A sensory system for robots using evolutionary artificial neural networks.

The thesis presents the research involved with developing an Intelligent Vision System for an animat that can analyse a visual scene in uncontrolled environments. Inspiration was drawn both from Biological Visual Systems and Artificial Image Recognition Systems. Several Biological Systems including the Insect, Toad and Human Visual Systems were studied alongside popular Pattern Recognition Systems such as fully connected Feedforward Networks, Modular Neural Networks and the Neocognitron. The developed system, called the Distributed Neural Network (DNN) was based on the sensory-motor connections in the common toad, Bufo Bufo. The sparsely connected network architecture has features of modularity enhanced by the presence of lateral inhibitory connections. It was implemented using Evolutionary Artificial Neural Networks (EANN). A novel method called FUSION was used to train the DNN, which is an amalgamation of several concepts of learning in Artificial Neural Networks such as Unsupervised Learning, Supervised Learning, Reinforcement Learning, Competitive Learning, Self-organisation and Fuzzy Logic. The DNN has unique feature detecting capabilities. When the DNN was tested using images that comprised of combination of features used in the training set, the DNN was successful in recognising individual features. The combinations of features were never used in the training set. This is a unique feature of the DNN trained using Fusion that cannot be matched by any other popular ANN architecture or training method. The system proved to be robust in dealing with New and Noisy Images. The unique features of the DNN make the network suitable for applications in robotics such as obstacle avoidance and terrain recognition, where the environment is unpredictable. The network can also be used in the field of Medical Imaging, Biometrics (Face and Finger Print Recognition) and Quality Inspection in the Food Processing Industry and applications in other uncontrolled environments

Task decomposition using pattern distributor

In this paper, we propose a new task decomposition method for multilayered feedforward neural networks, namely Task Decomposition with Pattern Distributor in order to shorten the training time and improve the generalization accuracy of a network under training. This new method uses the combination of modules (small-size feedforward network) in parallel and series, to produce the overall solution for a complex problem. Based on a “divide-and-conquer” technique, the original problem is decomposed into several simpler sub-problems by a pattern distributor module in the network, where each sub-problem is composed of the whole input vector and a fraction of the output vector of the original problem. These sub-problems are then solved by the corresponding groups of modules, where each group of modules is connected in series with the pattern distributor module and the modules in each group are connected in parallel. The design details and implementation of this new method are introduced in this paper. Several benchmark classification problems are used to test this new method. The analysis and experimental results show that this new method could reduce training time and improve generalization accuracy

Development of modularity in the neural activity of children's brains

We study how modularity of the human brain changes as children develop into adults. Theory suggests that modularity can enhance the response function of a networked system subject to changing external stimuli. Thus, greater cognitive performance might be achieved for more modular neural activity, and modularity might likely increase as children develop. The value of modularity calculated from fMRI data is observed to increase during childhood development and peak in young adulthood. Head motion is deconvolved from the fMRI data, and it is shown that the dependence of modularity on age is independent of the magnitude of head motion. A model is presented to illustrate how modularity can provide greater cognitive performance at short times, i.e.\ task switching. A fitness function is extracted from the model. Quasispecies theory is used to predict how the average modularity evolves with age, illustrating the increase of modularity during development from children to adults that arises from selection for rapid cognitive function in young adults. Experiments exploring the effect of modularity on cognitive performance are suggested. Modularity may be a potential biomarker for injury, rehabilitation, or disease.Comment: 29 pages, 11 figure