A General Pyramidal Modular Neural Network Architecture for High Dimensional Input Vectors
| dc.contributor.advisor | Raimond, Kumudha (PhD) | |
| dc.contributor.author | Tekeba, Menore | |
| dc.date.accessioned | 2018-06-28T11:31:07Z | |
| dc.date.accessioned | 2023-11-04T15:15:01Z | |
| dc.date.available | 2018-06-28T11:31:07Z | |
| dc.date.available | 2023-11-04T15:15:01Z | |
| dc.date.issued | 2011-09 | |
| dc.description.abstract | In this thesis new modular neural network (MNN) architecture is proposed. The basic building blocks of the architecture are small multilayer feed forward networks trained using the Back propagation algorithm (BPA). The newly proposed MNN Architecture is called Pyramidal MNN (PMNN). It is called Pyramidal for the number of the modules that constitutes the layers of network relatively decreases from the input layer to the output layer. An Optimization technique called PSO has been used to optimize the topology of the proposed PMNN architecture for typical high dimensional input vector datasets. The optimization technique is used to suit the PMNN architecture for specific problems of high dimensional input vectors depending on the nature of the data input and the nature of the problem. This is done by evolving topology of the modules that constitutes the network and changing the architecture of the overall network to suit the new data set. The suggested training algorithm works in multiple stages depending on the number of hidden layers of the network. The training of modules in the same layer of the PMNN is easy to implement in parallel. Since the network is not fully connected, the number of weight of connections is less and hence the training is very quick for large input dimensional vectors. An object-oriented implementation of the proposed PMNN architecture is written to simulate the behavior. The evaluation and optimization of the PMNN architecture for different real world applications is carried out to show the effectiveness of the proposed architecture for high dimensional input vector applications. The evaluation is based on three pattern recognition problems: palm-print recognition, iris recognition and face recognition. In all the three evaluations, it has achieved more than 95% accuracy of the test results. Furthermore, the proposed PMNN architecture performs better than other similar type research works. It is shown that as PMNN is a huge family of several specific architectures, this proposed topology of the neural net can serve wide range of complex domain problems that need to be solved using Artificial Intelligence (AI). Keywords: Modular Neural Networks, Pyramidal Modular Neural Networks, Particle Swarm Optimization, High Dimensional Input Vectors, General Pyramidal Modular Neural Networks, | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/4630 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Modular Neural Networks | en_US |
| dc.subject | Pyramidal Modular Neural Networks | en_US |
| dc.subject | Particle Swarm Optimization | en_US |
| dc.subject | High Dimensional Input Vectors | en_US |
| dc.subject | General Pyramidal Modular Neural Networks | en_US |
| dc.subject | Modularity in Neural Networks | en_US |
| dc.title | A General Pyramidal Modular Neural Network Architecture for High Dimensional Input Vectors | en_US |
| dc.type | Thesis | en_US |