Iterative learning

Real-time Iterative Learning Control Jian-Xin Xu, Sanjib K. Panda and Tong H. Lee... 

Experimental design is a large topic and we can only mention several of the important issues here. To keep this discussion focused on parameter estimation for reactor models, we must assume the. reader has had exposure to a course in basic statistics [4]. We assume the reader understands the source of experimental error or noise, and knows the difference between correlation and causation. The process of estimating parameters in reactor models is part of the classic, iterative scientific method hypothesize, collect experimental data, compare data and model predictions, modify hypothesis, and repeat. The goal of experimental design is to make this iterative learning process efficient. 

With the complexity and the multitude of open biological and physicochemical questions at early stages, it becomes imperative to work closely and to interact using a multidisciplinary approach. Information from iterative learning loops for a formulation is on one hand derived from physical stress (time, temperature and humidity) and on the other hand information is provided sequentially from biological tests as pharmacokinetic results. Of particular interest is the wide range of dosing encountered from... 

Fig. 40.2 Iterative learning loops to support the optimization of a formulation.

Lazarevic, P.M., D Type iterative learning control for fractional LTI system, in Proceedings of ICCC2003, Tatranska Lomnica, Slovak Republic, May, 26-29, 2003, p. 869. 

Marcos Vim cius B. do Couto is an undergraduate student (will graduate during 2012) in Control and Automation Engineering at Universidade Federal do Rio de Janeiro (UFRJ). He currently woiks with precision control and instrumentation of test equipment and iterative learning cyclic control of motion systems. 

In many instances companies lack the MID-specific systematic approach for developing products. MID experience is garnered over time in an iterative learning process. The expertise of individuals becomes a decisive factor. But the fact is that procedures adapted to MID already exist. 

The general principle behind most commonly used back-propagation learning methods is the delta rule, by which an objective function involving squares of the output errors from the network is minimized. The delta rule requires that the sigmoidal function used at each neuron be continuously differentiable. This methods identifies an error associated with each neuron for each iteration involving a cause-effect pattern. Therefore, the error for each neuron in the output layer can be represented as ... 

Conditions two and three can only be fulfilled in iteration using Schwab s substmctures of a curriculum Phil - Ped - Sub). The main question is how to start the iteration. To work out a new vision on the learning of micro-macro thinking, there needs to be an interrelation between chosen philosophies on chemistry (education Phil) that is consistent with a pedagogical theory Ped). An example is extensively described by Meijer, Bulte, Pilot (2005 see also Pilot et al. in this book) chemistry is considered as a human activity in relevant communities of practice Phil), while learning Ped) is to take place as participation in such (situated) communities of practice. To avoid the use of the traditional conceptual stracture... 

The ability of an ANN to learn is its greatest asset. When, as is usually the case, we cannot determine the connection weights by hand, the neural network can do the job itself. In an iterative process, the network is shown a sample pattern, such as the X, Y coordinates of a point, and uses the pattern to calculate its output it then compares its own output with the correct output for the sample pattern, and, unless its output is perfect, makes small adjustments to the connection weights to improve its performance. The training process is shown in Figure 2.13. 

Jones R, Carpenter E, Lamprecht Ret al (2009) Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. PNAS 106 1826-1831... 

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