. Following a femtosecond laser pulse, the products of the photochemical process are detected and compared with the user-defined objectives stored on the computer. A learning algorithm then calculates the modified electric fields required to shape the laser pulse and further optimize the yield of the desired product. Cycling through the loop many times gives the optimum pulse shape and best product yield. Adapted from Brixner et o/, Chem. Phys. Chem., 2003, 4 418, with permission of John Wiley Sons Ltd...

Following early pioneering work on the theory of coherent control, Rabitz and co-workers demonstrated experimentally that photoionization branching ratios can be controlled in a series of ketones (Levis et ai, 2001). For example, in the photoionization of acetone ((CH3)2CO), the CH3C0 ion, which was almost below the detection limits for an untailored pulse, became a substantial product when the laser pulse was tailored by the learning algorithm. This is particularly... 

This results in a light pulse with a time profile that depends on the phase differences between its spectral components, which in turn can be controlled by the LCD, driven by a special computer program (Fig. 11.34) [11.81,11.82]. A self-learning algorithm can be incorporated into the closed loop, which compares the output pulse form with the wanted one and tries to vary the voltage at the different pixels in such a way that the wanted pulse form is approximated [11.83]. More details can be found in [11.84]... 

Sheikholeslami, N., Shahmirzadi, D., Semsar, E., Lucas, C., Yazdanpanah, M. J. (2006). Applying brain emotional learning algorithm for multivariable control of HVAC systems. Journal of Intelligent Fuzzy Systems, 17, 35-46. 

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