Adaptive learning

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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-... 

L. Ljung, J. Sjoberg, and H. Hjahnarsson. On neural networks model structures in system identification. In S. Bittanti and G. Picci, editors, Identification, Adaptation, learning, pages 366-393. Springer, Berlin, 1996. 

The four experiments done previously with Rnp (= 0.5, 1, 3, 4) were used to train the neural network and the experiment with / exp = 2 was used to validate the system. Dynamic models of process-model mismatches for three state variables (i.e. X) of the system are considered here. They are the instant distillate composition (xD), accumulated distillate composition (xa) and the amount of distillate (Ha). The inputs and outputs of the network are as in Figure 12.2. A multilayered feed forward network, which is trained with the back propagation method using a momentum term as well as an adaptive learning rate to speed up the rate of convergence, is used in this work. The error between the actual mismatch (obtained from simulation and experiments) and that predicted by the network is used as the error signal to train the network as described earlier. 

Seme and Muller (1987) describe attempts to hnd statistical empirical relations between experimental variables and the measured sorption ratios (R(js). Mucciardi and Orr (1977) and Mucciardi (1978) used linear (polynomial regression of first-order independent variables) and nonlinear (multinomial quadratic functions of paired independent variables, termed the Adaptive Learning Network) techniques to examine effects of several variables on sorption coefficients. The dependent variables considered included cation-exchange capacity (CEC) and surface area (S A) of the solid substrate, solution variables (Na, Ca, Cl, HCO3), time, pH, and Eh. Techniques such as these allow modelers to constmct a narrow probability density function for K s. 

Potentially, these findings could be due to the simple ASAD strategy implemented here. For example, ASAD agents are not designed to consider the rate of change of prices in the market. Perhaps a more suitable approach would be to implement an adaptive learning rule, such as the Widrow-Hoff delta rule [28], which is the basis of the adaptation mechanism in ZIP [8] and AA [26]. We reserve this extension for future work. 

Shakya, S., McCall, J., Brownlee, A., Owusu, G. Deum - distribution estimation using markov networks. In Shakya, S., Santana, R. (eds.) Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, vol. 14, pp. 55-71. Springer, Berlin (2012)... 

Cheng-Jian L, Chin-Teng L (1997) An art-based fuzzy adaptive learning control network IEEE Trans Fuzzy Syst 5(4) 477 96... 

Instructors have the ability to assign adaptive follow-up assignments. Content delivered to students as part of adaptive learning will be automatically personalized for each individual based on strengths and weaknesses identified by his or her performance on Mastering... 

Vol. 85 Adaptive Learning Environments. Foundations and Frontiers. Edited by M. Jones and P. FI. Winne. VIII, 408 pages. 1992. (AET)... 

Hof nann, J. (2003). Anticipatoiy behavioral control In M. Butz, O. Sigaud, and P. Gerard (eds). Anticipatory Behavior in Adaptive Learning Systems. (pp. 44-65). Heidelbeig Springer. 

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,... 

Three predictive models, namely BP-ANN, GA-ANN and AGA-ANN, were developed in MATLAB. Network parameters, i.e. adaptive learning rate for BP-ANN, crossover and mutation probability for GA-ANN, adaptive crossover site and mutation probability for proposed AGA-ANN, were chosen optimally. For each input pattern, the predicted value of the output variable (the ratio of damage area to hole area) has been compared with the respective experimental value for different networks in Figure 6.12a. The network was tested with the 20 test data points that had not been used for the purpose of training. From the test results, it was observed that the predicted values are very close and follow almost the same trend as the experimental values for AGA-ANN... 

Luo, Z. (1991). On the convergence of the LMS algorithm with adaptive learning rate for linear feedforward neural networks, neural computation. 

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