Schmidt A. A Modular Neural Network Architecture with Additional Generalization Abilities for High Dimensional Input Vectors

Диплом (Master), Manchester Metropolitan University, 1996. — 123 p.In this project a new modular neural network is proposed. The basic building blocks of the architecture are small multilayer feedforward networks, trained using the Backpropagation algorithm.
The structure of the modular system is similar to architectures known from logical neural networks. The new network is not fully connected and therefore the number of weight connections is much less than in a monolithic multilayer Perceptron.
The suggested training algorithm works in two stages and is easy to implement in parallel. Due to the used modular structure the training is very quick for large input vectors.
The modular architecture is designed to combine two different approaches of generalization known from connectionist and logical neural networks; this enhances the generalization ability, which is especially significant for a high dimensional input space.
An object-oriented implementation of the proposed model was written to simulate the behaviour.
The evaluation using different real world data sets showed that the new architecture is very useful for high dimensional input vectors. For certain domains the learning speed as well as the generalization performance in the modular system is significantly better than in a monolithic multilayer feedforward network.Multilayer Networks and Backpropagation
Logical Neural Networks
Modularity
A New Modular Neural Network
The Implementation
Experimental EvaluationA: Conference Paper
B: Example Files
C: Binary Pictures