C++ Neural Networks and Fuzzy Logic: Preface

output_layer class.

There is one other class besides the layer class and its descendants defined in this header file, and that is the

network class, which is used to set up communication channels between layers and to feed and remove data

from the network. The network class performs the interconnection of layers by setting the pointer of an input

array of a given layer to the output array of a previous layer.

C++ Neural Networks and Fuzzy Logic:Preface

C++ Classes and Class Hierarchy

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This is a fairly extensible scheme that can be used to create variations on the feedforward

backpropagation network with feedback connections, for instance.

Another connection that the network class is responsible for is setting the pointer of an output_error array to

the back_error array of the next layer (remember, errors flow in reverse, and the back_error array is the

output error of the layer reflected at its inputs).

The network class stores an array of pointers to layers and an array of layer sizes for all the layers defined.

These layer objects and arrays are dynamically allocated on the heap with the New and Delete functions in

C++. There is some minimal error checking for file I/O and memory allocation, which can be enhanced, if

desired.

As you can see, the feedforward backpropagation network can quickly become a memory and CPU hog, with

large networks and large training sets. The size and topology of the network, or architecture, will largely

dictate both these characteristics.