Page 82 - Neural Network Modeling and Identification of Dynamical Systems

70 2. DYNAMIC NEURAL NETWORKS: STRUCTURES AND TRAINING METHODS

The third of the approaches (networks with
pretuning) is the most powerful and, accord-
ingly, the most complicated one. Following this
approach, it is necessary either to organize the
process of dynamic (i.e., directly during the
operation of the ANN model) replacement of
the components of the model with their al- FIGURE 2.26 Intermediate elements in a network (com-
ternative versions prepared in advance, corre- positional) model.
sponding to the changed task, or to organize
the ANN model in the form of an integrated
system in which there are special structural el- example, by adjusting the values of the param-
ements, called interneurons and intersubnets, eters of the work items, which in turn changes
whose function is to act on the operational el- the character of the transformation realized by
ements of the network in such a way that their this element.
current characteristics meet the specifics of the Thus, intermediate elements are introduced
particular task being solved at the given mo- into the NM as a tool of contextual impact on the
ment. parameters and characteristics of the NM work-
ing elements. Using intermediate elements is the
2.3.2.2 An Intersubnet as an Adaptation most effective way to make a network (compos-
Tool for ANN Models ite) model adaptive. The functional role of the
We can formulate the concept of NM, which intermediate elements is illustrated in Fig. 2.26,
generalizes the notion of the ANN model. Some in which the these elements are combined into
NM is a set of interrelated elements (NM ele- an intermediate subnet (intersubnet). It is seen
ments) organized as network associations built that the intermediate subnet receives the same
according to certain rules from a very small inputs as the working subnet of the NM in ques-
number of primitives. One possible example of tion, which implements the basic algorithm for
an NM element can be a single artificial neuron. processing input data. In addition, an intersub-
If this approach is followed by the principle net can also receive some additional informa-
of minimalism, then the most promising way, as tion, referred to herein as the NM context. Ac-
noted earlier, is the formation of a very limited cording to the received initial data (inputs of the
set of basic NM elements. Then a variety of spe- ANN model + context), the subnet introduces
cific types of NM elements required to produce adjustments to the working subnet in such a
ANN models are formed as particular cases of way that this working subnetwork corresponds
basic elements. to the changed task. The training of the subnet-
Processing elements of the NM can have two work is carried out in advance, at the stage of
versions: working elements and intermediate el- formation of the ANN model, so that the change
ements. The most important difference between of the task to be solved does not require addi-
them is that the work elements convert the input tional training (and, especially, retraining) of the
data to the desired output of the ANN model, working subnet; only its reconfiguration is per-
that is, in the desired result. In other words, the formed, which requires a short time.
set of interacting work items implements the
2.3.2.3 Presetting of ANN Models and Its
algorithm for solving the required application
task. In contrast, intermediate elements of the Possible Variants
NM do not participate directly in the above al- We will distinguish two options for preset-
gorithm; their role is to act on the work items, for ting: a strong presetting and a weak presetting.