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.
| AUs | 4.0 AUs |
| Categories | CoreBDE |
| Not Available To Programme | BCE(2011-onwards), BCG(2011-onwards), CE(2011-onwards), CSC(2011-onwards) |
| Not Available As BDE/UE To Programme | ACBS, ACC, ADM, AERO, ASEC, BEEC, BIE, BMS, BS, BUS, CBE, CBEC, CEE, CEEC, CHEM, CHIN, CS, CSEC, CVEC, ECON, EEE, EEEC, ELH, ENE, ENEC, ENG, IEEC, IEM, LMS, MAT, MATH, ME, ME(DES), ME(MEC), MEEC, MS, MTEC, PHSC, PHY, PSY, SOC |
| Mutually Exclusive With | CPE422, CZ4042 |
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