#### Title

Learning in the multiple class random neural network

#### Abbreviated Journal Title

IEEE Trans. Neural Netw.

#### Keywords

color image textures; learning; multiple class random neural network; (MCRNN); neural networks; random neural network (RNN); recurrent; networks; TEXTURE; COMPRESSION; Computer Science, Artificial Intelligence; Computer Science, Hardware &; Architecture; Computer Science, Theory & Methods; Engineering, ; Electrical & Electronic

#### Abstract

Learning is one of the most important useful features of artificial neural networks. In engineering applications, neural-network learning is used widely to capture relationships between sets of data when input-output examples are available and a mathematical representation of the relationship is not available in advance. Networks which have "learned" are then capable of "generalization." Spiked recurrent neural networks with "multiple classes" of signals have been recently introduced by Gelenbe and Fourneau, as an extension of the recurrent spiked random neural network introduced by Gelenbe. These new networks can represent interconnected neurons, which simultaneously process multiple streams of data such as the color information of images, or networks which simultaneously process streams of data from multiple sensors. This paper introduces a learning algorithm which applies both to recurrent and feedforward multiple signal class random neural networks (MCRNNs). It is based on gradient descent optimization of a cost function. The algorithm exploits the analytical properties of the MCRNN and requires the solution of a system of nC linear and nC nonlinear equations (where C is the number of signal classes and n is the number of neurons) each time the network learns a new input-output pair. Thus, the a algorithm is of O([nC](3)) complexity for the recurrent case, and O([nC](2)) for a feedforward MCRNN. Finally, we apply this learning algorithm to color texture modeling (learning), based on learning the weights of a recurrent network directly from the color texture image. The same trained recurrent network is then used to generate a synthetic texture that imitates the original. This approach is illustrated with various synthetic and natural textures.

#### Journal Title

Ieee Transactions on Neural Networks

#### Volume

13

#### Issue/Number

6

#### Publication Date

1-1-2002

#### Document Type

Article

#### Language

English

#### First Page

1257

#### Last Page

1267

#### WOS Identifier

#### ISSN

1045-9227

#### Recommended Citation

"Learning in the multiple class random neural network" (2002). *Faculty Bibliography 2000s*. 3216.

http://stars.library.ucf.edu/facultybib2000/3216