N. Joshi

N. Joshi

Many of the competitive neural network consists of spatially arranged neurons. The weigh matrix that connects cells represents local excitation and long-range inhibition. They are known as soft-winner-take-all networks and shown to exhibit desirable information-processing. The local excitatory connections are many times predefined hand-wired based depending on spatial arrangement which is chosen using the previous knowledge of data. Here we present learning in recurrent network through Haeusslers equation and modified wiring scheme based on biologically based Firefly algorithm. Following results show learning in such network from input patterns without hand-wiring with fixed topology.

N. Joshi

A classification algorithm that combines the components of k-nearest neighbours and multilayer neural networks has been designed and tested. With this method the computational time required for training the dataset has been reduced substancially. Gabor filters were used for the feature extraction to ensure a better performance. This algorithm is tested with MNIST dataset and it will be integrated as a module in the object recognition software which is currently under development.