A neural network based window filter and its training for image processing tasks : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Technology at Massey University, Palmerston North, New Zealand
The design and implementation of a neural network based universal window filter (NNWF) is described. Experiments are reported which demonstrate that such a network filter can learn to perform filtering operations from input-target image pairs. Difficulties with training such a filter for more complex tasks are then described. Standard methods of improving the learning performance of neural networks are reviewed. Speculation on development of intelligent systems is presented with particular reference to the purpose and use of logical sequential thought and rules. The importance of an educational environment is outlined and a series of four heuristics for improving the training of neural networks is suggested. Initial experiments with these heuristics are described. The analogy to instructional design theory for humans is proposed and a formal basis for the design of an educational environment for the neural network based window filter is developed. Finally, a series of experiments are reported which test the validity of the use of the educational environment and demonstrate the effectiveness of the methods developed for implementing such an environment for the NNWF.