Neural Networks

AY2015/2016 Semester 1

Biological neural systems. Introduction to artificial neural networks. Supervised and unsupervised learning. Merits and limitations of neurocomputing. Perceptron as a linear classifier. Perceptron learning algorithms: ADALINE, MADALINE. Multi-layer perceptron. Generalized delta-rule. Backpropagation learning. Linear associated memory networks. Bidirectional memory. Recurrent networks. Hopfield networks. Stochastic neural networks. Boltzmann machine. Simulated annealing. Kohonen networks. Self-organizing feature maps. Adaptive resonance theory: ART1 architecture. Hybrid networks. Radial basis function networks. Counterpropagation networks. Fuzzy neural networks. Genetic algorithms. Hardware implementation of neural networks. Application of neural networks.