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Evolutionary design of constructive multilayer feedforward neural network.
| Content Provider | CiteSeerX |
|---|---|
| Abstract | Abstract:- This paper proposes an evolutionary design methodology of multilayer feedforward neural networks based on constructive approach. We elaborate an adjustable processing element as primitive neuron model. The neural layer can be constructed by assembling several neurons. The multilayer neural network can be finally constructed through cascading several neural layers. The constructive approach facilitates substantially to extract design specifications from a multilayer neural network. Based on the constructive representation of multilayer feedforward neural networks, we use a genetic encoding method, after which the evolution process is elaborated for designing the optimal neural network. The results of our experiments reveal that our methodology is superior to the error back-propagation algorithm both for its executing efficiency and performance. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Constructive Multilayer Feedforward Neural Network Evolutionary Design Multilayer Feedforward Neural Network Constructive Approach Multilayer Neural Network Constructive Representation Executing Efficiency Several Neuron Neural Layer Adjustable Processing Element Evolutionary Design Methodology Several Neural Layer Design Specification Error Back-propagation Optimal Neural Network Primitive Neuron Model Evolution Process Genetic Encoding Method |
| Content Type | Text |
| Resource Type | Article |
